From 5e23e16a9a7512b73f8af8600c2e8c76dc783e6b Mon Sep 17 00:00:00 2001 From: Dragos Nedelcu Date: Thu, 11 Dec 2025 21:43:52 +0200 Subject: [PATCH 1/2] Remove documentation files and related workflows from the repository, including Sphinx configuration and build artifacts. --- .github/workflows/build_docs.yml | 42 - .github/workflows/static_docs.yml | 42 - docs/Makefile | 20 - docs/_build/doctrees/environment.pickle | Bin 546936 -> 0 bytes docs/_build/doctrees/index.doctree | Bin 3459 -> 0 bytes docs/_build/doctrees/source/examples.doctree | Bin 14807 -> 0 bytes docs/_build/doctrees/source/intro.doctree | Bin 15984 -> 0 bytes docs/_build/doctrees/source/modules.doctree | Bin 2797 -> 0 bytes .../_build/doctrees/source/notdiamond.doctree | Bin 131574 -> 0 bytes .../doctrees/source/notdiamond.llms.doctree | Bin 570498 -> 0 bytes .../source/notdiamond.metrics.doctree | Bin 34081 -> 0 bytes .../source/notdiamond.prompts.doctree | Bin 83623 -> 0 bytes .../source/notdiamond.toolkit.doctree | Bin 449342 -> 0 bytes .../source/notdiamond.toolkit.litellm.doctree | Bin 264315 -> 0 bytes .../source/notdiamond.toolkit.rag.doctree | Bin 315707 -> 0 bytes docs/_build/html/.buildinfo | 4 - docs/_build/html/.doctrees/environment.pickle | Bin 342026 -> 0 bytes docs/_build/html/.doctrees/index.doctree | Bin 3463 -> 0 bytes .../html/.doctrees/source/intro.doctree | Bin 15988 -> 0 bytes .../html/.doctrees/source/notdiamond.doctree | Bin 103714 -> 0 bytes .../.doctrees/source/notdiamond.llms.doctree | Bin 600084 -> 0 bytes .../source/notdiamond.metrics.doctree | Bin 35000 -> 0 bytes .../source/notdiamond.toolkit.doctree | Bin 63560 -> 0 bytes .../source/notdiamond.toolkit.rag.doctree | Bin 313876 -> 0 bytes docs/_build/html/.nojekyll | 0 docs/_build/html/_modules/index.html | 115 - .../html/_modules/notdiamond/_init.html | 257 -- .../html/_modules/notdiamond/callbacks.html | 154 - .../html/_modules/notdiamond/exceptions.html | 155 - .../html/_modules/notdiamond/llms/client.html | 1912 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docs/_build/html/_static/js/searchtools.js | 595 --- docs/_build/html/_static/js/theme.js | 1 - .../html/_static/js/vendor/bootstrap.min.js | 6 - docs/_build/html/_static/language_data.js | 199 - docs/_build/html/_static/minus.png | Bin 90 -> 0 bytes docs/_build/html/_static/plus.png | Bin 90 -> 0 bytes docs/_build/html/_static/pygments.css | 75 - docs/_build/html/_static/searchtools.js | 620 --- docs/_build/html/_static/sphinx_highlight.js | 154 - docs/_build/html/index.html | 131 - docs/_build/html/objects.inv | Bin 941 -> 0 bytes docs/_build/html/py-modindex.html | 152 - docs/_build/html/search.html | 127 - docs/_build/html/searchindex.js | 1 - docs/_build/html/source/examples.html | 239 -- docs/_build/html/source/intro.html | 170 - docs/_build/html/source/modules.html | 189 - docs/_build/html/source/notdiamond.html | 445 -- docs/_build/html/source/notdiamond.llms.html | 2016 --------- .../html/source/notdiamond.metrics.html | 193 - .../html/source/notdiamond.prompts.html | 136 - 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docs/source/notdiamond.llms.rst delete mode 100644 docs/source/notdiamond.metrics.rst delete mode 100644 docs/source/notdiamond.rst delete mode 100644 docs/source/notdiamond.toolkit.rag.rst delete mode 100644 docs/source/notdiamond.toolkit.rst diff --git a/.github/workflows/build_docs.yml b/.github/workflows/build_docs.yml deleted file mode 100644 index 5f8f8d1a..00000000 --- a/.github/workflows/build_docs.yml +++ /dev/null @@ -1,42 +0,0 @@ -# Simple workflow for deploying static content to GitHub Pages -name: Build docs based on Sphinx - -on: - # Runs on pushes targeting the default branch - push: - branches: ["main"] - - # Allows you to run this workflow manually from the Actions tab - workflow_dispatch: - -jobs: - build: - environment: - name: build-docs - runs-on: ubuntu-22.04 - steps: - - name: Checkout - uses: actions/checkout@v4 - - - name: Set working directory - uses: ./.github/actions/set-working-directory - with: - working_directory: "${{ github.workspace }}" - - - name: Setup python environment - uses: ./.github/actions/setup-python-env - with: - working_directory: "${{ env.WORKING_DIRECTORY }}" - - - name: Build docs - run: | - cd docs - make html - - - uses: EndBug/add-and-commit@v9.1.1 - with: - add: "${{ env.WORKING_DIRECTORY }}/docs/_build/html" - default_author: github_actor - fetch: true - message: "[skip ci] Update docs" - pathspec_error_handling: ignore diff --git a/.github/workflows/static_docs.yml b/.github/workflows/static_docs.yml deleted file mode 100644 index 467f7c6a..00000000 --- a/.github/workflows/static_docs.yml +++ /dev/null @@ -1,42 +0,0 @@ -# Simple workflow for deploying static content to GitHub Pages -name: Deploy docs to GitHub Pages - -on: - # Runs on pushes targeting the default branch - release: - types: [published] - - # Allows you to run this workflow manually from the Actions tab - workflow_dispatch: - -# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages -permissions: - contents: read - pages: write - id-token: write - -# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. -# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. -concurrency: - group: "pages" - cancel-in-progress: false - -jobs: - deploy: - environment: - name: github-pages - url: ${{ steps.deployment.outputs.page_url }} - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v4 - - name: Setup Pages - uses: actions/configure-pages@v5 - - name: Upload artifact - uses: actions/upload-pages-artifact@v3 - with: - # Upload entire repository - path: './docs/_build/html' - - name: Deploy to GitHub Pages - id: deployment - uses: actions/deploy-pages@v4 diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index d4bb2cbb..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command 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zAB4EwHpD%-EL}Rpbm|b7GTC}(i1Y6uPSS@sQ552A(sZ;;_b!>cvuv~uJ}TNaaDn(2 z3?gJX?pNtwcscO4z#E+$X*FVpl@&eA_!yU7lz_NMw#q84Ou&bDu-(M~^l%fF_yxjn z2ysSR(-^??q9voPMuYo}5aup8SA4%4oV+*mwt>4oRc(Xg4{l!v98}(59O)!JFNr@h dA9G2)Iog;WL!Wj6-OI&e7>tRyg^!Qn{|6Tg{>%UX diff --git a/docs/_build/html/.nojekyll b/docs/_build/html/.nojekyll deleted file mode 100644 index e69de29b..00000000 diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html deleted file mode 100644 index efe356cf..00000000 --- a/docs/_build/html/_modules/index.html +++ /dev/null @@ -1,115 +0,0 @@ - - - - - - Overview: module code — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -

- - -
- -
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    -
    • -
  • Overview: module code
    • -
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- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/_init.html b/docs/_build/html/_modules/notdiamond/_init.html deleted file mode 100644 index 6df7361b..00000000 --- a/docs/_build/html/_modules/notdiamond/_init.html +++ /dev/null @@ -1,257 +0,0 @@ - - - - - - notdiamond._init — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond._init

-import os
-from typing import Dict, List, Union
-
-from notdiamond.toolkit._retry import (
-    AsyncRetryWrapper,
-    ClientType,
-    ModelType,
-    OpenAIMessagesType,
-    RetryManager,
-    RetryWrapper,
-)
-
-
-

-[docs]
-def init(
-    client: Union[ClientType, List[ClientType]],
-    models: ModelType,
-    max_retries: Union[int, Dict[str, int]] = 1,
-    timeout: Union[float, Dict[str, float]] = 60.0,
-    model_messages: Dict[str, OpenAIMessagesType] = None,
-    api_key: Union[str, None] = None,
-    async_mode: bool = False,
-    backoff: Union[float, Dict[str, float]] = 2.0,
-) -> RetryManager:
-    """
-    Entrypoint for fallback and retry features without changing existing code.
-
-    Add this to existing codebase without other modifications to enable the following capabilities:
-
-    - Fallback to a different model if a model invocation fails.
-    - If configured, fallback to a different *provider* if a model invocation fails
-      (eg. azure/gpt-4o fails -> invoke openai/gpt-4o)
-    - Load-balance between models and providers, if specified.
-    - Pass timeout and retry configurations to each invoke, optionally configured per model.
-    - Pass model-specific messages on each retry (prepended to the provided `messages` parameter)
-
-    Parameters:
-        client (Union[ClientType, List[ClientType]]): Clients to apply retry/fallback logic to.
-        models (Union[Dict[str, float], List[str]]):
-            Models to use of the format <provider>/<model>.
-            Supports two formats:
-                - List of models, eg. ["openai/gpt-4o", "azure/gpt-4o"]. Models will be prioritized as listed.
-                - Dict of models to weights for load balancing, eg. {"openai/gpt-4o": 0.9, "azure/gpt-4o": 0.1}.
-                  If a model invocation fails, the next model is selected by sampling using the *remaining* weights.
-        max_retries (Union[int, Dict[str, int]]):
-            Maximum number of retries. Can be configured globally or per model.
-        timeout (Union[float, Dict[str, float]]):
-            Timeout in seconds per model. Can be configured globally or per model.
-        model_messages (Dict[str, OpenAIMessagesType]):
-            Model-specific messages to prepend to `messages` on each invocation, formatted OpenAI-style. Can be
-            configured using any role which is valid as an initial message (eg. "system" or "user", but not "assistant").
-        api_key (Optional[str]):
-            Not Diamond API key for authentication. Unused for now - will offer logging and metrics in the future.
-        async_mode (bool):
-            Whether to manage clients as async.
-        backoff (Union[float, Dict[str, float]]):
-            Backoff factor for exponential backoff per each retry. Can be configured globally or per model.
-
-    Returns:
-        RetryManager: Manager object that handles retries and fallbacks. Not required for usage.
-
-    Model Fallback Prioritization
-    -----------------------------
-
-    - If models is a list, the fallback model is selected in order after removing the failed model.
-      eg. If "openai/gpt-4o" fails for the list:
-        - ["openai/gpt-4o", "azure/gpt-4o"], "azure/gpt-4o" will be tried next
-        - ["openai/gpt-4o-mini", "openai/gpt-4o", "azure/gpt-4o"], "openai/gpt-4o-mini" will be tried next.
-    - If models is a dict, the next model is selected by sampling using the *remaining* weights.
-      eg. If "openai/gpt-4o" fails for the dict:
-        - {"openai/gpt-4o": 0.9, "azure/gpt-4o": 0.1}, "azure/gpt-4o" will be invoked 100% of the time
-        - {"openai/gpt-4o": 0.5, "azure/gpt-4o": 0.25, "openai/gpt-4o-mini": 0.25}, then "azure/gpt-4o" and
-          "openai/gpt-4o-mini" can be invoked with 50% probability each.
-
-    Usage
-    -----
-
-    Please refer to tests/test_init.py for more examples on how to use notdiamond.init.
-
-    .. code-block:: python
-
-        # ...existing workflow code, including client initialization...
-        openai_client = OpenAI(...)
-        azure_client = AzureOpenAI(...)
-
-        # Add `notdiamond.init` to the workflow.
-        notdiamond.init(
-            [openai_client, azure_client],
-            models={"openai/gpt-4o": 0.9, "azure/gpt-4o": 0.1},
-            max_retries={"openai/gpt-4o": 3, "azure/gpt-4o": 1},
-            timeout={"openai/gpt-4o": 10.0, "azure/gpt-4o": 5.0},
-            model_messages={
-                "openai/gpt-4o": [{"role": "user", "content": "Here is a prompt for OpenAI."}],
-                "azure/gpt-4o": [{"role": "user", "content": "Here is a prompt for Azure."}],
-            },
-            api_key="sk-...",
-            backoff=2.0,
-        )
-
-        # ...continue existing workflow code...
-        response = openai_client.chat.completions.create(
-            model="notdiamond",
-            messages=[{"role": "user", "content": "Hello!"}]
-        )
-
-    """
-    api_key = api_key or os.getenv("NOTDIAMOND_API_KEY")
-
-    if async_mode:
-        wrapper_cls = AsyncRetryWrapper
-    else:
-        wrapper_cls = RetryWrapper
-
-    for model in models:
-        if len(model.split("/")) != 2:
-            raise ValueError(
-                f"Model {model} must be in the format <provider>/<model>."
-            )
-
-    if not isinstance(client, List):
-        client_wrappers = [
-            wrapper_cls(
-                client=client,
-                models=models,
-                max_retries=max_retries,
-                timeout=timeout,
-                model_messages=model_messages,
-                api_key=api_key,
-                backoff=backoff,
-            )
-        ]
-    else:
-        client_wrappers = [
-            wrapper_cls(
-                client=cc,
-                models=models,
-                max_retries=max_retries,
-                timeout=timeout,
-                model_messages=model_messages,
-                api_key=api_key,
-                backoff=backoff,
-            )
-            for cc in client
-        ]
-    retry_manager = RetryManager(models, client_wrappers)
-
-    return retry_manager

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/callbacks.html b/docs/_build/html/_modules/notdiamond/callbacks.html deleted file mode 100644 index c0ec5678..00000000 --- a/docs/_build/html/_modules/notdiamond/callbacks.html +++ /dev/null @@ -1,154 +0,0 @@ - - - - - - notdiamond.callbacks — NotDiamond 0.2.7-beta - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.callbacks

-from typing import Any
-
-from langchain_core.callbacks.base import BaseCallbackHandler
-
-from notdiamond.llms.provider import NDLLMProvider
-
-
-

-[docs]
-class NDLLMBaseCallbackHandler(BaseCallbackHandler):
-    """
-    Base callback handler for NotDiamond LLMs.
-    Accepts all of the langchain_core callbacks and adds new ones.
-    """
-
-

-[docs]
-    def on_model_select(
-        self, model_provider: NDLLMProvider, model_name: str
-    ) -> Any:
-        """
-        Called when a model is selected.
-        """

-
-
-

-[docs]
-    def on_latency_tracking(
-        self,
-        session_id: str,
-        model_provider: NDLLMProvider,
-        tokens_per_second: float,
-    ):
-        """
-        Called when latency tracking is enabled.
-        """

-
-
-

-[docs]
-    def on_api_error(self, error_message: str):
-        """
-        Called when an NDLLM API error occurs.
-        """

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jul 08, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/exceptions.html b/docs/_build/html/_modules/notdiamond/exceptions.html deleted file mode 100644 index 3f484161..00000000 --- a/docs/_build/html/_modules/notdiamond/exceptions.html +++ /dev/null @@ -1,155 +0,0 @@ - - - - - - notdiamond.exceptions — NotDiamond 0.3.38 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.exceptions

-

-[docs]
-class UnsupportedLLMProvider(Exception):
-    """The exception class for unsupported LLM provider"""

-
-
-
-

-[docs]
-class UnsupportedEmbeddingProvider(Exception):
-    """The exception class for unsupported Embedding provider"""

-
-
-
-

-[docs]
-class InvalidApiKey(Exception):
-    """The exception class for InvalidApiKey"""

-
-
-
-

-[docs]
-class MissingApiKey(Exception):
-    """The exception class for MissingApiKey"""

-
-
-
-

-[docs]
-class MissingLLMConfigs(Exception):
-    """The exception class for empty LLM provider configs array"""

-
-
-
-

-[docs]
-class ApiError(Exception):
-    """The exception class for any ApiError"""

-
-
-
-

-[docs]
-class CreateUnavailableError(Exception):
-    """The exception class raised when `notdiamond[create]` is not available"""

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Feb 18, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/llms/client.html b/docs/_build/html/_modules/notdiamond/llms/client.html deleted file mode 100644 index fb0ecfaf..00000000 --- a/docs/_build/html/_modules/notdiamond/llms/client.html +++ /dev/null @@ -1,1912 +0,0 @@ - - - - - - notdiamond.llms.client — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.llms.client

-"""NotDiamond client class"""
-
-
-import inspect
-import logging
-import time
-import warnings
-from enum import Enum
-from typing import (
-    Any,
-    AsyncIterator,
-    Callable,
-    Dict,
-    Iterator,
-    List,
-    Optional,
-    Sequence,
-    Tuple,
-    Type,
-    Union,
-)
-
-# Details: https://python.langchain.com/v0.1/docs/guides/development/pydantic_compatibility/
-from pydantic import BaseModel
-from pydantic_partial import create_partial_model
-
-from notdiamond import settings
-from notdiamond._utils import _module_check, token_counter
-from notdiamond.exceptions import (
-    ApiError,
-    CreateUnavailableError,
-    MissingLLMConfigs,
-)
-from notdiamond.llms.config import LLMConfig
-from notdiamond.llms.providers import is_o1_model
-from notdiamond.llms.request import (
-    amodel_select,
-    create_preference_id,
-    model_select,
-    report_latency,
-)
-from notdiamond.metrics.metric import Metric
-from notdiamond.prompts import (
-    _curly_escape,
-    inject_system_prompt,
-    o1_system_prompt_translate,
-)
-from notdiamond.types import NDApiKeyValidator
-
-LOGGER = logging.getLogger(__name__)
-LOGGER.setLevel(logging.INFO)
-
-
-class _NDClientTarget(Enum):
-    ROUTER = "router"
-    INVOKER = "invoker"
-
-
-def _ndllm_factory(import_target: _NDClientTarget = None):
-    _invoke_error_msg_tmpl = (
-        "{fn_name} is not available. `notdiamond` can generate LLM responses after "
-        "installing additional dependencies via `pip install notdiamond[create]`."
-    )
-
-    _default_llm_config_invalid_warning = "The default LLMConfig set is invalid. Defaulting to {provider}/{model}"
-
-    _no_default_llm_config_warning = (
-        "No default LLMConfig set. Defaulting to {provider}/{model}"
-    )
-
-    class _NDRouterClient(BaseModel):
-        api_key: str
-        llm_configs: Optional[List[Union[LLMConfig, str]]]
-        default: Union[LLMConfig, int, str]
-        max_model_depth: Optional[int]
-        latency_tracking: bool
-        hash_content: bool
-        tradeoff: Optional[str]
-        preference_id: Optional[str]
-        tools: Optional[Sequence[Union[Dict[str, Any], Callable]]]
-        callbacks: Optional[List]
-        nd_api_url: Optional[str]
-        user_agent: Union[str, None]
-        max_retries: Optional[int]
-        timeout: Optional[Union[float, int]]
-
-        class Config:
-            arbitrary_types_allowed = True
-
-        def __init__(
-            self,
-            llm_configs: Optional[List[Union[LLMConfig, str]]] = None,
-            api_key: Optional[str] = None,
-            default: Union[LLMConfig, int, str] = 0,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: bool = True,
-            hash_content: bool = False,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            callbacks: Optional[List] = None,
-            tools: Optional[Sequence[Union[Dict[str, Any], Callable]]] = None,
-            nd_api_url: Optional[str] = settings.NOTDIAMOND_API_URL,
-            user_agent: Union[str, None] = None,
-            max_retries: Optional[int] = 3,
-            timeout: Optional[Union[float, int]] = 60.0,
-            **kwargs,
-        ):
-            if api_key is None:
-                api_key = settings.NOTDIAMOND_API_KEY
-            NDApiKeyValidator(api_key=api_key)
-
-            if user_agent is None:
-                user_agent = settings.DEFAULT_USER_AGENT
-
-            if llm_configs is not None:
-                llm_configs = self._parse_llm_configs_data(llm_configs)
-
-                if max_model_depth is None:
-                    max_model_depth = len(llm_configs)
-
-                if max_model_depth > len(llm_configs):
-                    LOGGER.warning(
-                        "WARNING: max_model_depth cannot be bigger than the number of LLMs."
-                    )
-                    max_model_depth = len(llm_configs)
-
-            if tradeoff is not None:
-                if tradeoff not in ["cost", "latency"]:
-                    raise ValueError(
-                        "Invalid tradeoff. Accepted values: cost, latency."
-                    )
-
-            if tradeoff is not None:
-                warnings.warn(
-                    "The tradeoff constructor parameter is deprecated and will be removed in a "
-                    "future version. Please specify the tradeoff when using model_select or invocation methods.",
-                    DeprecationWarning,
-                    stacklevel=2,
-                )
-
-            super().__init__(
-                api_key=api_key,
-                llm_configs=llm_configs,
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-                tools=tools,
-                callbacks=callbacks,
-                nd_api_url=nd_api_url,
-                user_agent=user_agent,
-                max_retries=max_retries,
-                timeout=timeout,
-                **kwargs,
-            )
-            self.user_agent = user_agent
-            assert (
-                self.api_key is not None
-            ), "API key is not set. Please set a Not Diamond API key."
-
-        @property
-        def chat(self):
-            return self
-
-        @property
-        def completions(self):
-            return self
-
-        def create_preference_id(self, name: Optional[str] = None) -> str:
-            return create_preference_id(self.api_key, name, self.nd_api_url)
-
-        async def amodel_select(
-            self,
-            messages: List[Dict[str, str]],
-            input: Optional[Dict[str, Any]] = None,
-            model: Optional[List[LLMConfig]] = None,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            metric: Metric = Metric("accuracy"),
-            previous_session: Optional[str] = None,
-            timeout: Optional[Union[float, int]] = None,
-            max_retries: Optional[int] = None,
-            **kwargs,
-        ) -> tuple[str, Optional[LLMConfig]]:
-            """
-            This function calls the NotDiamond backend to fetch the most suitable model for the given prompt,
-            and leaves the execution of the LLM call to the developer.
-            The function is async, so it's suitable for async codebases.
-
-            Parameters:
-                messages (List[Dict[str, str]]): List of messages, OpenAI style.
-                input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                            the values for those variables. Defaults to None, assuming no
-                                                            variables.
-                model (Optional[List[LLMConfig]]): List of models to choose from.
-                default (Optional[Union[LLMConfig, int, str]]): Default LLM.
-                max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                    of routing you're willing to go.
-                latency_tracking (Optional[bool]): Latency tracking flag.
-                hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-                tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                    for the router to determine the best LLM for a given query.
-                preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                                Defaults to None.
-                metric (Metric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                                Defaults to Metric("accuracy").
-                previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-                timeout (int): The number of seconds to wait before terminating the API call to Not Diamond backend.
-                max_retries (int): The number of retries to attempt before giving up.
-                nd_api_url (Optional[str]): The URL of the NotDiamond API. Defaults to settings.NOTDIAMOND_API_URL.
-                **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-            Returns:
-                tuple[str, Optional[LLMConfig]]: returns the session_id and the chosen LLM
-            """
-            if input is None:
-                input = {}
-
-            if model is not None:
-                llm_configs = self._parse_llm_configs_data(model)
-                self.llm_configs = llm_configs
-
-            self.validate_params(
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-            )
-
-            best_llm, session_id = await amodel_select(
-                messages=messages,
-                llm_configs=self.llm_configs,
-                metric=metric,
-                notdiamond_api_key=self.api_key,
-                max_model_depth=self.max_model_depth,
-                hash_content=self.hash_content,
-                tradeoff=self.tradeoff,
-                preference_id=self.preference_id,
-                tools=self.tools,
-                previous_session=previous_session,
-                timeout=timeout or self.timeout,
-                max_retries=max_retries or self.max_retries,
-                nd_api_url=self.nd_api_url,
-                _user_agent=self.user_agent,
-            )
-
-            if not best_llm:
-                LOGGER.warning(
-                    f"ND API error. Falling back to default provider={self.default_llm.provider}/{self.default_llm.model}."
-                )
-                best_llm = self.default_llm
-            self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-            return session_id, best_llm
-
-        def model_select(
-            self,
-            messages: List[Dict[str, str]],
-            input: Optional[Dict[str, Any]] = None,
-            model: Optional[List[LLMConfig]] = None,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            metric: Metric = Metric("accuracy"),
-            previous_session: Optional[str] = None,
-            timeout: Optional[Union[float, int]] = None,
-            max_retries: Optional[int] = None,
-            **kwargs,
-        ) -> tuple[str, Optional[LLMConfig]]:
-            """
-            This function calls the NotDiamond backend to fetch the most suitable model for the given prompt,
-            and leaves the execution of the LLM call to the developer.
-
-            Parameters:
-                messages (List[Dict[str, str]]): List of messages OpenAI style.
-                input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                            the values for those variables. Defaults to None, assuming no
-                                                            variables.
-                model (Optional[List[LLMConfig]]): List of models to choose from.
-                default (Optional[Union[LLMConfig, int, str]]): Default LLM.
-                max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                    of routing you're willing to go.
-                latency_tracking (Optional[bool]): Latency tracking flag.
-                hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-                tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                    for the router to determine the best LLM for a given query.
-                preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                                Defaults to None.
-                metric (Metric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                                Defaults to Metric("accuracy").
-                previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-                timeout (int): The number of seconds to wait before terminating the API call to Not Diamond backend.
-                max_retries (int): The number of retries to attempt before giving up.
-                **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-            Returns:
-                tuple[str, Optional[LLMConfig]]: returns the session_id and the chosen LLM
-            """
-            if input is None:
-                input = {}
-
-            if model is not None:
-                llm_configs = self._parse_llm_configs_data(model)
-                self.llm_configs = llm_configs
-
-            self.validate_params(
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-            )
-
-            best_llm, session_id = model_select(
-                messages=messages,
-                llm_configs=self.llm_configs,
-                metric=metric,
-                notdiamond_api_key=self.api_key,
-                max_model_depth=self.max_model_depth,
-                hash_content=self.hash_content,
-                tradeoff=self.tradeoff,
-                preference_id=self.preference_id,
-                tools=self.tools,
-                previous_session=previous_session,
-                timeout=timeout or self.timeout,
-                max_retries=max_retries or self.max_retries,
-                nd_api_url=self.nd_api_url,
-                _user_agent=self.user_agent,
-            )
-
-            if not best_llm:
-                LOGGER.warning(
-                    f"ND API error. Falling back to default provider={self.default_llm.provider}/{self.default_llm.model}."
-                )
-                best_llm = self.default_llm
-            self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-            return session_id, best_llm
-
-        @staticmethod
-        def _parse_llm_configs_data(
-            llm_configs: list,
-        ) -> List[LLMConfig]:
-            providers = []
-            for llm_config in llm_configs:
-                if isinstance(llm_config, LLMConfig):
-                    providers.append(llm_config)
-                    continue
-                parsed_provider = LLMConfig.from_string(llm_config)
-                providers.append(parsed_provider)
-            return providers
-
-        def validate_params(
-            self,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-        ):
-            self.default = default
-
-            if max_model_depth is not None:
-                self.max_model_depth = max_model_depth
-
-            if self.llm_configs is None or len(self.llm_configs) == 0:
-                raise MissingLLMConfigs(
-                    "No LLM config speficied. Specify at least one."
-                )
-
-            if self.max_model_depth is None:
-                self.max_model_depth = len(self.llm_configs)
-
-            if self.max_model_depth == 0:
-                raise ValueError("max_model_depth has to be bigger than 0.")
-
-            if self.max_model_depth > len(self.llm_configs):
-                LOGGER.warning(
-                    "WARNING: max_model_depth cannot be bigger than the number of LLMs."
-                )
-                self.max_model_depth = len(self.llm_configs)
-
-            if tradeoff is not None:
-                if tradeoff not in ["cost", "latency"]:
-                    raise ValueError(
-                        "Invalid tradeoff. Accepted values: cost, latency."
-                    )
-            self.tradeoff = tradeoff
-
-            if preference_id is not None:
-                self.preference_id = preference_id
-
-            if latency_tracking is not None:
-                self.latency_tracking = latency_tracking
-
-            if hash_content is not None:
-                self.hash_content = hash_content
-
-        def bind_tools(
-            self, tools: Sequence[Union[Dict[str, Any], Callable]]
-        ) -> "NotDiamond":
-            """
-            Bind tools to the LLM object. The tools will be passed to the LLM object when invoking it.
-            Results in the tools being available in the LLM object.
-            You can access the tool_calls in the result via `result.tool_calls`.
-            """
-
-            for provider in self.llm_configs:
-                if provider.model not in settings.PROVIDERS[
-                    provider.provider
-                ].get("support_tools", []):
-                    raise ApiError(
-                        f"{provider.provider}/{provider.model} does not support function calling."
-                    )
-            self.tools = tools
-
-            return self
-
-        def call_callbacks(self, function_name: str, *args, **kwargs) -> None:
-            """
-            Call all callbacks with a specific function name.
-            """
-
-            if self.callbacks is None:
-                return
-
-            for callback in self.callbacks:
-                if hasattr(callback, function_name):
-                    getattr(callback, function_name)(*args, **kwargs)
-
-        def create(*args, **kwargs):
-            format_str = f"`{inspect.stack()[0].function}`"
-            raise CreateUnavailableError(
-                _invoke_error_msg_tmpl.format(fn_name=format_str)
-            )
-
-        async def acreate(*args, **kwargs):
-            format_str = f"`{inspect.stack()[0].function}`"
-            raise CreateUnavailableError(
-                _invoke_error_msg_tmpl.format(fn_name=format_str)
-            )
-
-        def invoke(*args, **kwargs):
-            format_str = f"`{inspect.stack()[0].function}`"
-            raise CreateUnavailableError(
-                _invoke_error_msg_tmpl.format(fn_name=format_str)
-            )
-
-        async def ainvoke(*args, **kwargs):
-            format_str = f"`{inspect.stack()[0].function}`"
-            raise CreateUnavailableError(
-                _invoke_error_msg_tmpl.format(fn_name=format_str)
-            )
-
-        def stream(*args, **kwargs):
-            raise CreateUnavailableError(
-                _invoke_error_msg_tmpl.format(
-                    fn_name=inspect.stack()[0].function
-                )
-            )
-
-        async def astream(*args, **kwargs):
-            raise CreateUnavailableError(
-                _invoke_error_msg_tmpl.format(
-                    fn_name=inspect.stack()[0].function
-                )
-            )
-
-        @property
-        def default_llm(self) -> LLMConfig:
-            """
-            Return the default LLM that's set on the NotDiamond client class.
-            """
-            if isinstance(self.default, int):
-                if self.default < len(self.llm_configs):
-                    return self.llm_configs[self.default]
-
-            if isinstance(self.default, str):
-                try:
-                    default = LLMConfig.from_string(self.default)
-                    if default in self.llm_configs:
-                        return default
-                except Exception as e:
-                    LOGGER.debug(f"Error setting default llm: {e}")
-
-            if isinstance(self.default, LLMConfig):
-                return self.default
-
-            default = self.llm_configs[0]
-            if self.default is None:
-                LOGGER.info(
-                    _no_default_llm_config_warning.format(
-                        provider=default.provider, model=default.model
-                    )
-                )
-            else:
-                LOGGER.info(
-                    _default_llm_config_invalid_warning.format(
-                        provider=default.provider, model=default.model
-                    )
-                )
-            return default
-
-    # Do not import from langchain_core directly, as it is now an optional SDK dependency
-    try:
-        LLM = _module_check("langchain_core.language_models.llms", "LLM")
-        BaseMessageChunk = _module_check(
-            "langchain_core.messages", "BaseMessageChunk"
-        )
-        JsonOutputParser = _module_check(
-            "langchain_core.output_parsers", "JsonOutputParser"
-        )
-        ChatPromptTemplate = _module_check(
-            "langchain_core.prompts", "ChatPromptTemplate"
-        )
-    except (ModuleNotFoundError, ImportError) as ierr:
-        msg = _invoke_error_msg_tmpl.format(fn_name="NotDiamond creation")
-        if import_target == _NDClientTarget.INVOKER:
-            msg += " Create was requested, however - raising..."
-            raise ImportError(msg) from ierr
-        else:
-            LOGGER.debug(msg)
-            return _NDRouterClient
-
-    class _NDInvokerClient(_NDRouterClient, LLM):
-        """
-        Implementation of NotDiamond class, the main class responsible for creating and invoking LLM prompts.
-        The class inherits from Langchain's LLM class. Starting reference is from here:
-        https://python.langchain.com/docs/modules/model_io/llms/custom_llm
-
-        It's mandatory to have an API key set. If the api_key is not explicitly specified,
-        it will check for NOTDIAMOND_API_KEY in the .env file.
-
-        Raises:
-            MissingLLMProviders: you must specify at least one LLM provider for the router to work
-            ApiError: error raised when the NotDiamond API call fails.
-                        Ensure to set a default LLM provider to not break the code.
-        """
-
-        api_key: str
-        llm_configs: Optional[List[Union[LLMConfig, str]]]
-        default: Union[LLMConfig, int, str]
-        max_model_depth: Optional[int]
-        latency_tracking: bool
-        hash_content: bool
-        tradeoff: Optional[str]
-        preference_id: Optional[str]
-        tools: Optional[Sequence[Union[Dict[str, Any], Callable]]]
-        callbacks: Optional[List]
-        nd_api_url: Optional[str]
-        user_agent: Union[str, None]
-
-        def __init__(
-            self,
-            llm_configs: Optional[List[Union[LLMConfig, str]]] = None,
-            api_key: Optional[str] = None,
-            default: Union[LLMConfig, int, str] = 0,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: bool = True,
-            hash_content: bool = False,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            tools: Optional[Sequence[Union[Dict[str, Any], Callable]]] = None,
-            callbacks: Optional[List] = None,
-            nd_api_url: Optional[str] = settings.NOTDIAMOND_API_URL,
-            user_agent: Union[str, None] = None,
-            timeout: Optional[Union[float, int]] = 60.0,
-            max_retries: Optional[int] = 3,
-            **kwargs,
-        ) -> None:
-            super().__init__(
-                api_key=api_key,
-                llm_configs=llm_configs,
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-                tools=tools,
-                callbacks=callbacks,
-                nd_api_url=nd_api_url,
-                user_agent=user_agent,
-                timeout=timeout,
-                max_retries=max_retries,
-                **kwargs,
-            )
-            if user_agent is None:
-                user_agent = settings.DEFAULT_USER_AGENT
-
-            if tradeoff is not None:
-                warnings.warn(
-                    "The tradeoff constructor parameter is deprecated and will be removed in a "
-                    "future version. Please specify the tradeoff when using model_select or invocation methods.",
-                    DeprecationWarning,
-                    stacklevel=2,
-                )
-
-            self.user_agent = user_agent
-            assert (
-                self.api_key is not None
-            ), "API key is not set. Please set a Not Diamond API key."
-
-        def __repr__(self) -> str:
-            class_name = self.__class__.__name__
-            address = hex(id(self))  # Gets the memory address of the object
-            return f"<{class_name} object at {address}>"
-
-        @property
-        def _llm_type(self) -> str:
-            return "NotDiamond LLM"
-
-        @staticmethod
-        def _inject_model_instruction(messages, parser):
-            format_instructions = parser.get_format_instructions()
-            format_instructions = format_instructions.replace(
-                "{", "{{"
-            ).replace("}", "}}")
-            messages[0]["content"] = (
-                format_instructions + "\n" + messages[0]["content"]
-            )
-            return messages
-
-        def _call(
-            self,
-            prompt: str,
-            stop: Optional[List[str]] = None,
-            run_manager: Optional[Any] = None,
-            **kwargs: Any,
-        ) -> str:
-            if stop is not None:
-                raise ValueError("stop kwargs are not permitted.")
-            return "This function is deprecated for the latest LangChain version, use invoke instead"
-
-        def create(
-            self,
-            messages: List[Dict[str, str]],
-            model: Optional[List[LLMConfig]] = None,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            metric: Metric = Metric("accuracy"),
-            previous_session: Optional[str] = None,
-            response_model: Optional[Type[BaseModel]] = None,
-            timeout: Optional[Union[float, int]] = None,
-            max_retries: Optional[int] = None,
-            **kwargs,
-        ) -> tuple[str, str, LLMConfig]:
-            """
-            Function call to invoke the LLM, with the same interface
-            as the OpenAI Python library.
-
-            Parameters:
-                messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                    the messages OpenAI style.
-                model (Optional[List[LLMConfig]]): List of models to choose from.
-                default (Optional[Union[LLMConfig, int, str]]): Default LLM.
-                max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                    of routing you're willing to go.
-                latency_tracking (Optional[bool]): Latency tracking flag.
-                hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-                tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                    for the router to determine the best LLM for a given query.
-                preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                                Defaults to None.
-                metric (Metric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                                Defaults to Metric("accuracy").
-                previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-                response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the
-                                                                response into the given model. In which case result will a
-                                                                dict.
-                timeout (int): The number of seconds to wait before terminating the API call to Not Diamond backend.
-                max_retries (int): The number of retries to attempt before giving up.
-                nd_api_url (Optional[str]): The URL of the NotDiamond API. Defaults to settings.NOTDIAMOND_API_URL.
-                **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-            Raises:
-                ApiError: when the NotDiamond API fails
-
-            Returns:
-                tuple[Union[AIMessage, BaseModel], str, LLMConfig]:
-                    result: response type defined by Langchain, contains the response from the LLM.
-                    or object of the response_model
-                    str: session_id returned by the NotDiamond API
-                    LLMConfig: the best LLM selected by the router
-            """
-
-            return self.invoke(
-                messages=messages,
-                model=model,
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-                metric=metric,
-                previous_session=previous_session,
-                response_model=response_model,
-                timeout=timeout,
-                max_retries=max_retries,
-                **kwargs,
-            )
-
-        async def acreate(
-            self,
-            messages: List[Dict[str, str]],
-            model: Optional[List[LLMConfig]] = None,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            metric: Metric = Metric("accuracy"),
-            previous_session: Optional[str] = None,
-            response_model: Optional[Type[BaseModel]] = None,
-            timeout: Optional[Union[float, int]] = None,
-            max_retries: Optional[int] = None,
-            **kwargs,
-        ) -> tuple[str, str, LLMConfig]:
-            """
-            Async function call to invoke the LLM, with the same interface
-            as the OpenAI Python library.
-
-            Parameters:
-                messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                    the messages OpenAI style.
-                model (Optional[List[LLMConfig]]): List of models to choose from.
-                default (Optional[Union[LLMConfig, int, str]]): Default LLM.
-                max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                    of routing you're willing to go.
-                latency_tracking (Optional[bool]): Latency tracking flag.
-                hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-                tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                    for the router to determine the best LLM for a given query.
-                preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                                Defaults to None.
-                metric (Metric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                                Defaults to Metric("accuracy").
-                previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-                response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the
-                                                                response into the given model. In which case result will a
-                                                                dict.
-                timeout (int): The number of seconds to wait before terminating the API call to Not Diamond backend.
-                max_retries (int): The number of retries to attempt before giving up.
-                **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-            Raises:
-                ApiError: when the NotDiamond API fails
-
-            Returns:
-                tuple[Union[AIMessage, BaseModel], str, LLMConfig]:
-                    result: response type defined by Langchain, contains the response from the LLM.
-                    or object of the response_model
-                    str: session_id returned by the NotDiamond API
-                    LLMConfig: the best LLM selected by the router
-            """
-
-            result = await self.ainvoke(
-                messages=messages,
-                model=model,
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-                metric=metric,
-                previous_session=previous_session,
-                response_model=response_model,
-                timeout=timeout,
-                max_retries=max_retries,
-                **kwargs,
-            )
-            return result
-
-        def invoke(
-            self,
-            messages: List[Dict[str, str]],
-            model: Optional[List[LLMConfig]] = None,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            metric: Metric = Metric("accuracy"),
-            previous_session: Optional[str] = None,
-            response_model: Optional[Type[BaseModel]] = None,
-            timeout: Optional[Union[float, int]] = None,
-            max_retries: Optional[int] = None,
-            input: Optional[Dict[str, Any]] = None,
-            **kwargs,
-        ) -> tuple[str, str, LLMConfig]:
-            """
-            Function to invoke the LLM. Behind the scenes what happens:
-            1. API call to NotDiamond backend to get the most suitable LLM for the given prompt
-            2. Invoke the returned LLM client side
-            3. Return the response
-
-            Parameters:
-                prompt_template (Optional(Union[ NDPromptTemplate, NDChatPromptTemplate, str, ])):
-                    the prompt template defined by the user. It also supports Langchain prompt template types.
-                messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                    the messages OpenAI style.
-                model (Optional[List[LLMConfig]]): List of models to choose from.
-                default (Optional[Union[LLMConfig, int, str]]): Default LLM.
-                max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                    of routing you're willing to go.
-                latency_tracking (Optional[bool]): Latency tracking flag.
-                hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-                tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                    for the router to determine the best LLM for a given query.
-                preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                                Defaults to None.
-                metric (Metric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                                Defaults to Metric("accuracy").
-                previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-                response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the
-                                                                response into the given model. In which case result will a
-                                                                dict.
-                timeout (int): The number of seconds to wait before terminating the API call to Not Diamond backend.
-                max_retries (int): The number of retries to attempt before giving up.
-                input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                            the values for those variables. Defaults to None, assuming no
-                                                            variables.
-                **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-            Raises:
-                ApiError: when the NotDiamond API fails
-
-            Returns:
-                tuple[Union[AIMessage, BaseModel], str, LLMConfig]:
-                    result: response type defined by Langchain, contains the response from the LLM.
-                    or object of the response_model
-                    str: session_id returned by the NotDiamond API
-                    LLMConfig: the best LLM selected by the router
-            """
-
-            if model is not None:
-                llm_configs = self._parse_llm_configs_data(model)
-                self.llm_configs = llm_configs
-
-            self.validate_params(
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-            )
-
-            # If response_model is present, we will parse the response into the given model
-            # doing this here so that if validation errors occur, we can raise them before making the API call
-            response_model_parser = None
-            if response_model is not None:
-                self.verify_against_response_model()
-                response_model_parser = JsonOutputParser(
-                    pydantic_object=response_model
-                )
-
-            if input is None:
-                input = {}
-
-            best_llm, session_id = model_select(
-                messages=messages,
-                llm_configs=self.llm_configs,
-                metric=metric,
-                notdiamond_api_key=self.api_key,
-                max_model_depth=self.max_model_depth,
-                hash_content=self.hash_content,
-                tradeoff=self.tradeoff,
-                preference_id=self.preference_id,
-                tools=self.tools,
-                previous_session=previous_session,
-                timeout=timeout or self.timeout,
-                max_retries=max_retries or self.max_retries,
-                nd_api_url=self.nd_api_url,
-            )
-
-            is_default = False
-            if not best_llm:
-                LOGGER.warning(
-                    f"ND API error. Falling back to default provider={self.default_llm.provider}/{self.default_llm.model}."
-                )
-                best_llm = self.default_llm
-                is_default = True
-
-            if best_llm.system_prompt is not None:
-                messages = inject_system_prompt(
-                    messages, best_llm.system_prompt
-                )
-
-            messages = o1_system_prompt_translate(messages, best_llm)
-
-            self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-            llm = self._llm_from_config(best_llm, callbacks=self.callbacks)
-
-            if self.tools and not is_o1_model(best_llm):
-                llm = llm.bind_tools(self.tools)
-
-            if response_model is not None:
-                messages = _NDInvokerClient._inject_model_instruction(
-                    messages, response_model_parser
-                )
-            chain_messages = [
-                (msg["role"], _curly_escape(msg["content"]))
-                for msg in messages
-            ]
-            prompt_template = ChatPromptTemplate.from_messages(chain_messages)
-            chain = prompt_template | llm
-            accepted_errors = _get_accepted_invoke_errors(best_llm.provider)
-
-            try:
-                if self.latency_tracking:
-                    result = self._invoke_with_latency_tracking(
-                        session_id=session_id,
-                        chain=chain,
-                        llm_config=best_llm,
-                        is_default=is_default,
-                        input=input,
-                        **kwargs,
-                    )
-                else:
-                    result = chain.invoke(input, **kwargs)
-            except accepted_errors as e:
-                if best_llm.provider == "google":
-                    LOGGER.warning(
-                        f"Submitted chat messages are violating Google requirements with error {e}. "
-                        "If you see this message, `notdiamond` has returned a Google model as the best option, "
-                        "but the LLM call will fail. If possible, `notdiamond` will fall back to a non-Google model."
-                    )
-
-                    non_google_llm = next(
-                        (
-                            llm_config
-                            for llm_config in self.llm_configs
-                            if llm_config.provider != "google"
-                        ),
-                        None,
-                    )
-
-                    if non_google_llm is not None:
-                        best_llm = non_google_llm
-                        llm = self._llm_from_config(
-                            best_llm, callbacks=self.callbacks
-                        )
-                        if response_model is not None:
-                            messages = (
-                                _NDInvokerClient._inject_model_instruction(
-                                    messages, response_model_parser
-                                )
-                            )
-                        chain_messages = [
-                            (msg["role"], _curly_escape(msg["content"]))
-                            for msg in messages
-                        ]
-                        prompt_template = ChatPromptTemplate.from_messages(
-                            chain_messages
-                        )
-                        chain = prompt_template | llm
-
-                        if self.latency_tracking:
-                            result = self._invoke_with_latency_tracking(
-                                session_id=session_id,
-                                chain=chain,
-                                llm_config=best_llm,
-                                is_default=is_default,
-                                input=input,
-                                **kwargs,
-                            )
-                        else:
-                            result = chain.invoke(input, **kwargs)
-                    else:
-                        raise e
-                else:
-                    raise e
-
-            if response_model is not None:
-                parsed_dict = response_model_parser.parse(result.content)
-                result = response_model.parse_obj(parsed_dict)
-
-            return result, session_id, best_llm
-
-        async def ainvoke(
-            self,
-            messages: List[Dict[str, str]],
-            model: Optional[List[LLMConfig]] = None,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            metric: Metric = Metric("accuracy"),
-            previous_session: Optional[str] = None,
-            response_model: Optional[Type[BaseModel]] = None,
-            timeout: Optional[Union[float, int]] = None,
-            max_retries: Optional[int] = None,
-            input: Optional[Dict[str, Any]] = None,
-            **kwargs,
-        ) -> tuple[str, str, LLMConfig]:
-            """
-            Function to invoke the LLM. Behind the scenes what happens:
-            1. API call to NotDiamond backend to get the most suitable LLM for the given prompt
-            2. Invoke the returned LLM client side
-            3. Return the response
-
-            Parameters:
-                messages (List[Dict[str, str]]): List of messages, OpenAI style
-                model (Optional[List[LLMConfig]]): List of models to choose from.
-                default (Optional[Union[LLMConfig, int, str]]): Default LLM.
-                max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                    of routing you're willing to go.
-                latency_tracking (Optional[bool]): Latency tracking flag.
-                hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-                tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                    for the router to determine the best LLM for a given query.
-                preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                                Defaults to None.
-                metric (Metric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                                Defaults to Metric("accuracy").
-                previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-                response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the
-                                                                response into the given model. In which case result will a dict.
-                timeout (int): The number of seconds to wait before terminating the API call to Not Diamond backend.
-                max_retries (int): The number of retries to attempt before giving up.
-                input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                            the values for those variables. Defaults to None, assuming no
-                                                            variables.
-                **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-            Raises:
-                ApiError: when the NotDiamond API fails
-
-            Returns:
-                tuple[Union[AIMessage, BaseModel], str, LLMConfig]:
-                    result: response type defined by Langchain, contains the response from the LLM.
-                    or object of the response_model
-                    str: session_id returned by the NotDiamond API
-                    LLMConfig: the best LLM selected by the router
-            """
-
-            if model is not None:
-                llm_configs = self._parse_llm_configs_data(model)
-                self.llm_configs = llm_configs
-
-            self.validate_params(
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-            )
-
-            response_model_parser = None
-            if response_model is not None:
-                self.verify_against_response_model()
-                response_model_parser = JsonOutputParser(
-                    pydantic_object=response_model
-                )
-
-            if input is None:
-                input = {}
-
-            best_llm, session_id = await amodel_select(
-                messages=messages,
-                llm_configs=self.llm_configs,
-                metric=metric,
-                notdiamond_api_key=self.api_key,
-                max_model_depth=self.max_model_depth,
-                hash_content=self.hash_content,
-                tradeoff=self.tradeoff,
-                preference_id=self.preference_id,
-                tools=self.tools,
-                previous_session=previous_session,
-                timeout=timeout or self.timeout,
-                max_retries=max_retries or self.max_retries,
-                nd_api_url=self.nd_api_url,
-            )
-
-            is_default = False
-            if not best_llm:
-                LOGGER.warning(
-                    f"ND API error. Falling back to default provider={self.default_llm.provider}/{self.default_llm.model}."
-                )
-                best_llm = self.default_llm
-                is_default = True
-
-            if best_llm.system_prompt is not None:
-                messages = inject_system_prompt(
-                    messages, best_llm.system_prompt
-                )
-
-            messages = o1_system_prompt_translate(messages, best_llm)
-
-            self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-            llm = self._llm_from_config(best_llm, callbacks=self.callbacks)
-
-            if self.tools and not is_o1_model(best_llm):
-                llm = llm.bind_tools(self.tools)
-
-            if response_model is not None:
-                messages = _NDInvokerClient._inject_model_instruction(
-                    messages, response_model_parser
-                )
-            chain_messages = [
-                (msg["role"], _curly_escape(msg["content"]))
-                for msg in messages
-            ]
-            prompt_template = ChatPromptTemplate.from_messages(chain_messages)
-            chain = prompt_template | llm
-            accepted_errors = _get_accepted_invoke_errors(best_llm.provider)
-
-            try:
-                if self.latency_tracking:
-                    result = await self._async_invoke_with_latency_tracking(
-                        session_id=session_id,
-                        chain=chain,
-                        llm_config=best_llm,
-                        is_default=is_default,
-                        input=input,
-                        **kwargs,
-                    )
-                else:
-                    result = await chain.ainvoke(input, **kwargs)
-            except accepted_errors as e:
-                if best_llm.provider == "google":
-                    LOGGER.warning(
-                        f"Submitted chat messages are violating Google requirements with error {e}. "
-                        "If you see this message, `notdiamond` has returned a Google model as the best option, "
-                        "but the LLM call will fail. If possible, `notdiamond` will fall back to a non-Google model."
-                    )
-
-                    non_google_llm = next(
-                        (
-                            llm_config
-                            for llm_config in self.llm_configs
-                            if llm_config.provider != "google"
-                        ),
-                        None,
-                    )
-
-                    if non_google_llm is not None:
-                        best_llm = non_google_llm
-                        llm = self._llm_from_config(
-                            best_llm, callbacks=self.callbacks
-                        )
-                        if response_model is not None:
-                            messages = (
-                                _NDInvokerClient._inject_model_instruction(
-                                    messages, response_model_parser
-                                )
-                            )
-                        chain_messages = [
-                            (msg["role"], _curly_escape(msg["content"]))
-                            for msg in messages
-                        ]
-                        prompt_template = ChatPromptTemplate.from_messages(
-                            chain_messages
-                        )
-                        chain = prompt_template | llm
-
-                        if self.latency_tracking:
-                            result = (
-                                await self._async_invoke_with_latency_tracking(
-                                    session_id=session_id,
-                                    chain=chain,
-                                    llm_config=best_llm,
-                                    is_default=is_default,
-                                    input=input,
-                                    **kwargs,
-                                )
-                            )
-                        else:
-                            result = await chain.ainvoke(input, **kwargs)
-                    else:
-                        raise e
-                else:
-                    raise e
-
-            if response_model is not None:
-                parsed_dict = response_model_parser.parse(result.content)
-                result = response_model.parse_obj(parsed_dict)
-
-            return result, session_id, best_llm
-
-        def stream(
-            self,
-            messages: List[Dict[str, str]],
-            model: Optional[List[LLMConfig]] = None,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            metric: Metric = Metric("accuracy"),
-            previous_session: Optional[str] = None,
-            response_model: Optional[Type[BaseModel]] = None,
-            timeout: Optional[Union[float, int]] = None,
-            max_retries: Optional[int] = None,
-            **kwargs,
-        ) -> Iterator[Union[BaseMessageChunk, BaseModel]]:
-            """
-            This function calls the NotDiamond backend to fetch the most suitable model for the given prompt,
-            and calls the LLM client side to stream the response.
-
-            Parameters:
-                messages (Optional[List[Dict[str, str]], optional): List of messages, OpenAI style
-                model (Optional[List[LLMConfig]]): List of models to choose from.
-                default (Optional[Union[LLMConfig, int, str]]): Default LLM.
-                max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                    of routing you're willing to go.
-                latency_tracking (Optional[bool]): Latency tracking flag.
-                hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-                tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                    for the router to determine the best LLM for a given query.
-                preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                                Defaults to None.
-                metric (Metric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                                Defaults to Metric("accuracy").
-                previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-                response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the
-                                                                response into the given model. In which case result will a
-                                                                dict.
-                timeout (int): The number of seconds to wait before terminating the API call to Not Diamond backend.
-                max_retries (int): The number of retries to attempt before giving up.
-                **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-            Raises:
-                ApiError: when the NotDiamond API fails
-
-            Yields:
-                Iterator[Union[BaseMessageChunk, BaseModel]]: returns the response in chunks.
-                    If response_model is present, it will return the partial model object
-            """
-
-            if model is not None:
-                llm_configs = self._parse_llm_configs_data(model)
-                self.llm_configs = llm_configs
-
-            self.validate_params(
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-            )
-
-            response_model_parser = None
-            if response_model is not None:
-                self.verify_against_response_model()
-                response_model_parser = JsonOutputParser(
-                    pydantic_object=response_model
-                )
-
-            best_llm, session_id = model_select(
-                messages=messages,
-                llm_configs=self.llm_configs,
-                metric=metric,
-                notdiamond_api_key=self.api_key,
-                max_model_depth=self.max_model_depth,
-                hash_content=self.hash_content,
-                tradeoff=self.tradeoff,
-                preference_id=self.preference_id,
-                tools=self.tools,
-                previous_session=previous_session,
-                timeout=timeout or self.timeout,
-                max_retries=max_retries or self.max_retries,
-                nd_api_url=self.nd_api_url,
-            )
-
-            if not best_llm:
-                LOGGER.warning(
-                    f"ND API error. Falling back to default provider={self.default_llm.provider}/{self.default_llm.model}."
-                )
-                best_llm = self.default_llm
-
-            if best_llm.system_prompt is not None:
-                messages = inject_system_prompt(
-                    messages, best_llm.system_prompt
-                )
-
-            if response_model is not None:
-                messages = _NDInvokerClient._inject_model_instruction(
-                    messages, response_model_parser
-                )
-
-            self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-            llm = self._llm_from_config(best_llm, callbacks=self.callbacks)
-            if self.tools:
-                llm = llm.bind_tools(self.tools)
-
-            if response_model is not None:
-                chain = llm | response_model_parser
-            else:
-                chain = llm
-
-            for chunk in chain.stream(messages, **kwargs):
-                if response_model is None:
-                    yield chunk
-                else:
-                    partial_model = create_partial_model(response_model)
-                    yield partial_model(**chunk)
-
-        async def astream(
-            self,
-            messages: List[Dict[str, str]],
-            model: Optional[List[LLMConfig]] = None,
-            default: Optional[Union[LLMConfig, int, str]] = None,
-            max_model_depth: Optional[int] = None,
-            latency_tracking: Optional[bool] = None,
-            hash_content: Optional[bool] = None,
-            tradeoff: Optional[str] = None,
-            preference_id: Optional[str] = None,
-            metric: Metric = Metric("accuracy"),
-            previous_session: Optional[str] = None,
-            response_model: Optional[Type[BaseModel]] = None,
-            timeout: Optional[Union[float, int]] = None,
-            max_retries: Optional[int] = None,
-            **kwargs,
-        ) -> AsyncIterator[Union[BaseMessageChunk, BaseModel]]:
-            """
-            This function calls the NotDiamond backend to fetch the most suitable model for the given prompt,
-            and calls the LLM client side to stream the response. The function is async, so it's suitable for async codebases.
-
-            Parameters:
-                messages (Optional[List[Dict[str, str]], optional): List of messages, OpenAI style
-                model (Optional[List[LLMConfig]]): List of models to choose from.
-                default (Optional[Union[LLMConfig, int, str]]): Default LLM.
-                max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                    of routing you're willing to go.
-                latency_tracking (Optional[bool]): Latency tracking flag.
-                hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-                tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                    for the router to determine the best LLM for a given query.
-                preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                                Defaults to None.
-                metric (Metric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                                Defaults to Metric("accuracy").
-                previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-                response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the
-                                                                response into the given model. In which case result will a dict.
-                timeout (int): The number of seconds to wait before terminating the API call to Not Diamond backend.
-                max_retries (int): The number of retries to attempt before giving up.
-                **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-            Raises:
-                ApiError: when the NotDiamond API fails
-
-            Yields:
-                AsyncIterator[Union[BaseMessageChunk, BaseModel]]: returns the response in chunks.
-                    If response_model is present, it will return the partial model object
-            """
-
-            if model is not None:
-                llm_configs = self._parse_llm_configs_data(model)
-                self.llm_configs = llm_configs
-
-            self.validate_params(
-                default=default,
-                max_model_depth=max_model_depth,
-                latency_tracking=latency_tracking,
-                hash_content=hash_content,
-                tradeoff=tradeoff,
-                preference_id=preference_id,
-            )
-
-            response_model_parser = None
-            if response_model is not None:
-                self.verify_against_response_model()
-                response_model_parser = JsonOutputParser(
-                    pydantic_object=response_model
-                )
-
-            best_llm, session_id = await amodel_select(
-                messages=messages,
-                llm_configs=self.llm_configs,
-                metric=metric,
-                notdiamond_api_key=self.api_key,
-                max_model_depth=self.max_model_depth,
-                hash_content=self.hash_content,
-                tradeoff=self.tradeoff,
-                preference_id=self.preference_id,
-                tools=self.tools,
-                previous_session=previous_session,
-                timeout=timeout or self.timeout,
-                max_retries=max_retries or self.max_retries,
-                nd_api_url=self.nd_api_url,
-            )
-
-            if not best_llm:
-                LOGGER.warning(
-                    f"ND API error. Falling back to default provider={self.default_llm.provider}/{self.default_llm.model}."
-                )
-                best_llm = self.default_llm
-
-            if best_llm.system_prompt is not None:
-                messages = inject_system_prompt(
-                    messages, best_llm.system_prompt
-                )
-            if response_model is not None:
-                messages = _NDInvokerClient._inject_model_instruction(
-                    messages, response_model_parser
-                )
-
-            self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-            llm = self._llm_from_config(best_llm, callbacks=self.callbacks)
-            if self.tools:
-                llm = llm.bind_tools(self.tools)
-
-            if response_model is not None:
-                chain = llm | response_model_parser
-            else:
-                chain = llm
-
-            async for chunk in chain.astream(messages, **kwargs):
-                if response_model is None:
-                    yield chunk
-                else:
-                    partial_model = create_partial_model(response_model)
-                    yield partial_model(**chunk)
-
-        async def _async_invoke_with_latency_tracking(
-            self,
-            session_id: str,
-            chain: Any,
-            llm_config: LLMConfig,
-            input: Optional[Dict[str, Any]] = {},
-            is_default: bool = True,
-            **kwargs,
-        ):
-            if session_id in ("NO-SESSION-ID", "") and not is_default:
-                error_message = (
-                    "ND session_id is not valid for latency tracking."
-                    + "Please check the API response."
-                )
-                self.call_callbacks("on_api_error", error_message)
-                raise ApiError(error_message)
-
-            start_time = time.time()
-
-            result = await chain.ainvoke(input, **kwargs)
-
-            end_time = time.time()
-
-            tokens_completed = token_counter(
-                model=llm_config.model,
-                messages=[{"role": "assistant", "content": result.content}],
-            )
-            tokens_per_second = tokens_completed / (end_time - start_time)
-
-            report_latency(
-                session_id=session_id,
-                llm_config=llm_config,
-                tokens_per_second=tokens_per_second,
-                notdiamond_api_key=self.api_key,
-                nd_api_url=self.nd_api_url,
-                _user_agent=self.user_agent,
-            )
-            self.call_callbacks(
-                "on_latency_tracking",
-                session_id,
-                llm_config,
-                tokens_per_second,
-            )
-
-            return result
-
-        def _invoke_with_latency_tracking(
-            self,
-            session_id: str,
-            chain: Any,
-            llm_config: LLMConfig,
-            input: Optional[Dict[str, Any]] = {},
-            is_default: bool = True,
-            **kwargs,
-        ):
-            LOGGER.debug(f"Latency tracking enabled, session_id={session_id}")
-            if session_id in ("NO-SESSION-ID", "") and not is_default:
-                error_message = (
-                    "ND session_id is not valid for latency tracking."
-                    + "Please check the API response."
-                )
-                self.call_callbacks("on_api_error", error_message)
-                raise ApiError(error_message)
-
-            start_time = time.time()
-            result = chain.invoke(input, **kwargs)
-            end_time = time.time()
-
-            tokens_completed = token_counter(
-                model=llm_config.model,
-                messages=[{"role": "assistant", "content": result.content}],
-            )
-            tokens_per_second = tokens_completed / (end_time - start_time)
-
-            report_latency(
-                session_id=session_id,
-                llm_config=llm_config,
-                tokens_per_second=tokens_per_second,
-                notdiamond_api_key=self.api_key,
-                nd_api_url=self.nd_api_url,
-                _user_agent=self.user_agent,
-            )
-            self.call_callbacks(
-                "on_latency_tracking",
-                session_id,
-                llm_config,
-                tokens_per_second,
-            )
-
-            return result
-
-        @staticmethod
-        def _llm_from_config(
-            provider: LLMConfig,
-            callbacks: Optional[List] = None,
-        ) -> Any:
-            default_kwargs = {"max_retries": 5, "timeout": 120}
-            passed_kwargs = {**default_kwargs, **provider.kwargs}
-
-            if provider.provider == "openai":
-                ChatOpenAI = _module_check(
-                    "langchain_openai.chat_models",
-                    "ChatOpenAI",
-                    provider.provider,
-                )
-                if is_o1_model(provider):
-                    passed_kwargs["temperature"] = 1.0
-
-                return ChatOpenAI(
-                    openai_api_key=provider.api_key,
-                    model_name=provider.model,
-                    callbacks=callbacks,
-                    **passed_kwargs,
-                )
-            if provider.provider == "anthropic":
-                ChatAnthropic = _module_check(
-                    "langchain_anthropic", "ChatAnthropic", provider.provider
-                )
-                return ChatAnthropic(
-                    anthropic_api_key=provider.api_key,
-                    model=provider.model,
-                    callbacks=callbacks,
-                    **passed_kwargs,
-                )
-            if provider.provider == "google":
-                ChatGoogleGenerativeAI = _module_check(
-                    "langchain_google_genai",
-                    "ChatGoogleGenerativeAI",
-                    provider.provider,
-                )
-                return ChatGoogleGenerativeAI(
-                    google_api_key=provider.api_key,
-                    model=provider.model,
-                    convert_system_message_to_human=True,
-                    callbacks=callbacks,
-                    **passed_kwargs,
-                )
-            if provider.provider == "cohere":
-                ChatCohere = _module_check(
-                    "langchain_cohere.chat_models",
-                    "ChatCohere",
-                    provider.provider,
-                )
-                return ChatCohere(
-                    cohere_api_key=provider.api_key,
-                    model=provider.model,
-                    callbacks=callbacks,
-                    **passed_kwargs,
-                )
-            if provider.provider == "mistral":
-                ChatMistralAI = _module_check(
-                    "langchain_mistralai.chat_models",
-                    "ChatMistralAI",
-                    provider.provider,
-                )
-                return ChatMistralAI(
-                    mistral_api_key=provider.api_key,
-                    model=provider.model,
-                    callbacks=callbacks,
-                    **passed_kwargs,
-                )
-            if provider.provider == "togetherai":
-                provider_settings = settings.PROVIDERS.get(
-                    provider.provider, None
-                )
-                model_prefixes = provider_settings.get("model_prefix", None)
-                model_prefix = model_prefixes.get(provider.model, None)
-                del passed_kwargs["max_retries"]
-                del passed_kwargs["timeout"]
-
-                if model_prefix is not None:
-                    model = f"{model_prefix}/{provider.model}"
-                ChatTogether = _module_check(
-                    "langchain_together", "ChatTogether", provider.provider
-                )
-                return ChatTogether(
-                    together_api_key=provider.api_key,
-                    model=model,
-                    callbacks=callbacks,
-                    **passed_kwargs,
-                )
-            if provider.provider == "perplexity":
-                del passed_kwargs["max_retries"]
-                passed_kwargs["request_timeout"] = passed_kwargs["timeout"]
-                del passed_kwargs["timeout"]
-                ChatPerplexity = _module_check(
-                    "langchain_community.chat_models",
-                    "ChatPerplexity",
-                    provider.provider,
-                )
-                return ChatPerplexity(
-                    pplx_api_key=provider.api_key,
-                    model=provider.model,
-                    callbacks=callbacks,
-                    **passed_kwargs,
-                )
-            if provider.provider == "replicate":
-                provider_settings = settings.PROVIDERS.get(
-                    provider.provider, None
-                )
-                model_prefixes = provider_settings.get("model_prefix", None)
-                model_prefix = model_prefixes.get(provider.model, None)
-                passed_kwargs["request_timeout"] = passed_kwargs["timeout"]
-                del passed_kwargs["timeout"]
-
-                if model_prefix is not None:
-                    model = f"replicate/{model_prefix}/{provider.model}"
-                ChatLiteLLM = _module_check(
-                    "langchain_community.chat_models",
-                    "ChatLiteLLM",
-                    provider.provider,
-                )
-                return ChatLiteLLM(
-                    model=model,
-                    callbacks=callbacks,
-                    replicate_api_key=provider.api_key,
-                    **passed_kwargs,
-                )
-            raise ValueError(f"Unsupported provider: {provider.provider}")
-
-        def verify_against_response_model(self) -> bool:
-            """
-            Verify that the LLMs support response modeling.
-            """
-
-            for provider in self.llm_configs:
-                if provider.model not in settings.PROVIDERS[
-                    provider.provider
-                ].get("support_response_model", []):
-                    raise ApiError(
-                        f"{provider.provider}/{provider.model} does not support response modeling."
-                    )
-
-            return True
-
-    if import_target is _NDClientTarget.ROUTER:
-        return _NDRouterClient
-    return _NDInvokerClient
-
-
-_NDClient = _ndllm_factory()
-
-
-

-[docs]
-class NotDiamond(_NDClient):
-    api_key: str
-    """
-    API key required for making calls to NotDiamond.
-    You can get an API key via our dashboard: https://app.notdiamond.ai
-    If an API key is not set, it will check for NOTDIAMOND_API_KEY in .env file.
-    """
-
-    llm_configs: Optional[List[Union[LLMConfig, str]]]
-    """The list of LLMs that are available to route between."""
-
-    default: Union[LLMConfig, int, str]
-    """
-    Set a default LLM, so in case anything goes wrong in the flow,
-    as for example NotDiamond API call fails, your code won't break and you have
-    a fallback model. There are various ways to configure a default model:
-
-    - Integer, specifying the index of the default provider from the llm_configs list
-    - String, similar how you can specify llm_configs, of structure 'provider_name/model_name'
-    - LLMConfig, just directly specify the object of the provider
-
-    By default, we will set your first LLM in the list as the default.
-    """
-
-    max_model_depth: Optional[int]
-    """
-    If your top recommended model is down, specify up to which depth of routing you're willing to go.
-    If max_model_depth is not set, it defaults to the length of the llm_configs list.
-    If max_model_depth is set to 0, the init will fail.
-    If the value is larger than the llm_configs list length, we reset the value to len(llm_configs).
-    """
-
-    latency_tracking: bool
-    """
-    Tracking and sending latency of LLM call to NotDiamond server as feedback, so we can improve our router.
-    By default this is turned on, set it to False to turn off.
-    """
-
-    hash_content: bool
-    """
-    Hashing the content before being sent to the NotDiamond API.
-    By default this is False.
-    """
-
-    tradeoff: Optional[str]
-    """
-    [DEPRECATED] The tradeoff constructor parameter is deprecated and will be removed in a future version.
-    Please specify the tradeoff when using model_select or invocation methods.
-
-    Define tradeoff between "cost" and "latency" for the router to determine the best LLM for a given query.
-    If None is specified, then the router will not consider either cost or latency.
-
-    The supported values: "cost", "latency"
-
-    Defaults to None.
-    """
-
-    preference_id: Optional[str]
-    """The ID of the router preference that was configured via the Dashboard. Defaults to None."""
-
-    tools: Optional[Sequence[Union[Dict[str, Any], Callable]]]
-    """Bind tools to the LLM object. The tools will be passed to the LLM object when invoking it."""
-
-    nd_api_url: Optional[str]
-    """The URL of the NotDiamond API. Defaults to settings.NOTDIAMOND_API_URL."""
-
-    user_agent: Union[str, None]
-
-    max_retries: int
-    """The maximum number of retries to make when calling the Not Diamond API."""
-
-    timeout: float
-    """The timeout for the Not Diamond API call."""
-
-

-[docs]
-    class Config:
-        arbitrary_types_allowed = True

-
-
-    def __init__(
-        self,
-        nd_api_url: Optional[str] = settings.NOTDIAMOND_API_URL,
-        user_agent: Union[str, None] = settings.DEFAULT_USER_AGENT,
-        *args,
-        **kwargs,
-    ):
-        super().__init__(
-            nd_api_url=nd_api_url, user_agent=user_agent, *args, **kwargs
-        )
-        self.nd_api_url = nd_api_url
-
-        if kwargs.get("tradeoff") is not None:
-            warnings.warn(
-                "The tradeoff constructor parameter is deprecated and will be removed in a "
-                "future version. Please specify the tradeoff when using model_select or invocation methods.",
-                DeprecationWarning,
-                stacklevel=2,
-            )

-
-
-
-def _get_accepted_invoke_errors(provider: str) -> Tuple:
-    if provider == "google":
-        ChatGoogleGenerativeAIError = _module_check(
-            "langchain_google_genai.chat_models",
-            "ChatGoogleGenerativeAIError",
-            provider,
-        )
-        accepted_errors = (ChatGoogleGenerativeAIError, ValueError)
-    else:
-        accepted_errors = (ValueError,)
-    return accepted_errors
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/llms/config.html b/docs/_build/html/_modules/notdiamond/llms/config.html deleted file mode 100644 index 1d9e55d6..00000000 --- a/docs/_build/html/_modules/notdiamond/llms/config.html +++ /dev/null @@ -1,466 +0,0 @@ - - - - - - notdiamond.llms.config — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.llms.config

-import logging
-from typing import Optional
-
-from notdiamond import settings
-from notdiamond.exceptions import (
-    UnsupportedEmbeddingProvider,
-    UnsupportedLLMProvider,
-)
-
-POSSIBLE_PROVIDERS = list(settings.PROVIDERS.keys())
-POSSIBLE_MODELS = list(
-    model
-    for provider_values in settings.PROVIDERS.values()
-    for values in provider_values.values()
-    if isinstance(values, list)
-    for model in values
-)
-
-POSSIBLE_EMBEDDING_PROVIDERS = [
-    *list(settings.EMBEDDING_PROVIDERS.keys()),
-    "huggingface",
-]
-POSSIBLE_EMBEDDING_MODELS = list(
-    model
-    for provider_values in settings.EMBEDDING_PROVIDERS.values()
-    for values in provider_values.values()
-    if isinstance(values, list)
-    for model in values
-)
-
-LOGGER = logging.getLogger(__name__)
-LOGGER.setLevel(logging.INFO)
-
-
-

-[docs]
-class LLMConfig:
-    """
-    A NotDiamond LLM provider config (or LLMConfig) is represented by a combination of provider and model.
-    Provider refers to the company of the foundational model, such as openai, anthropic, google.
-    The model represents the model name as defined by the owner company, such as gpt-3.5-turbo
-    Beside this you can also specify the API key for each provider, specify extra arguments
-    that are also supported by Langchain (eg. temperature), and a system prmopt to be used
-    with the provider. If the provider is selected during routing, then the system prompt will
-    be used, replacing the one in the message array if there are any.
-
-    All supported providers and models can be found in our docs.
-
-    If the API key it's not specified, it will try to pick it up from an .env file before failing.
-    As example for OpenAI it will look for OPENAI_API_KEY.
-
-    Attributes:
-        provider (str): The name of the LLM provider (e.g., "openai", "anthropic"). Must be one of the
-                        predefined providers in `POSSIBLE_PROVIDERS`.
-        model (str): The name of the LLM model to use (e.g., "gpt-3.5-turbo").
-                        Must be one of the predefined models in `POSSIBLE_MODELS`.
-        system_prompt (Optional[str], optional): The system prompt to use for the provider. Defaults to None.
-        api_key (Optional[str], optional): The API key for accessing the LLM provider's services.
-                                            Defaults to None, in which case it tries to fetch from the settings.
-        openrouter_model (str): The OpenRouter model equivalent for this provider / model
-        **kwargs: Additional keyword arguments that might be necessary for specific providers or models.
-
-    Raises:
-        UnsupportedLLMProvider: If the `provider` or `model` specified is not supported.
-    """
-
-

-[docs]
-    def __init__(
-        self,
-        provider: str,
-        model: str,
-        is_custom: bool = False,
-        system_prompt: Optional[str] = None,
-        context_length: Optional[int] = None,
-        input_price: Optional[float] = None,
-        custom_input_price: Optional[float] = None,
-        output_price: Optional[float] = None,
-        custom_output_price: Optional[float] = None,
-        latency: Optional[float] = None,
-        custom_latency: Optional[float] = None,
-        api_key: Optional[str] = None,
-        **kwargs,
-    ):
-        """_summary_
-
-        Args:
-            provider (str): The name of the LLM provider (e.g., "openai", "anthropic").
-            model (str): The name of the LLM model to use (e.g., "gpt-3.5-turbo").
-            is_custom (bool): Whether this is a custom model. Defaults to False.
-            system_prompt (Optional[str], optional): The system prompt to use for the provider. Defaults to None.
-            context_length (Optional[int], optional): Custom context window length for the provider/model.
-            custom_input_price (Optional[float], optional): Custom input price (USD) per million tokens for this
-                provider/model; will default to public input price if available.
-            custom_output_price (Optional[float], optional): Custom output price (USD) per million tokens for this
-                provider/model; will default to public output price if available.
-            custom_latency (Optional[float], optional): Custom latency (time to first token) for provider/model.
-            api_key (Optional[str], optional): The API key for accessing the LLM provider's services.
-                                                Defaults to None.
-            **kwargs: Additional keyword arguments that might be necessary for specific providers or models.
-
-        Raises:
-            UnsupportedLLMProvider: If the `provider` or `model` specified is not supported.
-        """
-        if is_custom:
-            self._openrouter_model = None
-            self.api_key = api_key
-            self.default_input_price = custom_input_price or input_price
-            self.default_output_price = custom_output_price or output_price
-        else:
-            if provider not in POSSIBLE_PROVIDERS:
-                raise UnsupportedLLMProvider(
-                    f"Given LLM provider {provider} is not in the list of supported providers."
-                )
-            if model not in POSSIBLE_MODELS:
-                raise UnsupportedLLMProvider(
-                    f"Given LLM model {model} is not in the list of supported models."
-                )
-            self._openrouter_model = settings.PROVIDERS[provider][
-                "openrouter_identifier"
-            ].get(model, None)
-
-            self.api_key = (
-                api_key
-                if api_key is not None
-                else settings.PROVIDERS[provider]["api_key"]
-            )
-
-            self.default_input_price = settings.PROVIDERS[provider]["price"][
-                model
-            ]["input"]
-            self.default_output_price = settings.PROVIDERS[provider]["price"][
-                model
-            ]["output"]
-
-        self.provider = provider
-        self.model = model
-        self.system_prompt = system_prompt
-
-        self.is_custom = is_custom
-        self.context_length = context_length
-        self.input_price = custom_input_price or input_price
-        self.output_price = custom_output_price or output_price
-        self.latency = custom_latency or latency
-
-        self.kwargs = kwargs

-
-
-    def __str__(self) -> str:
-        return f"{self.provider}/{self.model}"
-
-    def __repr__(self) -> str:
-        return f"LLMConfig({self.provider}/{self.model})"
-
-    def __eq__(self, other):
-        if isinstance(other, LLMConfig):
-            return (
-                self.provider == other.provider and self.model == other.model
-            )
-        return False
-
-    def __hash__(self):
-        return hash(str(self))
-
-    @property
-    def openrouter_model(self):
-        if self._openrouter_model is None:
-            LOGGER.warning(
-                f"Configured model {str(self)} is not available via OpenRouter. Please try another model."
-            )
-        return self._openrouter_model
-
-

-[docs]
-    def prepare_for_request(self):
-        """
-        Converts the LLMConfig object to a dict in the format accepted by
-        the NotDiamond API.
-
-        Returns:
-            dict
-        """
-        return {
-            "provider": self.provider,
-            "model": self.model,
-            "is_custom": self.is_custom,
-            "context_length": self.context_length,
-            "input_price": self.input_price,
-            "output_price": self.output_price,
-            "latency": self.latency,
-        }

-
-
-

-[docs]
-    def set_api_key(self, api_key: str) -> "LLMConfig":
-        self.api_key = api_key
-
-        return self

-
-
-

-[docs]
-    @classmethod
-    def from_string(cls, llm_provider: str):
-        """
-        We allow our users to specify LLM providers for NotDiamond in the string format 'provider_name/model_name',
-        as example 'openai/gpt-3.5-turbo'. Underlying our workflows we want to ensure we use LLMConfig as
-        the base type, so this class method converts a string specification of an LLM provider into an
-        LLMConfig object.
-
-        Args:
-            llm_provider (str): this is the string definition of the LLM provider
-
-        Returns:
-            LLMConfig: initialized object with correct provider and model
-        """
-        split_items = llm_provider.split("/")
-        if len(split_items) not in [2, 3]:
-            raise ValueError(
-                f"Expected string of format 'provider/model' or 'prefix/provider/model' but got {llm_provider}"
-            )
-        elif len(split_items) == 3:
-            _, provider, model = split_items
-        else:
-            provider = split_items[0]
-            model = split_items[1]
-        return cls(provider=provider, model=model)

-

-
-
-
-

-[docs]
-class EmbeddingConfig:
-    """
-    A NotDiamond embedding provider config (or EmbeddingConfig) is represented by a combination of provider and model.
-    Provider refers to the company of the foundational model, such as openai, anthropic, google.
-    The model represents the model name as defined by the owner company, such as text-embedding-3-large
-    Beside this you can also specify the API key for each provider or extra arguments
-    that are also supported by Langchain.
-
-    All supported providers and models can be found in our docs.
-
-    If the API key is not specified, the Config will try to read the key from an .env file before failing.
-    For example, the Config will look for `OPENAI_API_KEY` to authenticate any OpenAI provider.
-
-    Attributes:
-        provider (str): The name of the LLM provider (e.g., "openai", "anthropic"). Must be one of the
-                        predefined providers in `POSSIBLE_EMBEDDING_PROVIDERS`.
-        model (str): The name of the LLM model to use (e.g., "gpt-3.5-turbo").
-                        Must be one of the predefined models in `POSSIBLE_MODELS`.
-        api_key (Optional[str], optional): The API key for accessing the LLM provider's services.
-                                            Defaults to None, in which case it tries to fetch from the environment.
-        **kwargs: Additional keyword arguments that might be necessary for specific providers or models.
-
-    Raises:
-        UnsupportedLLMProvider: If the `provider` or `model` specified is not supported.
-    """
-
-

-[docs]
-    def __init__(
-        self,
-        provider: str,
-        model: str,
-        api_key: Optional[str] = None,
-        **kwargs,
-    ):
-        """_summary_
-
-        Args:
-            provider (str): The name of the embedding provider (e.g., "openai", "anthropic").
-            model (str): The name of the embedding model to use (e.g., "text-embedding-3-large").
-            api_key (Optional[str], optional): The API key for accessing the embedding provider's services.
-                                                Defaults to None.
-            **kwargs: Additional keyword arguments that might be necessary for specific providers or models.
-
-        Raises:
-            UnsupportedEmbeddingProvider: If the `provider` or `model` specified is not supported.
-        """
-        if provider not in POSSIBLE_EMBEDDING_PROVIDERS:
-            raise UnsupportedEmbeddingProvider(
-                f"Given embedding provider {provider} is not in the list of supported providers."
-            )
-
-        if (
-            model not in POSSIBLE_EMBEDDING_MODELS
-            and provider != "huggingface"
-        ):
-            raise UnsupportedEmbeddingProvider(
-                f"Given embedding model {model} is not in the list of supported models."
-            )
-
-        self.api_key = (
-            api_key
-            if api_key is not None
-            else settings.PROVIDERS[provider]["api_key"]
-        )
-
-        self.provider = provider
-        self.model = model
-        self.kwargs = kwargs

-
-
-    def __str__(self) -> str:
-        return f"{self.provider}/{self.model}"
-
-    def __repr__(self) -> str:
-        return f"EmbeddingConfig({self.provider}/{self.model})"
-
-    def __eq__(self, other):
-        if isinstance(other, EmbeddingConfig):
-            return (
-                self.provider == other.provider and self.model == other.model
-            )
-        return False
-
-    def __hash__(self):
-        return hash(str(self))
-
-

-[docs]
-    def set_api_key(self, api_key: str) -> "EmbeddingConfig":
-        self.api_key = api_key
-
-        return self

-
-
-

-[docs]
-    @classmethod
-    def from_string(cls, llm_provider: str):
-        """
-        We allow our users to specify LLM providers for NotDiamond in the string format 'provider_name/model_name',
-        for example 'openai/gpt-3.5-turbo'. Our workflows expect LLMConfig as
-        the base type, so this class method converts a string specification of an LLM provider into an
-        LLMConfig object.
-
-        Args:
-            llm_provider (str): this is the string definition of the LLM provider
-
-        Returns:
-            LLMConfig: initialized object with correct provider and model
-        """
-        split_items = llm_provider.split("/")
-        if len(split_items) not in [2, 3]:
-            raise ValueError(
-                f"Expected string of format 'provider/model' or 'prefix/provider/model' but got {llm_provider}"
-            )
-        elif len(split_items) == 3:
-            _, provider, model = split_items
-        else:
-            provider = split_items[0]
-            model = split_items[1]
-        return cls(provider=provider, model=model)

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/llms/llm.html b/docs/_build/html/_modules/notdiamond/llms/llm.html deleted file mode 100644 index b5ee56e5..00000000 --- a/docs/_build/html/_modules/notdiamond/llms/llm.html +++ /dev/null @@ -1,1599 +0,0 @@ - - - - - - notdiamond.llms.llm — NotDiamond 0.2.7-beta - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.llms.llm

-"""NDLLM Class"""
-
-import time
-from typing import (
-    Any,
-    AsyncIterator,
-    Callable,
-    Dict,
-    Iterator,
-    List,
-    Optional,
-    Sequence,
-    Type,
-    Union,
-)
-
-from langchain.prompts import PromptTemplate
-from langchain_anthropic import ChatAnthropic
-from langchain_cohere.chat_models import ChatCohere
-from langchain_community.chat_models import ChatLiteLLM, ChatPerplexity
-from langchain_core.callbacks.base import BaseCallbackHandler
-from langchain_core.callbacks.manager import CallbackManagerForLLMRun
-from langchain_core.language_models.llms import LLM
-from langchain_core.messages import AIMessage, BaseMessage, BaseMessageChunk
-from langchain_core.output_parsers import JsonOutputParser
-from langchain_core.prompt_values import StringPromptValue
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_google_genai.chat_models import ChatGoogleGenerativeAIError
-from langchain_mistralai.chat_models import ChatMistralAI
-from langchain_openai import ChatOpenAI
-from langchain_together import Together
-from litellm import token_counter
-from pydantic import BaseModel
-from pydantic_partial import create_partial_model
-
-from notdiamond import settings
-from notdiamond.callbacks import NDLLMBaseCallbackHandler
-from notdiamond.exceptions import ApiError, MissingLLMProviders
-from notdiamond.llms.provider import NDLLMProvider
-from notdiamond.llms.request import amodel_select, model_select, report_latency
-from notdiamond.metrics.metric import NDMetric
-from notdiamond.prompts.prompt import NDChatPromptTemplate, NDPromptTemplate
-from notdiamond.types import NDApiKeyValidator
-
-
-

-[docs]
-class NDLLM(LLM):
-    """
-    Implementation of NDLLM class, the main class responsible for routing.
-    The class inherits from Langchain's LLM class. Starting reference is from here:
-    https://python.langchain.com/docs/modules/model_io/llms/custom_llm
-
-    It's mandatory to have an API key set. If the api_key is not explicitly specified,
-    it will check for NOTDIAMOND_API_KEY in the .env file.
-
-    Raises:
-        MissingLLMProviders: you must specify at least one LLM provider for the router to work
-        ApiError: error raised when the NotDiamond API call fails.
-                    Ensure to set a default LLM provider to not break the code.
-    """
-
-    api_key: str
-    """
-    API key required for making calls to NotDiamond.
-    You can get an API key via our dashboard: https://app.notdiamond.ai
-    If an API key is not set, it will check for NOTDIAMOND_API_KEY in .env file.
-    """
-
-    llm_providers: Optional[List[NDLLMProvider]]
-    """The list of LLM providers that are available to route between."""
-
-    default: Union[NDLLMProvider, int, str]
-    """
-    Set a default LLM provider, so in case anything goes wrong in the flow,
-    as for example NotDiamond API call fails, your code won't break and you have
-    a fallback model. There are various ways to configure a default model:
-
-    - Integer, specifying the index of the default provider from the llm_providers list
-    - String, similar how you can specify llm_providers, of structure 'provider_name/model_name'
-    - NDLLMProvider, just directly specify the object of the provider
-
-    By default, we will set your first LLM in the list as the default.
-    """
-
-    max_model_depth: Optional[int]
-    """
-    If your top recommended model is down, specify up to which depth of routing you're willing to go.
-    If max_model_depth is not set, it defaults to the length of the llm_providers list.
-    If max_model_depth is set to 0, the init will fail.
-    If the value is larger than the llm_providers list length, we reset the value to len(llm_providers).
-    """
-
-    latency_tracking: bool
-    """
-    Tracking and sending latency of LLM call to NotDiamond server as feedback, so we can improve our router.
-    By default this is turned on, set it to False to turn off.
-    """
-
-    hash_content: bool
-    """
-    Hashing the content before being sent to the NotDiamond API.
-    By default this is False.
-    """
-
-    tradeoff: Optional[str]
-    """
-    Define tradeoff between "cost" and "latency" for the router to determine the best LLM for a given query.
-    If None is specified, then the router will not consider either cost or latency.
-
-    The supported values: "cost", "latency"
-
-    Defaults to None.
-    """
-
-    preference_id: Optional[str]
-    """The ID of the router preference that was configured via the Dashboard. Defaults to None."""
-
-    tools: Optional[Sequence[Union[Dict[str, Any], Callable]]]
-    """Bind tools to the LLM object. The tools will be passed to the LLM object when invoking it."""
-
-    callbacks: Optional[
-        List[Union[BaseCallbackHandler, NDLLMBaseCallbackHandler]]
-    ]
-    """
-    Callback handler for the LLM object. It will be passed to the LLM object when invoking it.
-    Also has custom NDLLM callbacks:
-    - on_model_select
-    - on_latency_tracking
-    - on_api_error
-    """
-
-    def __init__(
-        self,
-        llm_providers: Optional[List[NDLLMProvider]] = None,
-        api_key: Optional[str] = None,
-        default: Union[NDLLMProvider, int, str] = 0,
-        max_model_depth: Optional[int] = None,
-        latency_tracking: bool = True,
-        hash_content: bool = False,
-        tradeoff: Optional[str] = None,
-        preference_id: Optional[str] = None,
-        callbacks: Optional[
-            List[Union[BaseCallbackHandler, NDLLMBaseCallbackHandler]]
-        ] = None,
-        **kwargs,
-    ) -> None:
-        if api_key is None:
-            api_key = settings.NOTDIAMOND_API_KEY
-        NDApiKeyValidator(api_key=api_key)
-
-        if llm_providers is not None:
-            llm_providers = self._parse_llm_providers_data(llm_providers)
-
-            if max_model_depth is None:
-                max_model_depth = len(llm_providers)
-
-            if max_model_depth > len(llm_providers):
-                print(
-                    "WARNING: max_model_depth cannot be bigger than the number of LLM providers."
-                )
-                max_model_depth = len(llm_providers)
-
-        if tradeoff is not None:
-            if tradeoff not in ["cost", "latency"]:
-                raise ValueError(
-                    "Invalid tradeoff. Accepted values: cost, latency."
-                )
-
-        super(NDLLM, self).__init__(
-            api_key=api_key,
-            llm_providers=llm_providers,
-            default=default,
-            max_model_depth=max_model_depth,
-            latency_tracking=latency_tracking,
-            hash_content=hash_content,
-            tradeoff=tradeoff,
-            preference_id=preference_id,
-            callbacks=callbacks,
-            **kwargs,
-        )
-
-    def __repr__(self) -> str:
-        class_name = self.__class__.__name__
-        address = hex(id(self))  # Gets the memory address of the object
-        return f"<{class_name} object at {address}>"
-
-    @property
-    def chat(self):
-        return self
-
-    @property
-    def completions(self):
-        return self
-
-    @property
-    def default_llm_provider(self) -> Union[NDLLMProvider, None]:
-        """
-        Return the default LLM provider that's set on the NDLLM class.
-        """
-        if isinstance(self.default, int):
-            return self.llm_providers[int(self.default)]
-        if isinstance(self.default, str):
-            if self.default.isdigit():
-                return self.llm_providers[int(self.default)]
-            return NDLLMProvider.from_string(self.default)
-        if isinstance(self.default, NDLLMProvider):
-            return self.default
-        return self.llm_providers[0]
-
-    @staticmethod
-    def _parse_llm_providers_data(llm_providers: list) -> List[NDLLMProvider]:
-        providers = []
-        for llm_provider in llm_providers:
-            if isinstance(llm_provider, NDLLMProvider):
-                providers.append(llm_provider)
-                continue
-            parsed_provider = NDLLMProvider.from_string(llm_provider)
-            providers.append(parsed_provider)
-        return providers
-
-    @property
-    def _llm_type(self) -> str:
-        return "NotDiamond LLM"
-
-    def _call(
-        self,
-        prompt: str,
-        stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
-    ) -> str:
-        if stop is not None:
-            raise ValueError("stop kwargs are not permitted.")
-        return "This function is deprecated for the latest LangChain version, use invoke instead"
-
-

-[docs]
-    def create(
-        self,
-        messages: List[Dict[str, str]],
-        model: Optional[List[NDLLMProvider]] = None,
-        default: Optional[Union[NDLLMProvider, int, str]] = None,
-        max_model_depth: Optional[int] = None,
-        latency_tracking: Optional[bool] = None,
-        hash_content: Optional[bool] = None,
-        tradeoff: Optional[str] = None,
-        preference_id: Optional[str] = None,
-        metric: NDMetric = NDMetric("accuracy"),
-        response_model: Optional[Type[BaseModel]] = None,
-        **kwargs,
-    ) -> tuple[Union[AIMessage, BaseModel], str, NDLLMProvider]:
-        """
-        Function call to invoke the LLM, with the same interface
-        as the OpenAI Python library.
-
-        Parameters:
-            messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                the messages OpenAI style.
-            model (Optional[List[NDLLMProvider]]): List of models to choose from.
-            default (Optional[Union[NDLLMProvider, int, str]]): Default LLM provider.
-            max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                of routing you're willing to go.
-            latency_tracking (Optional[bool]): Latency tracking flag.
-            hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-            tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                for the router to determine the best LLM for a given query.
-            preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                            Defaults to None.
-            **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-        Raises:
-            ApiError: when the NotDiamond API fails
-
-        Returns:
-            tuple[Union[AIMessage, BaseModel], str, NDLLMProvider]:
-                result: response type defined by Langchain, contains the response from the LLM.
-                or object of the response_model
-                str: session_id returned by the NotDiamond API
-                NDLLMProvider: the best LLM provider selected by the router
-        """
-
-        if model is not None:
-            llm_providers = self._parse_llm_providers_data(model)
-            self.llm_providers = llm_providers
-
-        self.validate_params(
-            default=default,
-            max_model_depth=max_model_depth,
-            latency_tracking=latency_tracking,
-            hash_content=hash_content,
-            tradeoff=tradeoff,
-            preference_id=preference_id,
-        )
-
-        return self.invoke(
-            messages=messages,
-            metric=metric,
-            response_model=response_model,
-            **kwargs,
-        )

-
-
-

-[docs]
-    async def acreate(
-        self,
-        messages: List[Dict[str, str]],
-        model: Optional[List[NDLLMProvider]] = None,
-        default: Optional[Union[NDLLMProvider, int, str]] = None,
-        max_model_depth: Optional[int] = None,
-        latency_tracking: Optional[bool] = None,
-        hash_content: Optional[bool] = None,
-        tradeoff: Optional[str] = None,
-        preference_id: Optional[str] = None,
-        metric: NDMetric = NDMetric("accuracy"),
-        response_model: Optional[Type[BaseModel]] = None,
-        **kwargs,
-    ) -> tuple[Union[AIMessage, BaseModel], str, NDLLMProvider]:
-        """
-        Async function call to invoke the LLM, with the same interface
-        as the OpenAI Python library.
-
-        Parameters:
-            messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                the messages OpenAI style.
-            model (Optional[List[NDLLMProvider]]): List of models to choose from.
-            default (Optional[Union[NDLLMProvider, int, str]]): Default LLM provider.
-            max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                of routing you're willing to go.
-            latency_tracking (Optional[bool]): Latency tracking flag.
-            hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-            tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                for the router to determine the best LLM for a given query.
-            preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                            Defaults to None.
-            **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-        Raises:
-            ApiError: when the NotDiamond API fails
-
-        Returns:
-            tuple[Union[AIMessage, BaseModel], str, NDLLMProvider]:
-                result: response type defined by Langchain, contains the response from the LLM.
-                or object of the response_model
-                str: session_id returned by the NotDiamond API
-                NDLLMProvider: the best LLM provider selected by the router
-        """
-        if model is not None and len(model) > 0:
-            llm_providers = self._parse_llm_providers_data(model)
-            self.llm_providers = llm_providers
-
-        self.validate_params(
-            default=default,
-            max_model_depth=max_model_depth,
-            latency_tracking=latency_tracking,
-            hash_content=hash_content,
-            tradeoff=tradeoff,
-            preference_id=preference_id,
-        )
-
-        result = await self.ainvoke(
-            messages=messages,
-            metric=metric,
-            response_model=response_model,
-            **kwargs,
-        )
-        return result

-
-
-

-[docs]
-    def invoke(
-        self,
-        prompt_template: Optional[
-            Union[
-                NDPromptTemplate,
-                PromptTemplate,
-                NDChatPromptTemplate,
-                ChatPromptTemplate,
-                str,
-            ]
-        ] = None,
-        messages: Optional[List[Dict[str, str]]] = None,
-        input: Optional[Dict[str, Any]] = None,
-        metric: NDMetric = NDMetric("accuracy"),
-        response_model: Optional[Type[BaseModel]] = None,
-        **kwargs,
-    ) -> tuple[Union[AIMessage, BaseModel], str, NDLLMProvider]:
-        """
-        Function to invoke the LLM. Behind the scenes what happens:
-        1. API call to NotDiamond backend to get the most suitable LLM for the given prompt
-        2. Invoke the returned LLM client side
-        3. Return the response
-
-        Parameters:
-            prompt_template (Optional(Union[ NDPromptTemplate, PromptTemplate, NDChatPromptTemplate, ChatPromptTemplate, str, ])):
-                the prompt template defined by the user. It also supports Langchain prompt template types.
-            messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                the messages OpenAI style.
-            input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                        the values for those variables. Defaults to None, assuming no variables.
-            metric (NDMetric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                            Defaults to NDMetric("accuracy").
-            response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the response into
-                                                              the given model. In which case result will a dict.
-            **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-        Raises:
-            ApiError: when the NotDiamond API fails
-
-        Returns:
-            tuple[Union[AIMessage, BaseModel], str, NDLLMProvider]:
-                result: response type defined by Langchain, contains the response from the LLM.
-                or object of the response_model
-                str: session_id returned by the NotDiamond API
-                NDLLMProvider: the best LLM provider selected by the router
-        """
-
-        # If response_model is present, we will parse the response into the given model
-        # doing this here so that if validation errors occur, we can raise them before making the API call
-        response_model_parser = None
-        if response_model is not None:
-            self.verify_against_response_model()
-            response_model_parser = JsonOutputParser(
-                pydantic_object=response_model
-            )
-
-        prompt_template = self._prepare_prompt_template(
-            prompt_template,
-            messages,
-            response_model_parser=response_model_parser,
-        )
-
-        if input is None:
-            input = {}
-
-        prompt_template.partial_variables = {
-            **prompt_template.partial_variables,
-            **input,
-        }
-
-        best_llm, session_id = model_select(
-            prompt_template=prompt_template,
-            llm_providers=self.llm_providers,
-            metric=metric,
-            notdiamond_api_key=self.api_key,
-            max_model_depth=self.max_model_depth,
-            hash_content=self.hash_content,
-            tradeoff=self.tradeoff,
-            preference_id=self.preference_id,
-            tools=self.tools,
-        )
-
-        is_default = False
-        if not best_llm:
-            best_llm = self.default_llm_provider
-            is_default = True
-
-            if best_llm is None:
-                error_message = (
-                    "ND couldn't find a suitable model to call."
-                    + "To avoid disruptions, we recommend setting a default fallback model or increasing max model depth."
-                )
-                self.call_callbacks("on_api_error", error_message)
-                raise ApiError(error_message)
-
-        if best_llm.system_prompt is not None:
-            prompt_template = prompt_template.inject_system_prompt(
-                best_llm.system_prompt
-            )
-
-        self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-        llm = self._llm_from_provider(best_llm, callbacks=self.callbacks)
-
-        if self.tools:
-            llm = llm.bind_tools(self.tools)
-
-        chain = prompt_template | llm
-
-        try:
-            if self.latency_tracking:
-                result = self._invoke_with_latency_tracking(
-                    session_id=session_id,
-                    chain=chain,
-                    llm_provider=best_llm,
-                    is_default=is_default,
-                    input=input,
-                    **kwargs,
-                )
-            else:
-                result = chain.invoke(input, **kwargs)
-        except (ChatGoogleGenerativeAIError, ValueError) as e:
-            if (
-                isinstance(prompt_template, NDChatPromptTemplate)
-                and best_llm.provider == "google"
-            ):
-                print(
-                    f"WARNING: Google model's chat messages are violating requirements with error {e}."
-                )
-                print(
-                    "If you see this message, means the NotDiamond API returned a Google model as the best option,"
-                    + "but the LLM call will fail. So we will automatically fall back to a non-Google model, if possible."
-                )
-
-                non_google_llm = next(
-                    (
-                        llm_provider
-                        for llm_provider in self.llm_providers
-                        if llm_provider.provider != "google"
-                    ),
-                    None,
-                )
-
-                if non_google_llm is not None:
-                    best_llm = non_google_llm
-                    llm = self._llm_from_provider(
-                        best_llm, callbacks=self.callbacks
-                    )
-                    chain = prompt_template | llm
-
-                    if self.latency_tracking:
-                        result = self._invoke_with_latency_tracking(
-                            session_id=session_id,
-                            chain=chain,
-                            llm_provider=best_llm,
-                            is_default=is_default,
-                            input=input,
-                            **kwargs,
-                        )
-                    else:
-                        result = chain.invoke(input, **kwargs)
-                else:
-                    raise e
-            else:
-                raise e
-
-        if isinstance(result, str):
-            result = AIMessage(content=result)
-
-        if response_model is not None:
-            parsed_dict = response_model_parser.parse(result.content)
-            result = response_model.parse_obj(parsed_dict)
-
-        return result, session_id, best_llm

-
-
-

-[docs]
-    async def ainvoke(
-        self,
-        prompt_template: Optional[
-            Union[
-                NDPromptTemplate,
-                PromptTemplate,
-                NDChatPromptTemplate,
-                ChatPromptTemplate,
-                str,
-            ]
-        ] = None,
-        messages: Optional[List[Dict[str, str]]] = None,
-        input: Optional[Dict[str, Any]] = None,
-        metric: NDMetric = NDMetric("accuracy"),
-        response_model: Optional[Type[BaseModel]] = None,
-        **kwargs,
-    ) -> tuple[Union[AIMessage, BaseModel], str, NDLLMProvider]:
-        """
-        Function to invoke the LLM. Behind the scenes what happens:
-        1. API call to NotDiamond backend to get the most suitable LLM for the given prompt
-        2. Invoke the returned LLM client side
-        3. Return the response
-
-        Parameters:
-            prompt_template (Optional(Union[ NDPromptTemplate, PromptTemplate, NDChatPromptTemplate, ChatPromptTemplate, str, ])):
-                the prompt template defined by the user. It also supports Langchain prompt template types.
-            messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                the messages OpenAI style.
-            input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                        the values for those variables. Defaults to None, assuming no variables.
-            metric (NDMetric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                            Defaults to NDMetric("accuracy").
-            response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the response into
-                                                              the given model. In which case result will a dict.
-            **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-        Raises:
-            ApiError: when the NotDiamond API fails
-
-        Returns:
-            tuple[Union[AIMessage, BaseModel], str, NDLLMProvider]:
-                result: response type defined by Langchain, contains the response from the LLM.
-                or object of the response_model
-                str: session_id returned by the NotDiamond API
-                NDLLMProvider: the best LLM provider selected by the router
-        """
-
-        response_model_parser = None
-        if response_model is not None:
-            self.verify_against_response_model()
-            response_model_parser = JsonOutputParser(
-                pydantic_object=response_model
-            )
-
-        prompt_template = self._prepare_prompt_template(
-            prompt_template,
-            messages,
-            response_model_parser=response_model_parser,
-        )
-
-        if input is None:
-            input = {}
-
-        prompt_template.partial_variables = {
-            **prompt_template.partial_variables,
-            **input,
-        }
-
-        best_llm, session_id = await amodel_select(
-            prompt_template=prompt_template,
-            llm_providers=self.llm_providers,
-            metric=metric,
-            notdiamond_api_key=self.api_key,
-            max_model_depth=self.max_model_depth,
-            hash_content=self.hash_content,
-            tradeoff=self.tradeoff,
-            preference_id=self.preference_id,
-            tools=self.tools,
-        )
-
-        is_default = False
-        if not best_llm:
-            best_llm = self.default_llm_provider
-            is_default = True
-
-            if best_llm is None:
-                error_message = (
-                    "ND couldn't find a suitable model to call."
-                    + "To avoid disruptions, we recommend setting a default fallback model or make max depth larger."
-                )
-                self.call_callbacks("on_api_error", error_message)
-                raise ApiError(error_message)
-
-        if best_llm.system_prompt is not None:
-            prompt_template = prompt_template.inject_system_prompt(
-                best_llm.system_prompt
-            )
-
-        self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-        llm = self._llm_from_provider(best_llm, callbacks=self.callbacks)
-
-        if self.tools:
-            llm = llm.bind_tools(self.tools)
-
-        chain = prompt_template | llm
-
-        try:
-            if self.latency_tracking:
-                result = await self._async_invoke_with_latency_tracking(
-                    session_id=session_id,
-                    chain=chain,
-                    llm_provider=best_llm,
-                    is_default=is_default,
-                    input=input,
-                    **kwargs,
-                )
-            else:
-                result = await chain.ainvoke(input, **kwargs)
-        except (ChatGoogleGenerativeAIError, ValueError) as e:
-            if (
-                isinstance(prompt_template, NDChatPromptTemplate)
-                and best_llm.provider == "google"
-            ):
-                print(
-                    f"WARNING: Google model's chat messages are violating requirements with error {e}."
-                )
-                print(
-                    "If you see this message, means the NotDiamond API returned a Google model as the best option,"
-                    + "but the LLM call will fail. So we will automatically fall back to a non-Google model, if possible."
-                )
-
-                non_google_llm = next(
-                    (
-                        llm_provider
-                        for llm_provider in self.llm_providers
-                        if llm_provider.provider != "google"
-                    ),
-                    None,
-                )
-
-                if non_google_llm is not None:
-                    best_llm = non_google_llm
-                    llm = self._llm_from_provider(
-                        best_llm, callbacks=self.callbacks
-                    )
-                    chain = prompt_template | llm
-
-                    if self.latency_tracking:
-                        result = (
-                            await self._async_invoke_with_latency_tracking(
-                                session_id=session_id,
-                                chain=chain,
-                                llm_provider=best_llm,
-                                is_default=is_default,
-                                input=input,
-                                **kwargs,
-                            )
-                        )
-                    else:
-                        result = await chain.ainvoke(input, **kwargs)
-                else:
-                    raise e
-            else:
-                raise e
-
-        if isinstance(result, str):
-            result = AIMessage(content=result)
-
-        if response_model is not None:
-            parsed_dict = response_model_parser.parse(result.content)
-            result = response_model.parse_obj(parsed_dict)
-
-        return result, session_id, best_llm

-
-
-

-[docs]
-    def stream(
-        self,
-        prompt_template: Optional[
-            Union[
-                NDPromptTemplate,
-                PromptTemplate,
-                NDChatPromptTemplate,
-                ChatPromptTemplate,
-                str,
-            ]
-        ] = None,
-        messages: Optional[List[Dict[str, str]]] = None,
-        input: Optional[Dict[str, Any]] = None,
-        metric: NDMetric = NDMetric("accuracy"),
-        response_model: Optional[Type[BaseModel]] = None,
-        **kwargs,
-    ) -> Iterator[Union[BaseMessageChunk, BaseModel]]:
-        """
-        This function calls the NotDiamond backend to fetch the most suitable model for the given prompt,
-        and calls the LLM client side to stream the response.
-
-        Parameters:
-            prompt_template (Optional(Union[ NDPromptTemplate, PromptTemplate, NDChatPromptTemplate, ChatPromptTemplate, str, ])):
-                the prompt template defined by the user. It also supports Langchain prompt template types.
-            messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                the messages OpenAI style.
-            input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                        the values for those variables. Defaults to None, assuming no variables.
-            metric (NDMetric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                            Defaults to NDMetric("accuracy").
-            response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the response into
-                                                              the given model. In which case result will a dict.
-            **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-        Raises:
-            ApiError: when the NotDiamond API fails
-
-        Yields:
-            Iterator[Union[BaseMessageChunk, BaseModel]]: returns the response in chunks.
-                If response_model is present, it will return the partial model object
-        """
-
-        response_model_parser = None
-        if response_model is not None:
-            self.verify_against_response_model()
-            response_model_parser = JsonOutputParser(
-                pydantic_object=response_model
-            )
-
-        prompt_template = self._prepare_prompt_template(
-            prompt_template=prompt_template,
-            messages=messages,
-            response_model_parser=response_model_parser,
-        )
-
-        if input is None:
-            input = {}
-
-        prompt_template.partial_variables = {
-            **prompt_template.partial_variables,
-            **input,
-        }
-
-        best_llm, session_id = model_select(
-            prompt_template=prompt_template,
-            llm_providers=self.llm_providers,
-            metric=metric,
-            notdiamond_api_key=self.api_key,
-            max_model_depth=self.max_model_depth,
-            hash_content=self.hash_content,
-            tradeoff=self.tradeoff,
-            preference_id=self.preference_id,
-            tools=self.tools,
-        )
-
-        if not best_llm:
-            best_llm = self.default_llm_provider
-
-            if best_llm is None:
-                error_message = (
-                    "ND couldn't find a suitable model to call."
-                    + "To avoid disruptions, we recommend setting a default fallback model or make max depth larger."
-                )
-                self.call_callbacks("on_api_error", error_message)
-                raise ApiError(error_message)
-
-        if best_llm.system_prompt is not None:
-            prompt_template = prompt_template.inject_system_prompt(
-                best_llm.system_prompt
-            )
-
-        self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-        llm = self._llm_from_provider(best_llm, callbacks=self.callbacks)
-        if self.tools:
-            llm = llm.bind_tools(self.tools)
-
-        if response_model is not None:
-            chain = llm | response_model_parser
-        else:
-            chain = llm
-
-        for chunk in chain.stream(prompt_template.format(), **kwargs):
-            if response_model is None:
-                yield chunk
-            else:
-                partial_model = create_partial_model(response_model)
-                yield partial_model(**chunk)

-
-
-

-[docs]
-    async def astream(
-        self,
-        prompt_template: Optional[
-            Union[
-                NDPromptTemplate,
-                PromptTemplate,
-                NDChatPromptTemplate,
-                ChatPromptTemplate,
-                str,
-            ]
-        ] = None,
-        messages: Optional[List[Dict[str, str]]] = None,
-        input: Optional[Dict[str, Any]] = None,
-        metric: NDMetric = NDMetric("accuracy"),
-        response_model: Optional[Type[BaseModel]] = None,
-        **kwargs,
-    ) -> AsyncIterator[Union[BaseMessageChunk, BaseModel]]:
-        """
-        This function calls the NotDiamond backend to fetch the most suitable model for the given prompt,
-        and calls the LLM client side to stream the response. The function is async, so it's suitable for async codebases.
-
-        Parameters:
-            prompt_template (Optional(Union[ NDPromptTemplate, PromptTemplate, NDChatPromptTemplate, ChatPromptTemplate, str, ])):
-                the prompt template defined by the user. It also supports Langchain prompt template types.
-            messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                the messages OpenAI style.
-            input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                        the values for those variables. Defaults to None, assuming no variables.
-            metric (NDMetric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                            Defaults to NDMetric("accuracy").
-            response_model (Optional[Type[BaseModel]], optional): If present, will use JsonOutputParser to parse the response into
-                                                              the given model. In which case result will a dict.
-            **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-        Raises:
-            ApiError: when the NotDiamond API fails
-
-        Yields:
-            AsyncIterator[Union[BaseMessageChunk, BaseModel]]: returns the response in chunks.
-                If response_model is present, it will return the partial model object
-        """
-
-        response_model_parser = None
-        if response_model is not None:
-            self.verify_against_response_model()
-            response_model_parser = JsonOutputParser(
-                pydantic_object=response_model
-            )
-
-        prompt_template = self._prepare_prompt_template(
-            prompt_template=prompt_template,
-            messages=messages,
-            response_model_parser=response_model_parser,
-        )
-        best_llm, session_id = await amodel_select(
-            prompt_template=prompt_template,
-            llm_providers=self.llm_providers,
-            metric=metric,
-            notdiamond_api_key=self.api_key,
-            max_model_depth=self.max_model_depth,
-            hash_content=self.hash_content,
-            tradeoff=self.tradeoff,
-            preference_id=self.preference_id,
-            tools=self.tools,
-        )
-
-        if input is None:
-            input = {}
-
-        prompt_template.partial_variables = {
-            **prompt_template.partial_variables,
-            **input,
-        }
-
-        if not best_llm:
-            best_llm = self.default_llm_provider
-
-            if best_llm is None:
-                error_message = (
-                    "ND couldn't find a suitable model to call."
-                    + "To avoid disruptions, we recommend setting a default fallback model or make max depth larger."
-                )
-                self.call_callbacks("on_api_error", error_message)
-                raise ApiError(error_message)
-
-        if best_llm.system_prompt is not None:
-            prompt_template = prompt_template.inject_system_prompt(
-                best_llm.system_prompt
-            )
-
-        self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-        llm = self._llm_from_provider(best_llm, callbacks=self.callbacks)
-        if self.tools:
-            llm = llm.bind_tools(self.tools)
-
-        if response_model is not None:
-            chain = llm | response_model_parser
-        else:
-            chain = llm
-
-        async for chunk in chain.astream(prompt_template.format(), **kwargs):
-            if response_model is None:
-                yield chunk
-            else:
-                partial_model = create_partial_model(response_model)
-                yield partial_model(**chunk)

-
-
-

-[docs]
-    async def amodel_select(
-        self,
-        messages: Optional[List[Dict[str, str]]] = None,
-        prompt_template: Optional[
-            Union[
-                NDPromptTemplate,
-                PromptTemplate,
-                NDChatPromptTemplate,
-                ChatPromptTemplate,
-                str,
-            ]
-        ] = None,
-        input: Optional[Dict[str, Any]] = None,
-        model: Optional[List[NDLLMProvider]] = None,
-        default: Optional[Union[NDLLMProvider, int, str]] = None,
-        max_model_depth: Optional[int] = None,
-        latency_tracking: Optional[bool] = None,
-        hash_content: Optional[bool] = None,
-        tradeoff: Optional[str] = None,
-        preference_id: Optional[str] = None,
-        metric: NDMetric = NDMetric("accuracy"),
-        **kwargs,
-    ) -> tuple[str, Optional[NDLLMProvider]]:
-        """
-        This function calls the NotDiamond backend to fetch the most suitable model for the given prompt,
-        and leaves the execution of the LLM call to the developer.
-        The function is async, so it's suitable for async codebases.
-
-        Parameters:
-            messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                the messages OpenAI style.
-            prompt_template (Union[ NDPromptTemplate, PromptTemplate, NDChatPromptTemplate, ChatPromptTemplate, str, ]):
-                the prompt template defined by the user. It also supports Langchain prompt template types.
-            input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                        the values for those variables. Defaults to None, assuming no variables.
-            model (Optional[List[NDLLMProvider]]): List of models to choose from.
-            default (Optional[Union[NDLLMProvider, int, str]]): Default LLM provider.
-            max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                of routing you're willing to go.
-            latency_tracking (Optional[bool]): Latency tracking flag.
-            hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-            tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                for the router to determine the best LLM for a given query.
-            preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                            Defaults to None.
-            metric (NDMetric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                            Defaults to NDMetric("accuracy").
-            **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-        Returns:
-            tuple[str, Optional[NDLLMProvider]]: returns the session_id and the chosen LLM provider
-        """
-        prompt_template = self._prepare_prompt_template(
-            prompt_template,
-            messages,
-        )
-
-        if input is None:
-            input = {}
-
-        prompt_template.partial_variables = {
-            **prompt_template.partial_variables,
-            **input,
-        }
-
-        if model is not None:
-            llm_providers = self._parse_llm_providers_data(model)
-            self.llm_providers = llm_providers
-
-        self.validate_params(
-            default=default,
-            max_model_depth=max_model_depth,
-            latency_tracking=latency_tracking,
-            hash_content=hash_content,
-            tradeoff=tradeoff,
-            preference_id=preference_id,
-        )
-
-        best_llm, session_id = await amodel_select(
-            prompt_template=prompt_template,
-            llm_providers=self.llm_providers,
-            metric=metric,
-            notdiamond_api_key=self.api_key,
-            max_model_depth=self.max_model_depth,
-            hash_content=self.hash_content,
-            tradeoff=self.tradeoff,
-            preference_id=self.preference_id,
-            tools=self.tools,
-        )
-
-        if not best_llm and self.default is not None:
-            print("ND API error. Falling back to default provider.")
-            best_llm = self.default_llm_provider
-        self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-        return session_id, best_llm

-
-
-

-[docs]
-    def model_select(
-        self,
-        messages: Optional[List[Dict[str, str]]] = None,
-        prompt_template: Optional[
-            Union[
-                NDPromptTemplate,
-                PromptTemplate,
-                NDChatPromptTemplate,
-                ChatPromptTemplate,
-                str,
-            ]
-        ] = None,
-        input: Optional[Dict[str, Any]] = None,
-        model: Optional[List[NDLLMProvider]] = None,
-        default: Optional[Union[NDLLMProvider, int, str]] = None,
-        max_model_depth: Optional[int] = None,
-        latency_tracking: Optional[bool] = None,
-        hash_content: Optional[bool] = None,
-        tradeoff: Optional[str] = None,
-        preference_id: Optional[str] = None,
-        metric: NDMetric = NDMetric("accuracy"),
-        **kwargs,
-    ) -> tuple[str, Optional[NDLLMProvider]]:
-        """
-        This function calls the NotDiamond backend to fetch the most suitable model for the given prompt,
-        and leaves the execution of the LLM call to the developer.
-
-        Parameters:
-            messages (Optional[List[Dict[str, str]], optional): Can be used instead of prompt_template to pass
-                the messages OpenAI style.
-            prompt_template (Union[ NDPromptTemplate, PromptTemplate, NDChatPromptTemplate, ChatPromptTemplate, str, ]):
-                the prompt template defined by the user. It also supports Langchain prompt template types.
-            input (Optional[Dict[str, Any]], optional): If the prompt_template contains variables, use input to specify
-                                                        the values for those variables. Defaults to None, assuming no variables.
-            model (Optional[List[NDLLMProvider]]): List of models to choose from.
-            default (Optional[Union[NDLLMProvider, int, str]]): Default LLM provider.
-            max_model_depth (Optional[int]): If your top recommended model is down, specify up to which depth
-                                                of routing you're willing to go.
-            latency_tracking (Optional[bool]): Latency tracking flag.
-            hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-            tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                                for the router to determine the best LLM for a given query.
-            preference_id (Optional[str]): The ID of the router preference that was configured via the Dashboard.
-                                            Defaults to None.
-            metric (NDMetric, optional): Metric used by NotDiamond router to choose the best LLM.
-                                            Defaults to NDMetric("accuracy").
-            **kwargs: Any other arguments that are supported by Langchain's invoke method, will be passed through.
-
-        Returns:
-            tuple[str, Optional[NDLLMProvider]]: returns the session_id and the chosen LLM provider
-        """
-        prompt_template = self._prepare_prompt_template(
-            prompt_template,
-            messages,
-        )
-
-        if input is None:
-            input = {}
-
-        prompt_template.partial_variables = {
-            **prompt_template.partial_variables,
-            **input,
-        }
-
-        if model is not None:
-            llm_providers = self._parse_llm_providers_data(model)
-            self.llm_providers = llm_providers
-
-        self.validate_params(
-            default=default,
-            max_model_depth=max_model_depth,
-            latency_tracking=latency_tracking,
-            hash_content=hash_content,
-            tradeoff=tradeoff,
-            preference_id=preference_id,
-        )
-
-        best_llm, session_id = model_select(
-            prompt_template=prompt_template,
-            llm_providers=self.llm_providers,
-            metric=metric,
-            notdiamond_api_key=self.api_key,
-            max_model_depth=self.max_model_depth,
-            hash_content=self.hash_content,
-            tradeoff=self.tradeoff,
-            preference_id=self.preference_id,
-            tools=self.tools,
-        )
-
-        if not best_llm and self.default is not None:
-            print("ND API error. Falling back to default provider.")
-            best_llm = self.default_llm_provider
-        self.call_callbacks("on_model_select", best_llm, best_llm.model)
-
-        return session_id, best_llm

-
-
-    async def _async_invoke_with_latency_tracking(
-        self,
-        session_id: str,
-        chain: Any,
-        llm_provider: NDLLMProvider,
-        input: Optional[Dict[str, Any]] = {},
-        is_default: bool = True,
-        **kwargs,
-    ):
-        if session_id in ("NO-SESSION-ID", "") and not is_default:
-            error_message = (
-                "ND session_id is not valid for latency tracking."
-                + "Please check the API response."
-            )
-            self.call_callbacks("on_api_error", error_message)
-            raise ApiError(error_message)
-
-        start_time = time.time()
-
-        result = await chain.ainvoke(input, **kwargs)
-
-        end_time = time.time()
-
-        if isinstance(result, str):
-            result = AIMessage(content=result)
-
-        tokens_completed = token_counter(
-            model=llm_provider.model,
-            messages=[{"role": "assistant", "content": result.content}],
-        )
-        tokens_per_second = tokens_completed / (end_time - start_time)
-
-        report_latency(
-            session_id=session_id,
-            llm_provider=llm_provider,
-            tokens_per_second=tokens_per_second,
-            notdiamond_api_key=self.api_key,
-        )
-        self.call_callbacks(
-            "on_latency_tracking", session_id, llm_provider, tokens_per_second
-        )
-
-        return result
-
-    def _invoke_with_latency_tracking(
-        self,
-        session_id: str,
-        chain: Any,
-        llm_provider: NDLLMProvider,
-        input: Optional[Dict[str, Any]] = {},
-        is_default: bool = True,
-        **kwargs,
-    ):
-        if session_id in ("NO-SESSION-ID", "") and not is_default:
-            error_message = (
-                "ND session_id is not valid for latency tracking."
-                + "Please check the API response."
-            )
-            self.call_callbacks("on_api_error", error_message)
-            raise ApiError(error_message)
-
-        start_time = time.time()
-        result = chain.invoke(input, **kwargs)
-        end_time = time.time()
-
-        if isinstance(result, str):
-            result = AIMessage(content=result)
-
-        tokens_completed = token_counter(
-            model=llm_provider.model,
-            messages=[{"role": "assistant", "content": result.content}],
-        )
-        tokens_per_second = tokens_completed / (end_time - start_time)
-
-        report_latency(
-            session_id=session_id,
-            llm_provider=llm_provider,
-            tokens_per_second=tokens_per_second,
-            notdiamond_api_key=self.api_key,
-        )
-        self.call_callbacks(
-            "on_latency_tracking", session_id, llm_provider, tokens_per_second
-        )
-
-        return result
-
-    @staticmethod
-    def _llm_from_provider(
-        provider: NDLLMProvider,
-        callbacks: Optional[
-            List[Union[BaseCallbackHandler, NDLLMBaseCallbackHandler]]
-        ],
-    ) -> Any:
-        default_kwargs = {"max_retries": 5, "timeout": 120}
-        passed_kwargs = {**default_kwargs, **provider.kwargs}
-
-        if provider.provider == "openai":
-            return ChatOpenAI(
-                openai_api_key=provider.api_key,
-                model_name=provider.model,
-                callbacks=callbacks,
-                **passed_kwargs,
-            )
-        if provider.provider == "anthropic":
-            return ChatAnthropic(
-                anthropic_api_key=provider.api_key,
-                model=provider.model,
-                callbacks=callbacks,
-                **passed_kwargs,
-            )
-        if provider.provider == "google":
-            return ChatGoogleGenerativeAI(
-                google_api_key=provider.api_key,
-                model=provider.model,
-                convert_system_message_to_human=True,
-                callbacks=callbacks,
-                **passed_kwargs,
-            )
-        if provider.provider == "cohere":
-            return ChatCohere(
-                cohere_api_key=provider.api_key,
-                model=provider.model,
-                callbacks=callbacks,
-                **passed_kwargs,
-            )
-        if provider.provider == "mistral":
-            return ChatMistralAI(
-                mistral_api_key=provider.api_key,
-                model=provider.model,
-                callbacks=callbacks,
-                **passed_kwargs,
-            )
-        if provider.provider == "togetherai":
-            provider_settings = settings.PROVIDERS.get(provider.provider, None)
-            model_prefixes = provider_settings.get("model_prefix", None)
-            model_prefix = model_prefixes.get(provider.model, None)
-            del passed_kwargs["max_retries"]
-            del passed_kwargs["timeout"]
-
-            if model_prefix is not None:
-                model = f"{model_prefix}/{provider.model}"
-            return Together(
-                together_api_key=provider.api_key,
-                model=model,
-                callbacks=callbacks,
-                **passed_kwargs,
-            )
-        if provider.provider == "perplexity":
-            del passed_kwargs["max_retries"]
-            passed_kwargs["request_timeout"] = passed_kwargs["timeout"]
-            del passed_kwargs["timeout"]
-            return ChatPerplexity(
-                pplx_api_key=provider.api_key,
-                model=provider.model,
-                callbacks=callbacks,
-                **passed_kwargs,
-            )
-        if provider.provider == "replicate":
-            provider_settings = settings.PROVIDERS.get(provider.provider, None)
-            model_prefixes = provider_settings.get("model_prefix", None)
-            model_prefix = model_prefixes.get(provider.model, None)
-            passed_kwargs["request_timeout"] = passed_kwargs["timeout"]
-            del passed_kwargs["timeout"]
-
-            if model_prefix is not None:
-                model = f"replicate/{model_prefix}/{provider.model}"
-            return ChatLiteLLM(
-                model=model,
-                callbacks=callbacks,
-                replicate_api_key=provider.api_key,
-                **passed_kwargs,
-            )
-        raise ValueError(f"Unsupported provider: {provider.provider}")
-
-    @staticmethod
-    def _prepare_prompt_template(
-        prompt_template, messages=None, response_model_parser=None
-    ) -> Union[NDPromptTemplate, NDChatPromptTemplate]:
-        resulting_prompt_template = None
-        if prompt_template is not None and messages is not None:
-            print(
-                "Warning: prompt_template value is overriding messages value. Set one of those values for optimal performance."
-            )
-        if prompt_template is not None:
-            if isinstance(prompt_template, NDPromptTemplate) or isinstance(
-                prompt_template, NDChatPromptTemplate
-            ):
-                resulting_prompt_template = prompt_template
-            elif isinstance(prompt_template, str):
-                resulting_prompt_template = NDPromptTemplate(
-                    template=prompt_template
-                )
-            elif isinstance(prompt_template, StringPromptValue):
-                resulting_prompt_template = NDChatPromptTemplate.from_messages(
-                    prompt_template.to_messages()
-                )
-            elif isinstance(prompt_template, PromptTemplate):
-                resulting_prompt_template = (
-                    NDPromptTemplate.from_langchain_prompt_template(
-                        prompt_template
-                    )
-                )
-            elif isinstance(prompt_template, ChatPromptTemplate):
-                resulting_prompt_template = (
-                    NDChatPromptTemplate.from_langchain_chat_prompt_template(
-                        prompt_template
-                    )
-                )
-            elif isinstance(prompt_template, list):
-                if all(isinstance(pt, BaseMessage) for pt in prompt_template):
-                    resulting_prompt_template = (
-                        NDChatPromptTemplate.from_messages(prompt_template)
-                    )
-            if resulting_prompt_template is None:
-                raise ValueError(
-                    f"Unsupported prompt_template type {type(prompt_template)}"
-                )
-        if messages is not None:
-            resulting_prompt_template = (
-                NDChatPromptTemplate.from_openai_messages(messages)
-            )
-
-        if resulting_prompt_template is None:
-            raise ValueError("prompt_template or messages must be specified.")
-
-        if response_model_parser is not None:
-            resulting_prompt_template = (
-                resulting_prompt_template.inject_model_instruction(
-                    response_model_parser
-                )
-            )
-
-        return resulting_prompt_template
-
-

-[docs]
-    def bind_tools(
-        self,
-        tools: Sequence[Union[Dict[str, Any], Callable]],
-    ) -> "NDLLM":
-        """
-        Bind tools to the LLM object. The tools will be passed to the LLM object when invoking it.
-        Results in the tools being available in the LLM object.
-        You can access the tool_calls in the result via `result.tool_calls`.
-        """
-
-        for provider in self.llm_providers:
-            if provider.model not in settings.PROVIDERS[provider.provider].get(
-                "support_tools", []
-            ):
-                raise ApiError(
-                    f"{provider.provider}/{provider.model} does not support function calling."
-                )
-        self.tools = tools
-
-        return self

-
-
-

-[docs]
-    def call_callbacks(self, function_name: str, *args, **kwargs) -> None:
-        """
-        Call all callbacks with a specific function name.
-        """
-
-        if self.callbacks is None:
-            return
-
-        for callback in self.callbacks:
-            if hasattr(callback, function_name):
-                getattr(callback, function_name)(*args, **kwargs)

-
-
-

-[docs]
-    def verify_against_response_model(self) -> bool:
-        """
-        Verify that the LLM providers support response modeling.
-        """
-
-        for provider in self.llm_providers:
-            if provider.model not in settings.PROVIDERS[provider.provider].get(
-                "support_response_model", []
-            ):
-                raise ApiError(
-                    f"{provider.provider}/{provider.model} does not support response modeling."
-                )
-
-        return True

-
-
-

-[docs]
-    def validate_params(
-        self,
-        default: Optional[Union[NDLLMProvider, int, str]] = None,
-        max_model_depth: Optional[int] = None,
-        latency_tracking: Optional[bool] = None,
-        hash_content: Optional[bool] = None,
-        tradeoff: Optional[str] = None,
-        preference_id: Optional[str] = None,
-    ):
-        if default is not None:
-            self.default = default
-
-        if max_model_depth is not None:
-            self.max_model_depth = max_model_depth
-
-        if self.llm_providers is None or len(self.llm_providers) == 0:
-            raise MissingLLMProviders(
-                "No LLM provider speficied. Specify at least one."
-            )
-
-        if self.max_model_depth is None:
-            self.max_model_depth = len(self.llm_providers)
-
-        if self.max_model_depth == 0:
-            raise ValueError("max_model_depth has to be bigger than 0.")
-
-        if self.max_model_depth > len(self.llm_providers):
-            print(
-                "WARNING: max_model_depth cannot be bigger than the number of LLM providers."
-            )
-            self.max_model_depth = len(self.llm_providers)
-
-        if tradeoff is not None:
-            if tradeoff not in ["cost", "latency"]:
-                raise ValueError(
-                    "Invalid tradeoff. Accepted values: cost, latency."
-                )
-            self.tradeoff = tradeoff
-
-        if preference_id is not None:
-            self.preference_id = preference_id
-
-        if latency_tracking is not None:
-            self.latency_tracking = latency_tracking
-
-        if hash_content is not None:
-            self.hash_content = hash_content

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jul 08, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/llms/provider.html b/docs/_build/html/_modules/notdiamond/llms/provider.html deleted file mode 100644 index 08837352..00000000 --- a/docs/_build/html/_modules/notdiamond/llms/provider.html +++ /dev/null @@ -1,253 +0,0 @@ - - - - - - notdiamond.llms.provider — NotDiamond 0.2.7-beta - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.llms.provider

-from typing import Optional
-
-from notdiamond import settings
-from notdiamond.exceptions import UnsupportedLLMProvider
-
-POSSIBLE_PROVIDERS = list(settings.PROVIDERS.keys())
-POSSIBLE_MODELS = list(
-    model
-    for provider_values in settings.PROVIDERS.values()
-    for values in provider_values.values()
-    if isinstance(values, list)
-    for model in values
-)
-
-
-

-[docs]
-class NDLLMProvider:
-    """
-    An NDLLM provider is represented by a combination of provider and model.
-    Provider refers to the company of the foundational model, such as openai, anthropic, google.
-    The model represents the model name as defined by the owner company, such as gpt-3.5-turbo
-    Beside this you can also specify the API key for each provider, specify extra arguments
-    that are also supported by Langchain (eg. temperature), and a system prmopt to be used
-    with the provider. If the provider is selected during routing, then the system prompt will
-    be used, replacing the one in the message array if there are any.
-
-    All supported providers and models can be found in our docs.
-
-    If the API key it's not specified, it will try to pick it up from an .env file before failing.
-    As example for OpenAI it will look for OPENAI_API_KEY.
-
-    Attributes:
-        provider (str): The name of the LLM provider (e.g., "openai", "anthropic"). Must be one of the
-                        predefined providers in `POSSIBLE_PROVIDERS`.
-        model (str): The name of the LLM model to use (e.g., "gpt-3.5-turbo").
-                        Must be one of the predefined models in `POSSIBLE_MODELS`.
-        system_prompt (Optional[str], optional): The system prompt to use for the provider. Defaults to None.
-        api_key (Optional[str], optional): The API key for accessing the LLM provider's services.
-                                            Defaults to None, in which case it tries to fetch from the settings.
-        openrouter_model (str): The OpenRouter model equivalent for this provider / model
-        **kwargs: Additional keyword arguments that might be necessary for specific providers or models.
-
-    Raises:
-        UnsupportedLLMProvider: If the `provider` or `model` specified is not supported.
-    """
-
-

-[docs]
-    def __init__(
-        self,
-        provider: str,
-        model: str,
-        system_prompt: Optional[str] = None,
-        api_key: Optional[str] = None,
-        **kwargs,
-    ):
-        """_summary_
-
-        Args:
-            provider (str): The name of the LLM provider (e.g., "openai", "anthropic").
-            model (str): The name of the LLM model to use (e.g., "gpt-3.5-turbo").
-            system_prompt (Optional[str], optional): The system prompt to use for the provider. Defaults to None.
-            api_key (Optional[str], optional): The API key for accessing the LLM provider's services.
-                                                Defaults to None.
-            **kwargs: Additional keyword arguments that might be necessary for specific providers or models.
-
-        Raises:
-            UnsupportedLLMProvider: If the `provider` or `model` specified is not supported.
-        """
-        if provider not in POSSIBLE_PROVIDERS:
-            raise UnsupportedLLMProvider(
-                f"Given LLM provider {provider} is not in the list of supported providers."
-            )
-        if model not in POSSIBLE_MODELS:
-            raise UnsupportedLLMProvider(
-                f"Given LLM model {model} is not in the list of supported models."
-            )
-
-        self.provider = provider
-        self.model = model
-        self.system_prompt = system_prompt
-        self._openrouter_model = settings.PROVIDERS[provider][
-            "openrouter_identifier"
-        ].get(model, None)
-        self.api_key = (
-            api_key
-            if api_key is not None
-            else settings.PROVIDERS[provider]["api_key"]
-        )
-        self.kwargs = kwargs

-
-
-    def __repr__(self) -> str:
-        return f"{self.provider}/{self.model}"
-
-    @property
-    def openrouter_model(self):
-        if self._openrouter_model is None:
-            print("WARNING: this model is not available via OpenRouter")
-        return self._openrouter_model
-
-

-[docs]
-    def prepare_for_request(self):
-        """
-        Converts the NDLLMProvider object to a dict in the format accepted by
-        the NotDiamond API.
-
-        Returns:
-            dict
-        """
-        return {"provider": self.provider, "model": self.model}

-
-
-

-[docs]
-    def set_api_key(self, api_key: str) -> "NDLLMProvider":
-        self.api_key = api_key
-
-        return self

-
-
-

-[docs]
-    @classmethod
-    def from_string(cls, llm_provider: str):
-        """
-        We allow our users to specify LLM providers for NDLLM in the string format 'provider_name/model_name',
-        as example 'openai/gpt-3.5-turbo'. Underlying our workflows we want to ensure we use NDLLMProvider as
-        the base type, so this class method converts a string specification of an LLM provider into an
-        NDLLMProvider object.
-
-        Args:
-            llm_provider (str): this is the string definition of the LLM provider
-
-        Returns:
-            NDLLMProvider: initialized object with correct provider and model
-        """
-        split_items = llm_provider.split("/")
-        provider = split_items[0]
-        model = split_items[1]
-        return cls(provider=provider, model=model)

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jul 08, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/llms/providers.html b/docs/_build/html/_modules/notdiamond/llms/providers.html deleted file mode 100644 index 68a59f5b..00000000 --- a/docs/_build/html/_modules/notdiamond/llms/providers.html +++ /dev/null @@ -1,342 +0,0 @@ - - - - - - notdiamond.llms.providers — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.llms.providers

-from enum import Enum
-
-from notdiamond.llms.config import LLMConfig
-
-
-

-[docs]
-class NDLLMProviders(Enum):
-    """
-    NDLLMProviders serves as a registry for the supported LLM models by NotDiamond.
-    It allows developers to easily specify available LLM providers for the router.
-
-    Attributes:
-        GPT_3_5_TURBO (NDLLMProvider): refers to 'gpt-3.5-turbo' model by OpenAI
-        GPT_3_5_TURBO_0125 (NDLLMProvider): refers to 'gpt-3.5-turbo-0125' model by OpenAI
-        GPT_4 (NDLLMProvider): refers to 'gpt-4' model by OpenAI
-        GPT_4_0613 (NDLLMProvider): refers to 'gpt-4-0613' model by OpenAI
-        GPT_4_1106_PREVIEW (NDLLMProvider): refers to 'gpt-4-1106-preview' model by OpenAI
-        GPT_4_TURBO (NDLLMProvider): refers to 'gpt-4-turbo' model by OpenAI
-        GPT_4_TURBO_PREVIEW (NDLLMProvider): refers to 'gpt-4-turbo-preview' model by OpenAI
-        GPT_4_TURBO_2024_04_09 (NDLLMProvider): refers to 'gpt-4-turbo-2024-04-09' model by OpenAI
-        GPT_4o_2024_05_13 (NDLLMProvider): refers to 'gpt-4o-2024-05-13' model by OpenAI
-        GPT_4o_2024_08_06 (NDLLMProvider): refers to 'gpt-4o-2024-08-06' model by OpenAI
-        GPT_4o (NDLLMProvider): refers to 'gpt-4o' model by OpenAI
-        GPT_4o_MINI_2024_07_18 (NDLLMProvider): refers to 'gpt-4o-mini-2024-07-18' model by OpenAI
-        GPT_4o_MINI (NDLLMProvider): refers to 'gpt-4o-mini' model by OpenAI
-        GPT_4_0125_PREVIEW (NDLLMProvider): refers to 'gpt-4-0125-preview' model by OpenAI
-        GPT_4_1 (NDLLMProvider): refers to 'gpt-4.1' model by OpenAI
-        GPT_4_1_2025_04_14 (NDLLMProvider): refers to 'gpt-4.1-2025-04-14' model by OpenAI
-        GPT_4_1_MINI (NDLLMProvider): refers to 'gpt-4.1-mini' model by OpenAI
-        GPT_4_1_MINI_2025_04_14 (NDLLMProvider): refers to 'gpt-4.1-mini-2025-04-14' model by OpenAI
-        GPT_4_1_NANO (NDLLMProvider): refers to 'gpt-4.1-nano' model by OpenAI
-        GPT_4_1_NANO_2025_04_14 (NDLLMProvider): refers to 'gpt-4.1-nano-2025-04-14' model by OpenAI
-        O1_PREVIEW (NDLLMProvider): refers to 'o1-preview' model by OpenAI
-        O1_PREVIEW_2024_09_12 (NDLLMProvider): refers to 'o1-preview-2024-09-12' model by OpenAI
-        O1_MINI (NDLLMProvider): refers to 'o1-mini' model by OpenAI
-        O1_MINI_2024_09_12 (NDLLMProvider): refers to 'o1-mini-2024-09-12' model by OpenAI
-
-        CLAUDE_2_1 (NDLLMProvider): refers to 'claude-2.1' model by Anthropic
-        CLAUDE_3_OPUS_20240229 (NDLLMProvider): refers to 'claude-3-opus-20240229' model by Anthropic
-        CLAUDE_3_SONNET_20240229 (NDLLMProvider): refers to 'claude-3-sonnet-20240229' model by Anthropic
-        CLAUDE_3_5_SONNET_20240620 (NDLLMProvider): refers to 'claude-3-5-sonnet-20240620' model by Anthropic
-        CLAUDE_3_7_SONNET_LATEST (NDLLMProvider): refers to 'claude-3-7-sonnet-latest' model by Anthropic
-        CLAUDE_3_7_SONNET_20250219 (NDLLMProvider): refers to 'claude-3-7-sonnet-20250219' model by Anthropic
-        CLAUDE_3_5_HAIKU_20241022 (NDLLMProvider): refers to 'claude-3-5-haiku-20241022' model by Anthropic
-        CLAUDE_3_HAIKU_20240307 (NDLLMProvider): refers to 'claude-3-haiku-20240307' model by Anthropic
-        CLAUDE_OPUS_4_20250514 (NDLLMProvider): refers to 'claude-opus-4-20250514' model by Anthropic
-        CLAUDE_SONNET_4_20250514 (NDLLMProvider): refers to 'claude-sonnet-4-20250514' model by Anthropic
-        CLAUDE_OPUS_4_0 (NDLLMProvider): refers to 'claude-opus-4-0' model by Anthropic
-        CLAUDE_SONNET_4_0 (NDLLMProvider): refers to 'claude-sonnet-4-0' model by Anthropic
-
-        GEMINI_PRO (NDLLMProvider): refers to 'gemini-pro' model by Google
-        GEMINI_1_PRO_LATEST (NDLLMProvider): refers to 'gemini-1.0-pro-latest' model by Google
-        GEMINI_15_PRO_LATEST (NDLLMProvider): refers to 'gemini-1.5-pro-latest' model by Google
-        GEMINI_15_PRO_EXP_0801 (NDLLMProvider): refers to 'gemini-1.5-pro-exp-0801' model by Google
-        GEMINI_15_FLASH_LATEST (NDLLMProvider): refers to 'gemini-1.5-flash-latest' model by Google
-        GEMINI_20_FLASH (NDLLMProvider): refers to 'gemini-20-flash' model by Google
-        GEMINI_20_FLASH_001 (NDLLMProvider): refers to 'gemini-20-flash-001' model by Google
-        GEMINI_25_FLASH (NDLLMProvider): refers to 'gemini-25-flash' model by Google
-        GEMINI_25_PRO (NDLLMProvider): refers to 'gemini-25-pro' model by Google
-
-        COMMAND_R (NDLLMProvider): refers to 'command-r' model by Cohere
-        COMMAND_R_PLUS (NDLLMProvider): refers to 'command-r-plus' model by Cohere
-
-        MISTRAL_LARGE_LATEST (NDLLMProvider): refers to 'mistral-large-latest' model by Mistral AI
-        MISTRAL_LARGE_2407 (NDLLMProvider): refers to 'mistral-large-2407' model by Mistral AI
-        MISTRAL_LARGE_2402 (NDLLMProvider): refers to 'mistral-large-2402' model by Mistral AI
-        MISTRAL_MEDIUM_LATEST (NDLLMProvider): refers to 'mistral-medium-latest' model by Mistral AI
-        MISTRAL_SMALL_LATEST (NDLLMProvider): refers to 'mistral-small-latest' model by Mistral AI
-        OPEN_MISTRAL_7B (NDLLMProvider): refers to 'open-mistral-7b' model by Mistral AI
-        OPEN_MIXTRAL_8X7B (NDLLMProvider): refers to 'open-mixtral-8x7b' model by Mistral AI
-        OPEN_MIXTRAL_8X22B (NDLLMProvider): refers to 'open-mixtral-8x22b' model by Mistral AI
-        OPEN_MISTRAL_NEMO (NDLLMProvider): refers to 'open-mistral-nemo' model by Mistral AI
-
-        TOGETHER_MISTRAL_7B_INSTRUCT_V0_2 (NDLLMProvider): refers to 'Mistral-7B-Instruct-v0.2' model served via TogetherAI
-        TOGETHER_MIXTRAL_8X7B_INSTRUCT_V0_1 (NDLLMProvider): refers to 'Mixtral-8x7B-Instruct-v0.1' model served via TogetherAI
-        TOGETHER_MIXTRAL_8X22B_INSTRUCT_V0_1 (NDLLMProvider): refers to 'Mixtral-8x22B-Instruct-v0.1' model served via TogetherAI
-        TOGETHER_LLAMA_3_70B_CHAT_HF (NDLLMProvider): refers to 'Llama-3-70b-chat-hf' model served via TogetherAI
-        TOGETHER_LLAMA_3_8B_CHAT_HF (NDLLMProvider): refers to 'Llama-3-8b-chat-hf' model served via TogetherAI
-        TOGETHER_QWEN2_72B_INSTRUCT (NDLLMProvider): refers to 'Qwen2-72B-Instruct' model served via TogetherAI
-        TOGETHER_LLAMA_3_1_8B_INSTRUCT_TURBO (NDLLMProvider): refers to 'Meta-Llama-3.1-8B-Instruct-Turbo'
-            model served via TogetherAI
-        TOGETHER_LLAMA_3_1_70B_INSTRUCT_TURBO (NDLLMProvider): refers to 'Meta-Llama-3.1-70B-Instruct-Turbo'
-            model served via TogetherAI
-        TOGETHER_LLAMA_3_1_405B_INSTRUCT_TURBO (NDLLMProvider): refers to 'Meta-Llama-3.1-405B-Instruct-Turbo'
-            model served via TogetherAI
-        TOGETHER_DEEPSEEK_R1 (NDLLMProvider): refers to 'DeepSeek-R1'
-            model served via TogetherAI
-
-        REPLICATE_MISTRAL_7B_INSTRUCT_V0_2 (NDLLMProvider): refers to "mistral-7b-instruct-v0.2" model served via Replicate
-        REPLICATE_MIXTRAL_8X7B_INSTRUCT_V0_1 (NDLLMProvider): refers to "mixtral-8x7b-instruct-v0.1" model served via Replicate
-        REPLICATE_META_LLAMA_3_70B_INSTRUCT (NDLLMProvider): refers to "meta-llama-3-70b-instruct" model served via Replicate
-        REPLICATE_META_LLAMA_3_8B_INSTRUCT (NDLLMProvider): refers to "meta-llama-3-8b-instruct" model served via Replicate
-        REPLICATE_META_LLAMA_3_1_405B_INSTRUCT (NDLLMProvider): refers to "meta-llama-3.1-405b-instruct"
-            model served via Replicate
-
-        SONAR (NDLLMProvider): refers to "sonar" model by Perplexity
-    """
-
-    GPT_3_5_TURBO = ("openai", "gpt-3.5-turbo")
-    GPT_3_5_TURBO_0125 = ("openai", "gpt-3.5-turbo-0125")
-    GPT_4 = ("openai", "gpt-4")
-    GPT_4_0613 = ("openai", "gpt-4-0613")
-    GPT_4_1106_PREVIEW = ("openai", "gpt-4-1106-preview")
-    GPT_4_TURBO = ("openai", "gpt-4-turbo")
-    GPT_4_TURBO_PREVIEW = ("openai", "gpt-4-turbo-preview")
-    GPT_4_TURBO_2024_04_09 = ("openai", "gpt-4-turbo-2024-04-09")
-    GPT_4o_2024_05_13 = ("openai", "gpt-4o-2024-05-13")
-    GPT_4o_2024_08_06 = ("openai", "gpt-4o-2024-08-06")
-    GPT_4o = ("openai", "gpt-4o")
-    GPT_4o_MINI_2024_07_18 = ("openai", "gpt-4o-mini-2024-07-18")
-    GPT_4o_MINI = ("openai", "gpt-4o-mini")
-    GPT_4_0125_PREVIEW = ("openai", "gpt-4-0125-preview")
-    GPT_4_1 = ("openai", "gpt-4.1")
-    GPT_4_1_2025_04_14 = ("openai", "gpt-4.1-2025-04-14")
-    GPT_4_1_MINI = ("openai", "gpt-4.1-mini")
-    GPT_4_1_MINI_2025_04_14 = ("openai", "gpt-4.1-mini-2025-04-14")
-    GPT_4_1_NANO = ("openai", "gpt-4.1-nano")
-    GPT_4_1_NANO_2025_04_14 = ("openai", "gpt-4.1-nano-2025-04-14")
-    O1_PREVIEW = ("openai", "o1-preview")
-    O1_PREVIEW_2024_09_12 = ("openai", "o1-preview-2024-09-12")
-    O1_MINI = ("openai", "o1-mini")
-    O1_MINI_2024_09_12 = ("openai", "o1-mini-2024-09-12")
-    CHATGPT_4o_LATEST = ("openai", "chatgpt-4o-latest")
-
-    CLAUDE_2_1 = ("anthropic", "claude-2.1")
-    CLAUDE_3_OPUS_20240229 = ("anthropic", "claude-3-opus-20240229")
-    CLAUDE_3_SONNET_20240229 = ("anthropic", "claude-3-sonnet-20240229")
-    CLAUDE_3_5_SONNET_20240620 = ("anthropic", "claude-3-5-sonnet-20240620")
-    CLAUDE_3_5_SONNET_20241022 = ("anthropic", "claude-3-5-sonnet-20241022")
-    CLAUDE_3_5_SONNET_LATEST = ("anthropic", "claude-3-5-sonnet-latest")
-    CLAUDE_3_7_SONNET_LATEST = ("anthropic", "claude-3-7-sonnet-latest")
-    CLAUDE_3_7_SONNET_20250219 = ("anthropic", "claude-3-7-sonnet-20250219")
-    CLAUDE_3_5_HAIKU_20241022 = ("anthropic", "claude-3-5-haiku-20241022")
-    CLAUDE_3_HAIKU_20240307 = ("anthropic", "claude-3-haiku-20240307")
-    CLAUDE_OPUS_4_20250514 = ("anthropic", "claude-opus-4-20250514")
-    CLAUDE_SONNET_4_20250514 = ("anthropic", "claude-sonnet-4-20250514")
-    CLAUDE_OPUS_4_0 = ("anthropic", "claude-opus-4-0")
-    CLAUDE_SONNET_4_0 = ("anthropic", "claude-sonnet-4-0")
-
-    GEMINI_PRO = ("google", "gemini-pro")
-    GEMINI_1_PRO_LATEST = ("google", "gemini-1.0-pro-latest")
-    GEMINI_15_PRO_LATEST = ("google", "gemini-1.5-pro-latest")
-    GEMINI_15_PRO_EXP_0801 = ("google", "gemini-1.5-pro-exp-0801")
-    GEMINI_15_FLASH_LATEST = ("google", "gemini-1.5-flash-latest")
-    GEMINI_20_FLASH = ("google", "gemini-2.0-flash")
-    GEMINI_20_FLASH_001 = ("google", "gemini-2.0-flash-001")
-    GEMINI_25_FLASH = ("google", "gemini-2.5-flash")
-    GEMINI_25_PRO = ("google", "gemini-2.5-pro")
-
-    COMMAND_R = ("cohere", "command-r")
-    COMMAND_R_PLUS = ("cohere", "command-r-plus")
-
-    MISTRAL_LARGE_LATEST = ("mistral", "mistral-large-latest")
-    MISTRAL_LARGE_2407 = ("mistral", "mistral-large-2407")
-    MISTRAL_LARGE_2402 = ("mistral", "mistral-large-2402")
-    MISTRAL_MEDIUM_LATEST = ("mistral", "mistral-medium-latest")
-    MISTRAL_SMALL_LATEST = ("mistral", "mistral-small-latest")
-    CODESTRAL_LATEST = ("mistral", "codestral-latest")
-    OPEN_MISTRAL_7B = ("mistral", "open-mistral-7b")
-    OPEN_MIXTRAL_8X7B = ("mistral", "open-mixtral-8x7b")
-    OPEN_MIXTRAL_8X22B = ("mistral", "open-mixtral-8x22b")
-    OPEN_MISTRAL_NEMO = ("mistral", "open-mistral-nemo")
-
-    TOGETHER_MISTRAL_7B_INSTRUCT_V0_2 = (
-        "togetherai",
-        "Mistral-7B-Instruct-v0.2",
-    )
-    TOGETHER_MIXTRAL_8X7B_INSTRUCT_V0_1 = (
-        "togetherai",
-        "Mixtral-8x7B-Instruct-v0.1",
-    )
-    TOGETHER_MIXTRAL_8X22B_INSTRUCT_V0_1 = (
-        "togetherai",
-        "Mixtral-8x22B-Instruct-v0.1",
-    )
-    TOGETHER_LLAMA_3_70B_CHAT_HF = ("togetherai", "Llama-3-70b-chat-hf")
-    TOGETHER_LLAMA_3_8B_CHAT_HF = ("togetherai", "Llama-3-8b-chat-hf")
-    TOGETHER_QWEN2_72B_INSTRUCT = ("togetherai", "Qwen2-72B-Instruct")
-    TOGETHER_LLAMA_3_1_8B_INSTRUCT_TURBO = (
-        "togetherai",
-        "Meta-Llama-3.1-8B-Instruct-Turbo",
-    )
-    TOGETHER_LLAMA_3_1_70B_INSTRUCT_TURBO = (
-        "togetherai",
-        "Meta-Llama-3.1-70B-Instruct-Turbo",
-    )
-    TOGETHER_LLAMA_3_1_405B_INSTRUCT_TURBO = (
-        "togetherai",
-        "Meta-Llama-3.1-405B-Instruct-Turbo",
-    )
-    TOGETHER_DEEPSEEK_R1 = ("togetherai", "DeepSeek-R1")
-
-    SONAR = (
-        "perplexity",
-        "sonar",
-    )
-
-    REPLICATE_MISTRAL_7B_INSTRUCT_V0_2 = (
-        "replicate",
-        "mistral-7b-instruct-v0.2",
-    )
-    REPLICATE_MIXTRAL_8X7B_INSTRUCT_V0_1 = (
-        "replicate",
-        "mixtral-8x7b-instruct-v0.1",
-    )
-    REPLICATE_META_LLAMA_3_70B_INSTRUCT = (
-        "replicate",
-        "meta-llama-3-70b-instruct",
-    )
-    REPLICATE_META_LLAMA_3_8B_INSTRUCT = (
-        "replicate",
-        "meta-llama-3-8b-instruct",
-    )
-    REPLICATE_META_LLAMA_3_1_405B_INSTRUCT = (
-        "replicate",
-        "meta-llama-3.1-405b-instruct",
-    )
-
-    def __new__(cls, provider, model):
-        return LLMConfig(provider=provider, model=model)

-
-
-
-

-[docs]
-def is_o1_model(llm: LLMConfig):
-    return llm in (
-        NDLLMProviders.O1_PREVIEW,
-        NDLLMProviders.O1_PREVIEW_2024_09_12,
-        NDLLMProviders.O1_MINI,
-        NDLLMProviders.O1_MINI_2024_09_12,
-    )

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/llms/request.html b/docs/_build/html/_modules/notdiamond/llms/request.html deleted file mode 100644 index 139902a5..00000000 --- a/docs/_build/html/_modules/notdiamond/llms/request.html +++ /dev/null @@ -1,483 +0,0 @@ - - - - - - notdiamond.llms.request — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.llms.request

-import json
-import logging
-from typing import Any, Callable, Dict, List, Optional, Sequence, Union
-
-import aiohttp
-import requests
-
-from notdiamond import settings
-from notdiamond._utils import _default_headers, convert_tool_to_openai_function
-from notdiamond.llms.config import LLMConfig
-from notdiamond.metrics.metric import Metric
-from notdiamond.types import ModelSelectRequestPayload
-
-LOGGER = logging.getLogger(__name__)
-LOGGER.setLevel(logging.INFO)
-
-
-

-[docs]
-def model_select_prepare(
-    messages: List[Dict[str, str]],
-    llm_configs: List[LLMConfig],
-    metric: Metric,
-    notdiamond_api_key: str,
-    max_model_depth: int,
-    hash_content: bool,
-    tradeoff: Optional[str] = None,
-    preference_id: Optional[str] = None,
-    tools: Optional[Sequence[Union[Dict[str, Any], Callable]]] = [],
-    previous_session: Optional[str] = None,
-    nd_api_url: Optional[str] = settings.NOTDIAMOND_API_URL,
-    _user_agent: str = settings.DEFAULT_USER_AGENT,
-):
-    """
-    This is the core method for the model_select endpoint.
-    It returns the best fitting LLM to call and a session ID that can be used for feedback.
-
-    Parameters:
-        messages (List[Dict[str, str]]): list of messages to be used for the LLM call
-        llm_configs (List[LLMConfig]): a list of available LLMs that the router can decide from
-        metric (Metric): metric based off which the router makes the decision. As of now only 'accuracy' supported.
-        notdiamond_api_key (str): API key generated via the NotDiamond dashboard.
-        max_model_depth (int): if your top recommended model is down, specify up to which depth of routing you're willing to go.
-        hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-        tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                            for the router to determine the best LLM for a given query.
-        preference_id (Optional[str], optional): The ID of the router preference that was configured via the Dashboard.
-                                                    Defaults to None.
-        previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-        async_mode (bool, optional): whether to run the request in async mode. Defaults to False.
-        nd_api_url (Optional[str], optional): The URL of the NotDiamond API. Defaults to None.
-
-    Returns:
-        tuple(url, payload, headers): returns data to be used for the API call of modelSelect
-    """
-    url = f"{nd_api_url}/v2/modelRouter/modelSelect"
-    tools_dict = get_tools_in_openai_format(tools)
-
-    payload: ModelSelectRequestPayload = {
-        "messages": messages,
-        "llm_providers": [
-            llm_provider.prepare_for_request() for llm_provider in llm_configs
-        ],
-        "metric": metric.metric,
-        "max_model_depth": max_model_depth,
-        "hash_content": hash_content,
-    }
-
-    if tools_dict:
-        payload["tools"] = tools_dict
-    if tradeoff is not None:
-        payload["tradeoff"] = tradeoff
-    if preference_id is not None:
-        payload["preference_id"] = preference_id
-    if previous_session is not None:
-        payload["previous_session"] = previous_session
-
-    headers = _default_headers(notdiamond_api_key, _user_agent)
-
-    return url, payload, headers

-
-
-
-

-[docs]
-def get_tools_in_openai_format(
-    tools: Optional[Sequence[Union[Dict[str, Any], Callable]]],
-):
-    """
-    This function converts the tools list into the format that OpenAI expects.
-    Does this by using langchains Model that automatically creates the dictionary on bind_tools
-
-    Parameters:
-        tools (Optional[Sequence[Union[Dict[str, Any], Callable]]]): list of tools to be converted
-
-    Returns:
-        dict: dictionary of tools in the format that OpenAI expects
-    """
-    if tools:
-        return [
-            {
-                "type": "function",
-                "function": convert_tool_to_openai_function(tool),
-            }
-            for tool in tools
-        ]
-
-    return None

-
-
-
-

-[docs]
-def model_select_parse(response_code, response_json, llm_configs):
-    if response_code == 200:
-        providers = response_json["providers"]
-        session_id = response_json["session_id"]
-
-        top_provider = providers[0]
-
-        best_llm = list(
-            filter(
-                lambda x: (x.model == top_provider["model"])
-                & (x.provider == top_provider["provider"]),
-                llm_configs,
-            )
-        )[0]
-        return best_llm, session_id
-
-    error_message = response_json["detail"]
-    LOGGER.error(f"API error: {response_code}. {error_message}")
-    return None, "NO-SESSION-ID"

-
-
-
-

-[docs]
-def model_select(
-    messages: List[Dict[str, str]],
-    llm_configs: List[LLMConfig],
-    metric: Metric,
-    notdiamond_api_key: str,
-    max_model_depth: int,
-    hash_content: bool,
-    tradeoff: Optional[str] = None,
-    preference_id: Optional[str] = None,
-    tools: Optional[Sequence[Union[Dict[str, Any], Callable]]] = [],
-    previous_session: Optional[str] = None,
-    timeout: Optional[Union[float, int]] = 60,
-    max_retries: Optional[int] = 3,
-    nd_api_url: Optional[str] = settings.NOTDIAMOND_API_URL,
-    _user_agent: str = settings.DEFAULT_USER_AGENT,
-):
-    """
-    This endpoint receives the prompt and routing settings, and makes a call to the NotDiamond API.
-    It returns the best fitting LLM to call and a session ID that can be used for feedback.
-
-    Parameters:
-        messages (List[Dict[str, str]]): list of messages to be used for the LLM call
-        llm_configs (List[LLMConfig]): a list of available LLMs that the router can decide from
-        metric (Metric): metric based off which the router makes the decision. As of now only 'accuracy' supported.
-        notdiamond_api_key (str): API key generated via the NotDiamond dashboard.
-        max_model_depth (int): if your top recommended model is down, specify up to which depth of routing you're willing to go.
-        hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-        tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                            for the router to determine the best LLM for a given query.
-        preference_id (Optional[str], optional): The ID of the router preference that was configured via the Dashboard.
-                                                    Defaults to None.
-        previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-        timeout (int, optional): timeout for the request. Defaults to 60.
-        max_retries (int, optional): The maximum number of retries to make when calling the Not Diamond API.
-                                    Defaults to 3.
-        nd_api_url (Optional[str], optional): The URL of the NotDiamond API. Defaults to None.
-    Returns:
-        tuple(LLMConfig, string): returns a tuple of the chosen LLMConfig to call and a session ID string.
-                                        In case of an error the LLM defaults to None and the session ID defaults
-                                        to 'NO-SESSION-ID'.
-    """
-    url, payload, headers = model_select_prepare(
-        messages=messages,
-        llm_configs=llm_configs,
-        metric=metric,
-        notdiamond_api_key=notdiamond_api_key,
-        max_model_depth=max_model_depth,
-        hash_content=hash_content,
-        tradeoff=tradeoff,
-        preference_id=preference_id,
-        tools=tools,
-        previous_session=previous_session,
-        nd_api_url=nd_api_url,
-        _user_agent=_user_agent,
-    )
-
-    for n_retry in range(1, max_retries + 1):
-        try:
-            response = requests.post(
-                url, data=json.dumps(payload), headers=headers, timeout=timeout
-            )
-            response_code = response.status_code
-            response_json = response.json()
-            break
-        except Exception as e:
-            LOGGER.error(
-                f"Retry {n_retry} of {max_retries}: API error: {e}",
-                exc_info=True,
-            )
-            if n_retry == max_retries:
-                return None, "NO-SESSION-ID"
-
-    best_llm, session_id = model_select_parse(
-        response_code, response_json, llm_configs
-    )
-
-    return best_llm, session_id

-
-
-
-

-[docs]
-async def amodel_select(
-    messages: List[Dict[str, str]],
-    llm_configs: List[LLMConfig],
-    metric: Metric,
-    notdiamond_api_key: str,
-    max_model_depth: int,
-    hash_content: bool,
-    tradeoff: Optional[str] = None,
-    preference_id: Optional[str] = None,
-    tools: Optional[Sequence[Union[Dict[str, Any], Callable]]] = [],
-    previous_session: Optional[str] = None,
-    timeout: Optional[Union[float, int]] = 60,
-    max_retries: Optional[int] = 3,
-    nd_api_url: Optional[str] = settings.NOTDIAMOND_API_URL,
-    _user_agent: str = settings.DEFAULT_USER_AGENT,
-):
-    """
-    This endpoint receives the prompt and routing settings, and makes a call to the NotDiamond API.
-    It returns the best fitting LLM to call and a session ID that can be used for feedback.
-
-    Parameters:
-        messages (List[Dict[str, str]]): list of messages to be used for the LLM call
-        llm_configs (List[LLMConfig]): a list of available LLMs that the router can decide from
-        metric (Metric): metric based off which the router makes the decision. As of now only 'accuracy' supported.
-        notdiamond_api_key (str): API key generated via the NotDiamond dashboard.
-        max_model_depth (int): if your top recommended model is down, specify up to which depth of routing you're willing to go.
-        hash_content (Optional[bool]): Flag for hashing content before sending to NotDiamond API.
-        tradeoff (Optional[str], optional): Define the "cost" or "latency" tradeoff
-                                            for the router to determine the best LLM for a given query.
-        preference_id (Optional[str], optional): The ID of the router preference that was configured via the Dashboard.
-                                                    Defaults to None.
-        previous_session (Optional[str], optional): The session ID of a previous session, allow you to link requests.
-        timeout (int, optional): timeout for the request. Defaults to 60.
-        max_retries (int, optional): The maximum number of retries to make when calling the Not Diamond API.
-        nd_api_url (Optional[str], optional): The URL of the NotDiamond API. Defaults to None.
-    Returns:
-        tuple(LLMConfig, string): returns a tuple of the chosen LLMConfig to call and a session ID string.
-                                        In case of an error the LLM defaults to None and the session ID defaults
-                                        to 'NO-SESSION-ID'.
-    """
-    url, payload, headers = model_select_prepare(
-        messages=messages,
-        llm_configs=llm_configs,
-        metric=metric,
-        notdiamond_api_key=notdiamond_api_key,
-        max_model_depth=max_model_depth,
-        hash_content=hash_content,
-        tradeoff=tradeoff,
-        preference_id=preference_id,
-        tools=tools,
-        previous_session=previous_session,
-        nd_api_url=nd_api_url,
-        _user_agent=_user_agent,
-    )
-
-    for n_retry in range(1, max_retries + 1):
-        try:
-            async with aiohttp.ClientSession() as session:
-                async with session.post(
-                    url,
-                    data=json.dumps(payload),
-                    headers=headers,
-                    timeout=timeout,
-                ) as response:
-                    response_code = response.status
-                    response_json = await response.json()
-                    break
-        except Exception as e:
-            LOGGER.error(
-                f"Retry {n_retry} of {max_retries}: API error: {e}",
-                exc_info=True,
-            )
-            if n_retry == max_retries:
-                return None, "NO-SESSION-ID"
-
-    best_llm, session_id = model_select_parse(
-        response_code, response_json, llm_configs
-    )
-
-    return best_llm, session_id

-
-
-
-

-[docs]
-def report_latency(
-    session_id: str,
-    llm_config: LLMConfig,
-    tokens_per_second: float,
-    notdiamond_api_key: str,
-    nd_api_url: Optional[str] = settings.NOTDIAMOND_API_URL,
-    _user_agent: str = settings.DEFAULT_USER_AGENT,
-):
-    """
-    This method makes an API call to the NotDiamond server to report the latency of an LLM call.
-    It helps fine-tune our model router and ensure we offer recommendations that meet your latency expectation.
-
-    This feature can be disabled on the NDLLM class level by setting `latency_tracking` to False.
-
-    Parameters:
-        session_id (str): the session ID that was returned from the `invoke` or `model_select` calls, so we know which
-                            router call your latency report refers to.
-        llm_provider (LLMConfig): specifying the LLM provider for which the latency is reported
-        tokens_per_second (float): latency of the model call calculated based on time elapsed, input tokens, and output tokens
-        notdiamond_api_key (str): NotDiamond API call used for authentication
-        nd_api_url (Optional[str], optional): The URL of the NotDiamond API. Defaults to None.
-    Returns:
-        int: status code of the API call, 200 if it's success
-
-    Raises:
-        ApiError: if the API call to the NotDiamond backend fails, this error is raised
-    """
-    url = f"{nd_api_url}/v2/report/metrics/latency"
-
-    payload = {
-        "session_id": session_id,
-        "provider": llm_config.prepare_for_request(),
-        "feedback": {"tokens_per_second": tokens_per_second},
-    }
-
-    headers = _default_headers(notdiamond_api_key, _user_agent)
-
-    try:
-        response = requests.post(url, json=payload, headers=headers)
-    except Exception as e:
-        LOGGER.error(
-            f"API error for report metrics latency: {e}", exc_info=True
-        )
-        return 500
-
-    return response.status_code

-
-
-
-

-[docs]
-def create_preference_id(
-    notdiamond_api_key: str,
-    name: Optional[str] = None,
-    nd_api_url: Optional[str] = settings.NOTDIAMOND_API_URL,
-    _user_agent: str = settings.DEFAULT_USER_AGENT,
-) -> str:
-    """
-    Create a preference id with an optional name. The preference name will appear in your
-    dashboard on Not Diamond.
-    """
-    url = f"{nd_api_url}/v2/preferences/userPreferenceCreate"
-    headers = _default_headers(notdiamond_api_key, _user_agent)
-    res = requests.post(url=url, headers=headers, json={"name": name})
-    if res.status_code == 200:
-        preference_id = res.json()["preference_id"]
-    else:
-        raise Exception(f"Error creating preference ID: {res.text}")
-
-    return preference_id

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/metrics/metric.html b/docs/_build/html/_modules/notdiamond/metrics/metric.html deleted file mode 100644 index ef71d364..00000000 --- a/docs/_build/html/_modules/notdiamond/metrics/metric.html +++ /dev/null @@ -1,157 +0,0 @@ - - - - - - notdiamond.metrics.metric — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.metrics.metric

-from typing import Optional
-
-from notdiamond import settings
-from notdiamond.exceptions import ApiError
-from notdiamond.llms.config import LLMConfig
-from notdiamond.metrics.request import feedback_request
-from notdiamond.types import NDApiKeyValidator
-
-
-

-[docs]
-class Metric:
-    def __init__(self, metric: Optional[str] = "accuracy"):
-        self.metric = metric
-
-    def __call__(self):
-        return self.metric
-
-

-[docs]
-    def feedback(
-        self,
-        session_id: str,
-        llm_config: LLMConfig,
-        value: int,
-        notdiamond_api_key: Optional[str] = None,
-        _user_agent: str = None,
-    ):
-        if notdiamond_api_key is None:
-            notdiamond_api_key = settings.NOTDIAMOND_API_KEY
-        NDApiKeyValidator(api_key=notdiamond_api_key)
-        if value not in [0, 1]:
-            raise ApiError("Invalid feedback value. It must be 0 or 1.")
-
-        return feedback_request(
-            session_id=session_id,
-            llm_config=llm_config,
-            feedback_payload=self.request_payload(value),
-            notdiamond_api_key=notdiamond_api_key,
-            _user_agent=_user_agent,
-        )

-
-
-

-[docs]
-    def request_payload(self, value: int):
-        return {self.metric: value}

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/metrics/request.html b/docs/_build/html/_modules/notdiamond/metrics/request.html deleted file mode 100644 index 08141cc7..00000000 --- a/docs/_build/html/_modules/notdiamond/metrics/request.html +++ /dev/null @@ -1,156 +0,0 @@ - - - - - - notdiamond.metrics.request — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.metrics.request

-import logging
-from typing import Dict
-
-import requests
-
-from notdiamond import settings
-from notdiamond._utils import _default_headers
-from notdiamond.exceptions import ApiError
-from notdiamond.llms.config import LLMConfig
-from notdiamond.types import FeedbackRequestPayload
-
-LOGGER = logging.getLogger(__name__)
-LOGGER.setLevel(logging.INFO)
-
-
-

-[docs]
-def feedback_request(
-    session_id: str,
-    llm_config: LLMConfig,
-    feedback_payload: Dict[str, int],
-    notdiamond_api_key: str,
-    nd_api_url: str = settings.NOTDIAMOND_API_URL,
-    _user_agent: str = settings.DEFAULT_USER_AGENT,
-) -> bool:
-    url = f"{nd_api_url}/v2/report/metrics/feedback"
-
-    payload: FeedbackRequestPayload = {
-        "session_id": session_id,
-        "provider": llm_config.prepare_for_request(),
-        "feedback": feedback_payload,
-    }
-
-    headers = _default_headers(notdiamond_api_key, _user_agent)
-
-    try:
-        response = requests.post(url, json=payload, headers=headers)
-    except Exception as e:
-        raise ApiError(f"ND API error for feedback: {e}")
-
-    if response.status_code != 200:
-        LOGGER.error(
-            f"ND API feedback error: failed to report feedback with status {response.status_code}. {response.text}"
-        )
-        return False
-
-    return True

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/prompts.html b/docs/_build/html/_modules/notdiamond/prompts.html deleted file mode 100644 index a7a9c4fb..00000000 --- a/docs/_build/html/_modules/notdiamond/prompts.html +++ /dev/null @@ -1,170 +0,0 @@ - - - - - - notdiamond.prompts — NotDiamond 0.3.38 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.prompts

-import logging
-import re
-from typing import Dict, List
-
-from notdiamond.llms.config import LLMConfig
-from notdiamond.llms.providers import is_o1_model
-
-LOGGER = logging.getLogger(__name__)
-LOGGER.setLevel(logging.INFO)
-
-
-

-[docs]
-def inject_system_prompt(
-    messages: List[Dict[str, str]], system_prompt: str
-) -> List[Dict[str, str]]:
-    """
-    Add a system prompt to an OpenAI-style message list. If a system prompt is already present, replace it.
-    """
-    new_messages = []
-    found = False
-    for msg in messages:
-        # t7: replace the first system prompt with the new one
-        if msg["role"] == "system" and not found:
-            new_messages.append({"role": "system", "content": system_prompt})
-            found = True
-        else:
-            new_messages.append(msg)
-    if not found:
-        new_messages.insert(0, {"role": "system", "content": system_prompt})
-    return new_messages

-
-
-
-

-[docs]
-def _curly_escape(text: str) -> str:
-    """
-    Escape curly braces in the text, but only for single occurrences of alphabetic characters.
-    This function will not escape double curly braces or non-alphabetic characters.
-    """
-    return re.sub(r"(?<!{){([a-zA-Z])}(?!})", r"{{\1}}", text)

-
-
-
-

-[docs]
-def o1_system_prompt_translate(
-    messages: List[Dict[str, str]], llm: LLMConfig
-) -> List[Dict[str, str]]:
-    if is_o1_model(llm):
-        translated_messages = []
-        for msg in messages:
-            if msg["role"] == "system":
-                translated_messages.append(
-                    {"role": "user", "content": msg["content"]}
-                )
-            else:
-                translated_messages.append(msg)
-        return translated_messages
-    return messages

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Feb 18, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/prompts/hash.html b/docs/_build/html/_modules/notdiamond/prompts/hash.html deleted file mode 100644 index 4de284b2..00000000 --- a/docs/_build/html/_modules/notdiamond/prompts/hash.html +++ /dev/null @@ -1,119 +0,0 @@ - - - - - - notdiamond.prompts.hash — NotDiamond 0.2.2-beta - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.prompts.hash

-import ppdeep
-
-
-

-[docs]
-def nd_hash(s: str) -> str:
-    """
-    Source of library from: https://github.com/elceef/ppdeep
-    """
-    return ppdeep.hash(s)

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 05, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/prompts/prompt.html b/docs/_build/html/_modules/notdiamond/prompts/prompt.html deleted file mode 100644 index a5d931f1..00000000 --- a/docs/_build/html/_modules/notdiamond/prompts/prompt.html +++ /dev/null @@ -1,350 +0,0 @@ - - - - - - notdiamond.prompts.prompt — NotDiamond 0.2.7-beta - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.prompts.prompt

-from typing import Any, Dict, List, Optional, Union
-
-from langchain.prompts import PromptTemplate
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
-from langchain_core.output_parsers import JsonOutputParser
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.prompts.string import get_template_variables
-
-
-

-[docs]
-class NDPromptTemplate(PromptTemplate):
-    """Custom implementation of NDPromptTemplate
-    Starting reference is from here:
-    https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.prompt.PromptTemplate.html
-    """
-
-    def __init__(
-        self,
-        template: str,
-        input_variables: Optional[List[str]] = None,
-        partial_variables: Optional[Dict[str, Any]] = {},
-    ):
-        if input_variables is None:
-            input_variables = get_template_variables(template, "f-string")
-
-        if partial_variables:
-            input_variables = []
-
-        super(NDPromptTemplate, self).__init__(
-            template=template,
-            input_variables=input_variables,
-            partial_variables=partial_variables,
-        )
-
-

-[docs]
-    @classmethod
-    def from_langchain_prompt_template(cls, prompt_template: PromptTemplate):
-        return cls(
-            template=prompt_template.template,
-            input_variables=prompt_template.input_variables,
-            partial_variables=prompt_template.partial_variables,
-        )

-
-
-

-[docs]
-    def format(self, **kwargs: Any) -> str:
-        """Format the prompt template with the given variables and convert it to NDPromptTemplate."""
-        return super(NDPromptTemplate, self).format(**kwargs)

-
-
-

-[docs]
-    def optimize(self):
-        print("Not yet implemented!")

-
-
-

-[docs]
-    def prepare_for_request(self):
-        return [{"role": "user", "content": self.format()}]

-
-
-

-[docs]
-    def inject_system_prompt(self, system_prompt: str):
-        self.template = system_prompt
-        return self

-
-
-

-[docs]
-    def inject_model_instruction(self, parser: JsonOutputParser):
-        format_instructions = parser.get_format_instructions()
-        format_instructions = format_instructions.replace("{", "{{").replace(
-            "}", "}}"
-        )
-        self.template = format_instructions + "\n" + self.template
-
-        return self

-

-
-
-
-

-[docs]
-class NDChatPromptTemplate(ChatPromptTemplate):
-    """
-    Starting reference is from
-    here:https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html
-    """
-
-    def __init__(
-        self,
-        messages: Optional[List] = None,
-        input_variables: Optional[List[str]] = None,
-        partial_variables: [str, Any] = dict,
-    ):
-        if messages is None:
-            messages = []
-        if partial_variables:
-            input_variables = []
-
-        super().__init__(
-            messages=messages,
-            input_variables=input_variables,
-            partial_variables=partial_variables,
-        )
-
-    @property
-    def template(self):
-        message = """
-        SYSTEM: {system_prompt}
-        CONTEXT: {context_prompt}
-        QUERY: {user_query}
-        """
-        return message
-
-

-[docs]
-    @classmethod
-    def from_langchain_chat_prompt_template(
-        cls, chat_prompt_template: ChatPromptTemplate
-    ):
-        return cls(
-            messages=chat_prompt_template.messages,
-            input_variables=chat_prompt_template.input_variables,
-            partial_variables=chat_prompt_template.partial_variables,
-        )

-
-
-

-[docs]
-    @classmethod
-    def from_openai_messages(cls, messages: List[Dict[str, str]]):
-        transformed_messages = []
-        for message in messages:
-            if message["role"] == "system":
-                transformed_messages.append(SystemMessage(message["content"]))
-            elif message["role"] == "assistant":
-                transformed_messages.append(AIMessage(message["content"]))
-            elif message["role"] == "user":
-                transformed_messages.append(HumanMessage(message["content"]))
-            else:
-                raise ValueError(f"Unsupported role: {message['role']}")
-        return cls(
-            messages=transformed_messages,
-            input_variables=None,
-            partial_variables={},
-        )

-
-
-

-[docs]
-    def format(self, **kwargs: Any) -> str:
-        """Format the prompt template with the given variables. and converts it to NDChatPromptTemplate."""
-        return super(NDChatPromptTemplate, self).format(**kwargs)

-
-
-

-[docs]
-    def get_last_human_message(self, formated_messages: List) -> str:
-        for message in reversed(formated_messages):
-            if isinstance(message, HumanMessage):
-                return message.content
-
-        raise ValueError("No human message found in the list of messages.")

-
-
-

-[docs]
-    def get_role_of_message(
-        self, message: Union[AIMessage, HumanMessage, SystemMessage]
-    ) -> str:
-        if isinstance(message, SystemMessage):
-            return "system"
-        if isinstance(message, AIMessage):
-            return "assistant"
-        if isinstance(message, HumanMessage):
-            return "user"
-        raise ValueError(f"Unsupported message type: {type(message)}")

-
-
-

-[docs]
-    def prepare_for_request(self):
-        formated_messages = self.format_messages(**self.partial_variables)
-        messages = []
-        for message in formated_messages:
-            if (
-                isinstance(message, SystemMessage)
-                or isinstance(message, AIMessage)
-                or isinstance(message, HumanMessage)
-            ):
-                messages.append(
-                    {
-                        "role": self.get_role_of_message(message),
-                        "content": message.content,
-                    }
-                )
-
-        return messages

-
-
-

-[docs]
-    def inject_system_prompt(self, system_prompt: str):
-        messages = self.prepare_for_request()
-        new_messages = []
-        found = False
-        for msg in messages:
-            # t7: replace the first system prompt with the new one
-            if msg["role"] == "system" and not found:
-                new_messages.append(
-                    {"role": "system", "content": system_prompt}
-                )
-                found = True
-            else:
-                new_messages.append(msg)
-        if not found:
-            new_messages.insert(
-                0, {"role": "system", "content": system_prompt}
-            )
-        return self.from_openai_messages(new_messages)

-
-
-

-[docs]
-    def inject_model_instruction(self, parser: JsonOutputParser):
-        format_instructions = parser.get_format_instructions()
-        format_instructions = format_instructions.replace("{", "{{").replace(
-            "}", "}}"
-        )
-        self.messages[0].content = (
-            format_instructions + "\n" + self.messages[0].content
-        )
-
-        return self

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jul 08, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/toolkit/custom_router.html b/docs/_build/html/_modules/notdiamond/toolkit/custom_router.html deleted file mode 100644 index b495b080..00000000 --- a/docs/_build/html/_modules/notdiamond/toolkit/custom_router.html +++ /dev/null @@ -1,572 +0,0 @@ - - - - - - notdiamond.toolkit.custom_router — NotDiamond 0.4.4 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Module code
    • -
  • notdiamond.toolkit.custom_router
    • -
  • -
    • -
-
-
-
-
- -

Source code for notdiamond.toolkit.custom_router

-import json
-import tempfile
-import time
-from collections import OrderedDict
-from typing import Dict, List, Optional, Tuple, Union
-
-import numpy as np
-import pandas as pd
-import requests
-from tqdm import tqdm
-
-from notdiamond.exceptions import ApiError
-from notdiamond.llms.client import NotDiamond
-from notdiamond.llms.config import LLMConfig
-from notdiamond.settings import NOTDIAMOND_API_KEY, NOTDIAMOND_API_URL, VERSION
-from notdiamond.types import NDApiKeyValidator
-from notdiamond._utils import token_counter
-
-
-

-[docs]
-class CustomRouter:
-    """
-    Implementation of CustomRouter class, used to train custom routers using custom datasets.
-
-    Attributes:
-        language (str): The language of the dataset in lowercase. Defaults to "english".
-        maximize (bool): Whether higher score is better. Defaults to true.
-        api_key (Optional[str], optional): The NotDiamond API key. If not specified, will try to
-            find it in the environment variable NOTDIAMOND_API_KEY.
-    """
-
-    def __init__(
-        self,
-        language: str = "english",
-        maximize: bool = True,
-        api_key: Optional[str] = None,
-    ):
-        if api_key is None:
-            api_key = NOTDIAMOND_API_KEY
-        NDApiKeyValidator(api_key=api_key)
-
-        self.api_key = api_key
-        self.language = language
-        self.maximize = maximize
-
-    def _request_train_router(
-        self,
-        prompt_column: str,
-        dataset_file: str,
-        llm_configs: List[LLMConfig],
-        preference_id: Optional[str],
-        nd_api_url: str,
-    ) -> str:
-        url = f"{nd_api_url}/v2/pzn/trainCustomRouter"
-
-        files = {"dataset_file": open(dataset_file, "rb")}
-
-        payload = {
-            "language": self.language,
-            "llm_providers": json.dumps(
-                [provider.prepare_for_request() for provider in llm_configs]
-            ),
-            "prompt_column": prompt_column,
-            "maximize": self.maximize,
-            "preference_id": preference_id,
-        }
-
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "User-Agent": f"Python-SDK/{VERSION}",
-        }
-
-        response = requests.post(
-            url=url, headers=headers, data=payload, files=files
-        )
-        if response.status_code != 200:
-            raise ApiError(
-                f"ND backend error status code: {response.status_code}, {response.text}"
-            )
-
-        preference_id = response.json()["preference_id"]
-        return preference_id
-
-    def _prepare_joint_dataset(
-        self,
-        dataset: Dict[Union[str, LLMConfig], pd.DataFrame],
-        prompt_column: str,
-        response_column: str,
-        score_column: str,
-    ) -> Tuple[pd.DataFrame, List[LLMConfig]]:
-        a_provider = list(dataset.keys())[0]
-        prompts = dataset[a_provider].get(prompt_column, None)
-        if prompts is None:
-            raise ValueError(f"Prompt column {prompt_column} not found in df.")
-        prompts = prompts.to_list()
-
-        llm_configs = []
-        joint_dataset = {prompt_column: prompts}
-        for provider, df in dataset.items():
-            llm_configs.append(provider)
-
-            responses = df.get(response_column, None)
-            if responses is None:
-                raise ValueError(
-                    f"Response column {response_column} not found in df."
-                )
-            responses = responses.to_list()
-            joint_dataset[f"{str(provider)}/response"] = responses
-
-            scores = df.get(score_column, None)
-            if scores is None:
-                raise ValueError(
-                    f"Score column {score_column} not found in df."
-                )
-            scores = scores.to_list()
-            joint_dataset[f"{str(provider)}/score"] = scores
-
-        joint_df = pd.DataFrame(joint_dataset)
-
-        llm_configs = NotDiamond._parse_llm_configs_data(llm_configs)
-        return joint_df, llm_configs
-
-

-[docs]
-    def fit(
-        self,
-        dataset: Dict[Union[str, LLMConfig], pd.DataFrame],
-        prompt_column: str,
-        response_column: str,
-        score_column: str,
-        preference_id: Optional[str] = None,
-        nd_api_url: Optional[str] = NOTDIAMOND_API_URL,
-    ) -> str:
-        """
-        Method to train a custom router using provided dataset.
-
-        Parameters:
-            dataset (Dict[str, pandas.DataFrame]): The dataset to train a custom router.
-                Each key in the dictionary should be in the form of <provider>/<model>.
-            prompt_column (str): The column name in each DataFrame corresponding
-                to the prompts used to evaluate the LLM.
-            response_column (str): The column name in each DataFrame corresponding
-                to the response given by the LLM for a given prompt.
-            score_column (str): The column name in each DataFrame corresponding
-                to the score given to the response from the LLM.
-            preference_id (Optional[str], optional): If specified, the custom router
-                associated with the preference_id will be updated with the provided dataset.
-            nd_api_url (Optional[str], optional): The URL of the NotDiamond API. Defaults to prod.
-
-        Raises:
-            ApiError: When the NotDiamond API fails
-            ValueError: When parsing the provided dataset fails
-            UnsupportedLLMProvider: When a provider specified in the dataset is not supported.
-
-        Returns:
-            str:
-                preference_id: the preference_id associated with the custom router.
-                    Use this preference_id in your routing calls to use the custom router.
-        """
-
-        joint_df, llm_configs = self._prepare_joint_dataset(
-            dataset, prompt_column, response_column, score_column
-        )
-
-        with tempfile.NamedTemporaryFile(suffix=".csv") as joint_csv:
-            joint_df.to_csv(joint_csv.name, index=False)
-            preference_id = self._request_train_router(
-                prompt_column,
-                joint_csv.name,
-                llm_configs,
-                preference_id,
-                nd_api_url,
-            )
-
-        return preference_id

-
-
-    def _get_latency(self, llm_config: LLMConfig, prompt: str) -> float:
-        llm = NotDiamond._llm_from_config(llm_config)
-        start_time = time.time()
-        _ = llm.invoke([("human", prompt)])
-        end_time = time.time()
-        return (end_time - start_time) * 1000  # ms
-
-    def _get_cost(
-        self, llm_config: LLMConfig, prompt: str, response: str
-    ) -> float:
-        n_input_tokens = token_counter(model="gpt-4o", text=prompt)
-        n_output_tokens = token_counter(model="gpt-4o", text=response)
-        input_price = (
-            llm_config.default_input_price
-            if llm_config.input_price is None
-            else llm_config.input_price
-        )
-        output_price = (
-            llm_config.default_output_price
-            if llm_config.output_price is None
-            else llm_config.output_price
-        )
-        return (
-            n_input_tokens * input_price + n_output_tokens * output_price
-        ) / 1e6
-
-    def _eval_custom_router(
-        self,
-        client: NotDiamond,
-        llm_configs: List[LLMConfig],
-        joint_df: pd.DataFrame,
-        prompt_column: str,
-        include_latency: bool,
-    ) -> Tuple[pd.DataFrame, pd.DataFrame]:
-        eval_results = OrderedDict()
-        eval_results[prompt_column] = []
-        eval_results["session_id"] = []
-        eval_results["notdiamond/score"] = []
-        eval_results["notdiamond/cost"] = []
-        eval_results["notdiamond/response"] = []
-        eval_results["notdiamond/recommended_provider"] = []
-
-        if include_latency:
-            eval_results["notdiamond/latency"] = []
-
-        for provider in llm_configs:
-            provider_score_column = (
-                f"{provider.provider}/{provider.model}/score"
-            )
-            eval_results[provider_score_column] = []
-
-            provider_response_column = (
-                f"{provider.provider}/{provider.model}/response"
-            )
-            eval_results[provider_response_column] = []
-
-            provider_cost_column = f"{provider.provider}/{provider.model}/cost"
-            eval_results[provider_cost_column] = []
-
-            if include_latency:
-                provider_latency_column = (
-                    f"{provider.provider}/{provider.model}/latency"
-                )
-                eval_results[provider_latency_column] = []
-
-        for _, row in tqdm(joint_df.iterrows(), total=len(joint_df)):
-            prompt = row[prompt_column]
-            eval_results[prompt_column].append(prompt)
-
-            session_id, nd_provider = client.chat.completions.model_select(
-                messages=[{"role": "user", "content": prompt}], timeout=60
-            )
-            if nd_provider is None:
-                continue
-
-            eval_results["session_id"].append(session_id)
-
-            provider_matched = False
-            for provider in llm_configs:
-                provider_score = row[
-                    f"{provider.provider}/{provider.model}/score"
-                ]
-                eval_results[
-                    f"{provider.provider}/{provider.model}/score"
-                ].append(provider_score)
-
-                provider_response = row[
-                    f"{provider.provider}/{provider.model}/response"
-                ]
-                eval_results[
-                    f"{provider.provider}/{provider.model}/response"
-                ].append(provider_response)
-
-                provider_cost = self._get_cost(
-                    provider, prompt, provider_response
-                )
-                eval_results[
-                    f"{provider.provider}/{provider.model}/cost"
-                ].append(provider_cost)
-
-                if include_latency:
-                    provider_latency = self._get_latency(provider, prompt)
-                    eval_results[
-                        f"{provider.provider}/{provider.model}/latency"
-                    ].append(provider_latency)
-
-                if (
-                    not provider_matched
-                    and provider.provider == nd_provider.provider
-                    and provider.model == nd_provider.model
-                ):
-                    provider_matched = True
-                    eval_results["notdiamond/score"].append(provider_score)
-                    eval_results["notdiamond/cost"].append(provider_cost)
-                    eval_results["notdiamond/response"].append(
-                        provider_response
-                    )
-                    eval_results["notdiamond/recommended_provider"].append(
-                        f"{nd_provider.provider}/{nd_provider.model}"
-                    )
-                    if include_latency:
-                        eval_results["notdiamond/latency"].append(
-                            provider_latency
-                        )
-
-            if not provider_matched:
-                raise ValueError(
-                    f"""
-                    Custom router returned {nd_provider.provider}/{nd_provider.model}
-                    which is not in the set of models in the test dataset
-                    """
-                )
-
-        eval_results_df = pd.DataFrame(eval_results)
-
-        eval_stats = OrderedDict()
-        best_average_provider = None
-        best_average_score = -(2 * int(self.maximize) - 1) * np.inf
-
-        nd_average_score = eval_results_df["notdiamond/score"].mean()
-        eval_stats["Not Diamond Average Score"] = [nd_average_score]
-
-        nd_average_cost = eval_results_df["notdiamond/cost"].mean()
-        eval_stats["Not Diamond Average Cost"] = [nd_average_cost]
-
-        if include_latency:
-            nd_average_latency = eval_results_df["notdiamond/latency"].mean()
-            eval_stats["Not Diamond Average Latency"] = [nd_average_latency]
-
-        for provider in llm_configs:
-            provider_avg_score = eval_results_df[
-                f"{provider.provider}/{provider.model}/score"
-            ].mean()
-            eval_stats[f"{provider.provider}/{provider.model}/avg_score"] = [
-                provider_avg_score
-            ]
-
-            provider_avg_cost = eval_results_df[
-                f"{provider.provider}/{provider.model}/cost"
-            ].mean()
-            eval_stats[f"{provider.provider}/{provider.model}/avg_cost"] = [
-                provider_avg_cost
-            ]
-
-            if include_latency:
-                provider_avg_latency = eval_results_df[
-                    f"{provider.provider}/{provider.model}/latency"
-                ].mean()
-                eval_stats[
-                    f"{provider.provider}/{provider.model}/avg_latency"
-                ] = [provider_avg_latency]
-
-            if self.maximize:
-                if provider_avg_score > best_average_score:
-                    best_average_score = provider_avg_score
-                    best_average_cost = provider_avg_cost
-                    best_average_provider = (
-                        f"{provider.provider}/{provider.model}"
-                    )
-                    if include_latency:
-                        best_average_latency = provider_avg_latency
-            else:
-                if provider_avg_score < best_average_score:
-                    best_average_score = provider_avg_score
-                    best_average_cost = provider_avg_cost
-                    best_average_provider = (
-                        f"{provider.provider}/{provider.model}"
-                    )
-                    if include_latency:
-                        best_average_latency = provider_avg_latency
-
-        eval_stats["Best Average Provider"] = [best_average_provider]
-        eval_stats["Best Provider Average Score"] = [best_average_score]
-        eval_stats["Best Provider Average Cost"] = [best_average_cost]
-
-        if include_latency:
-            eval_stats["Best Provider Average Latency"] = [
-                best_average_latency
-            ]
-
-            first_columns = [
-                "Best Average Provider",
-                "Best Provider Average Score",
-                "Best Provider Average Cost",
-                "Best Provider Average Latency",
-                "Not Diamond Average Score",
-                "Not Diamond Average Cost",
-                "Not Diamond Average Latency",
-            ]
-        else:
-            first_columns = [
-                "Best Average Provider",
-                "Best Provider Average Score",
-                "Best Provider Average Cost",
-                "Not Diamond Average Score",
-                "Not Diamond Average Cost",
-            ]
-
-        column_order = first_columns + [
-            col for col in eval_stats.keys() if col not in first_columns
-        ]
-        ordered_eval_stats = OrderedDict()
-        for col in column_order:
-            ordered_eval_stats[col] = eval_stats[col]
-
-        eval_stats_df = pd.DataFrame(ordered_eval_stats)
-        return eval_results_df, eval_stats_df
-
-

-[docs]
-    def eval(
-        self,
-        dataset: Dict[Union[str, LLMConfig], pd.DataFrame],
-        prompt_column: str,
-        response_column: str,
-        score_column: str,
-        preference_id: str,
-        include_latency: bool = False,
-    ) -> Tuple[pd.DataFrame, pd.DataFrame]:
-        """
-        Method to evaluate a custom router using provided dataset.
-
-        Parameters:
-            dataset (Dict[str, pandas.DataFrame]): The dataset to train a custom router.
-                Each key in the dictionary should be in the form of <provider>/<model>.
-            prompt_column (str): The column name in each DataFrame corresponding
-                to the prompts used to evaluate the LLM.
-            response_column (str): The column name in each DataFrame corresponding
-                to the response given by the LLM for a given prompt.
-            score_column (str): The column name in each DataFrame corresponding
-                to the score given to the response from the LLM.
-            preference_id (str): The preference_id associated with the custom router
-                returned from .fit().
-
-        Raises:
-            ApiError: When the NotDiamond API fails
-            ValueError: When parsing the provided dataset fails
-            UnsupportedLLMProvider: When a provider specified in the dataset is not supported.
-
-        Returns:
-            Tuple[pandas.DataFrame, pandas.DataFrame]:
-                eval_results_df: A DataFrame containing all the prompts, responses of each provider
-                    (indicated by column <provider>/<model>/response), scores of each provider
-                    (indicated by column <provider>/<model>/score), and notdiamond custom router
-                    response and score (indicated by column notdiamond/response and notdiamond/score).
-                eval_stats_df: A DataFrame containing the "Best Average Provider" computed from the
-                    provided dataset, the "Best Provider Average Score" achieved by the "Best Average Provider",
-                    and the "Not Diamond Average Score" achieved through custom router.
-        """
-
-        joint_df, llm_configs = self._prepare_joint_dataset(
-            dataset, prompt_column, response_column, score_column
-        )
-
-        client = NotDiamond(
-            llm_configs=llm_configs,
-            api_key=self.api_key,
-            preference_id=preference_id,
-        )
-
-        eval_results_df, eval_stats_df = self._eval_custom_router(
-            client, llm_configs, joint_df, prompt_column, include_latency
-        )
-        return eval_results_df, eval_stats_df

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Jun 17, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/toolkit/langchain.html b/docs/_build/html/_modules/notdiamond/toolkit/langchain.html deleted file mode 100644 index bde1235d..00000000 --- a/docs/_build/html/_modules/notdiamond/toolkit/langchain.html +++ /dev/null @@ -1,494 +0,0 @@ - - - - - - notdiamond.toolkit.langchain — NotDiamond 0.3.25 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.toolkit.langchain

-import os
-from importlib import metadata
-from typing import (
-    Any,
-    AsyncIterator,
-    Dict,
-    Iterator,
-    List,
-    Optional,
-    Sequence,
-    Union,
-)
-
-from langchain.chat_models.base import init_chat_model
-from langchain_community.adapters.openai import convert_message_to_dict
-from langchain_core.language_models import LanguageModelInput
-from langchain_core.messages.utils import convert_to_messages
-from langchain_core.prompt_values import (
-    ChatPromptValue,
-    PromptValue,
-    StringPromptValue,
-)
-from langchain_core.runnables import Runnable, RunnableConfig
-
-import notdiamond as nd
-
-_LANGCHAIN_PROVIDERS = {
-    "openai",
-    "anthropic",
-    "google",
-    "mistral",
-    "togetherai",
-    "cohere",
-}
-
-
-

-[docs]
-class NotDiamondRunnable(Runnable[LanguageModelInput, str]):
-    """
-    See Runnable docs for details
-    https://python.langchain.com/v0.1/docs/expression_language/interface/
-    """
-
-    llm_configs: List
-    api_key: Optional[str] = os.getenv("NOTDIAMOND_API_KEY")
-    client: Any
-
-

-[docs]
-    def __init__(
-        self,
-        nd_llm_configs: Optional[List] = None,
-        nd_api_key: Optional[str] = None,
-        nd_client: Optional[Any] = None,
-        nd_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        """
-        Params:
-            nd_llm_configs: List of LLM configs to use.
-            nd_api_key: Not Diamond API key.
-            nd_client: Not Diamond client.
-            nd_kwargs: Keyword arguments to pass directly to model_select.
-        """
-        if not nd_client:
-            if not nd_api_key or not nd_llm_configs:
-                raise ValueError(
-                    "Must provide either client or api_key and llm_configs to "
-                    "instantiate NotDiamondRunnable."
-                )
-            nd_client = nd.NotDiamond(
-                llm_configs=nd_llm_configs,
-                api_key=nd_api_key,
-            )
-        elif nd_client.llm_configs:
-            for llm_config in nd_client.llm_configs:
-                if isinstance(llm_config, str):
-                    llm_config = nd.LLMConfig.from_string(llm_config)
-                if llm_config.provider not in _LANGCHAIN_PROVIDERS:
-                    raise ValueError(
-                        f"Requested provider in {llm_config} supported by Not Diamond "
-                        "but not langchain.chat_models.base.init_chat_model. Please "
-                        "remove it from your llm_configs."
-                    )
-
-        try:
-            nd_client.user_agent = (
-                f"langchain-community/{metadata.version('notdiamond')}"
-            )
-        except AttributeError:
-            pass
-
-        self.client = nd_client
-        self.api_key = nd_client.api_key
-        self.llm_configs = nd_client.llm_configs
-        self.nd_kwargs = nd_kwargs or dict()

-
-
-    def _model_select(self, input: LanguageModelInput) -> str:
-        messages = _convert_input_to_message_dicts(input)
-        _, provider = self.client.chat.completions.model_select(
-            messages=messages, **self.nd_kwargs
-        )
-        provider_str = _nd_provider_to_langchain_provider(str(provider))
-        return provider_str
-
-    async def _amodel_select(self, input: LanguageModelInput) -> str:
-        messages = _convert_input_to_message_dicts(input)
-        _, provider = await self.client.chat.completions.amodel_select(
-            messages=messages, **self.nd_kwargs
-        )
-        provider_str = _nd_provider_to_langchain_provider(str(provider))
-        return provider_str
-
-

-[docs]
-    def stream(
-        self,
-        input: LanguageModelInput,
-        config: Optional[RunnableConfig] = None,
-        **kwargs: Optional[Any],
-    ) -> Iterator[str]:
-        yield self._model_select(input)

-
-
-

-[docs]
-    def invoke(
-        self,
-        input: LanguageModelInput,
-        config: Optional[RunnableConfig] = None,
-    ) -> str:
-        return self._model_select(input)

-
-
-

-[docs]
-    def batch(
-        self,
-        inputs: List[LanguageModelInput],
-        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
-        **kwargs: Optional[Any],
-    ) -> List[str]:
-        return [self._model_select(input) for input in inputs]

-
-
-

-[docs]
-    async def astream(
-        self,
-        input: LanguageModelInput,
-        config: Optional[RunnableConfig] = None,
-        **kwargs: Optional[Any],
-    ) -> AsyncIterator[str]:
-        yield await self._amodel_select(input)

-
-
-

-[docs]
-    async def ainvoke(
-        self,
-        input: LanguageModelInput,
-        config: Optional[RunnableConfig] = None,
-        **kwargs: Optional[Any],
-    ) -> str:
-        return await self._amodel_select(input)

-
-
-

-[docs]
-    async def abatch(
-        self,
-        inputs: List[LanguageModelInput],
-        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
-        **kwargs: Optional[Any],
-    ) -> List[str]:
-        return [await self._amodel_select(input) for input in inputs]

-

-
-
-
-

-[docs]
-class NotDiamondRoutedRunnable(Runnable[LanguageModelInput, Any]):
-

-[docs]
-    def __init__(
-        self,
-        *args: Any,
-        configurable_fields: Optional[List[str]] = None,
-        nd_llm_configs: Optional[List] = None,
-        nd_api_key: Optional[str] = None,
-        nd_client: Optional[Any] = None,
-        nd_kwargs: Optional[Dict[str, Any]] = None,
-        **kwargs: Optional[Dict[Any, Any]],
-    ) -> None:
-        """
-        Params:
-            nd_llm_configs: List of LLM configs to use.
-            nd_api_key: Not Diamond API key.
-            nd_client: Not Diamond client.
-            nd_kwargs: Keyword arguments to pass directly to model_select.
-        """
-        _nd_kwargs = {
-            kw: kwargs[kw] for kw in kwargs.keys() if kw.startswith("nd_")
-        }
-        if nd_kwargs:
-            _nd_kwargs.update(nd_kwargs)
-
-        self._ndrunnable = NotDiamondRunnable(
-            nd_api_key=nd_api_key,
-            nd_llm_configs=nd_llm_configs,
-            nd_client=nd_client,
-            nd_kwargs=_nd_kwargs,
-        )
-        _routed_fields = ["model", "model_provider"]
-        if configurable_fields is None:
-            configurable_fields = []
-        self._configurable_fields = _routed_fields + configurable_fields
-        self._configurable_model = init_chat_model(
-            *args,
-            configurable_fields=self._configurable_fields,
-            config_prefix="nd",
-            **{kw: kwv for kw, kwv in kwargs.items() if kw not in _nd_kwargs},  # type: ignore[arg-type]
-        )

-
-
-

-[docs]
-    def stream(
-        self,
-        input: LanguageModelInput,
-        config: Optional[RunnableConfig] = None,
-        **kwargs: Optional[Any],
-    ) -> Iterator[Any]:
-        provider_str = self._ndrunnable._model_select(input)
-        _config = self._build_model_config(provider_str, config)
-        yield from self._configurable_model.stream(input, config=_config)

-
-
-

-[docs]
-    def invoke(
-        self,
-        input: LanguageModelInput,
-        config: Optional[RunnableConfig] = None,
-        **kwargs: Optional[Any],
-    ) -> Any:
-        provider_str = self._ndrunnable._model_select(input)
-        _config = self._build_model_config(provider_str, config)
-        return self._configurable_model.invoke(input, config=_config)

-
-
-

-[docs]
-    def batch(
-        self,
-        inputs: List[LanguageModelInput],
-        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
-        **kwargs: Optional[Any],
-    ) -> List[Any]:
-        config = config or {}
-
-        provider_strs = [
-            self._ndrunnable._model_select(input) for input in inputs
-        ]
-        if isinstance(config, dict):
-            _configs = [
-                self._build_model_config(ps, config) for ps in provider_strs
-            ]
-        else:
-            _configs = [
-                self._build_model_config(ps, config[i])
-                for i, ps in enumerate(provider_strs)
-            ]
-
-        return self._configurable_model.batch(inputs, config=_configs)

-
-
-

-[docs]
-    async def astream(
-        self,
-        input: LanguageModelInput,
-        config: Optional[RunnableConfig] = None,
-        **kwargs: Optional[Any],
-    ) -> AsyncIterator[Any]:
-        provider_str = await self._ndrunnable._amodel_select(input)
-        _config = self._build_model_config(provider_str, config)
-        async for chunk in self._configurable_model.astream(
-            input, config=_config
-        ):
-            yield chunk

-
-
-

-[docs]
-    async def ainvoke(
-        self,
-        input: LanguageModelInput,
-        config: Optional[RunnableConfig] = None,
-        **kwargs: Optional[Any],
-    ) -> Any:
-        provider_str = await self._ndrunnable._amodel_select(input)
-        _config = self._build_model_config(provider_str, config)
-        return await self._configurable_model.ainvoke(input, config=_config)

-
-
-

-[docs]
-    async def abatch(
-        self,
-        inputs: List[LanguageModelInput],
-        config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
-        **kwargs: Optional[Any],
-    ) -> List[Any]:
-        config = config or {}
-
-        provider_strs = [
-            await self._ndrunnable._amodel_select(input) for input in inputs
-        ]
-        if isinstance(config, dict):
-            _configs = [
-                self._build_model_config(ps, config) for ps in provider_strs
-            ]
-        else:
-            _configs = [
-                self._build_model_config(ps, config[i])
-                for i, ps in enumerate(provider_strs)
-            ]
-
-        return await self._configurable_model.abatch(inputs, config=_configs)

-
-
-

-[docs]
-    def _build_model_config(
-        self, provider_str: str, config: Optional[RunnableConfig] = None
-    ) -> RunnableConfig:
-        """
-        Provider string should take the form '{model}/{model_provider}'
-        """
-        config = config or RunnableConfig()
-
-        model_provider, model = provider_str.split("/")
-        _config = RunnableConfig(
-            configurable={
-                "nd_model": model,
-                "nd_model_provider": model_provider,
-            },
-        )
-
-        for k, v in config.items():
-            _config["configurable"][f"nd_{k}"] = v
-        return _config

-

-
-
-
-def _convert_input_to_message_dicts(
-    input: LanguageModelInput,
-) -> List[Dict[str, str]]:
-    if isinstance(input, PromptValue):
-        output = input
-    elif isinstance(input, str):
-        output = StringPromptValue(text=input)
-    elif isinstance(input, Sequence):
-        output = ChatPromptValue(messages=convert_to_messages(input))
-    else:
-        raise ValueError(
-            f"Invalid input type {type(input)}. "
-            "Must be a PromptValue, str, or list of BaseMessages."
-        )
-    return [
-        convert_message_to_dict(message) for message in output.to_messages()
-    ]
-
-
-def _nd_provider_to_langchain_provider(llm_config_str: str) -> str:
-    provider, model = llm_config_str.split("/")
-    provider = (
-        provider.replace("google", "google_genai")
-        .replace("mistral", "mistralai")
-        .replace("togetherai", "together")
-    )
-    return f"{provider}/{model}"
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Nov 22, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/toolkit/openai.html b/docs/_build/html/_modules/notdiamond/toolkit/openai.html deleted file mode 100644 index 777b32cd..00000000 --- a/docs/_build/html/_modules/notdiamond/toolkit/openai.html +++ /dev/null @@ -1,276 +0,0 @@ - - - - - - notdiamond.toolkit.openai — NotDiamond 0.3.38 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.toolkit.openai

-"""
-Tools for working directly with OpenAI's various models.
-"""
-import logging
-from typing import List, Union
-
-from notdiamond import NotDiamond
-from notdiamond.llms.providers import NDLLMProviders
-from notdiamond.settings import NOTDIAMOND_API_KEY, OPENAI_API_KEY
-
-LOGGER = logging.getLogger(__name__)
-LOGGER.setLevel(logging.INFO)
-
-_ND_PARAMS = [
-    "llm_configs",
-    "default",
-    "max_model_depth",
-    "latency_tracking",
-    "hash_content",
-    "tradeoff",
-    "preference_id",
-    "tools",
-    "callbacks",
-    "nd_api_url",
-    "nd_api_key",
-    "user_agent",
-]
-_SHARED_PARAMS = ["timeout", "max_retries"]
-
-
-

-[docs]
-class _OpenAIBase:
-    """
-    Base class which wraps both an openai client and Not Diamond retry / fallback logic.
-    """
-
-    def __init__(self, oai_client_cls, *args, **kwargs):
-        nd_kwargs = {
-            k: v for k, v in kwargs.items() if k in _ND_PARAMS + _SHARED_PARAMS
-        }
-
-        # TODO [a9] remove llm_configs as valid constructor arg for ND client
-        self._nd_client = NotDiamond(
-            api_key=nd_kwargs.get("nd_api_key", NOTDIAMOND_API_KEY),
-            llm_configs=["openai/gpt-3.5-turbo"],
-            *args,
-            **nd_kwargs,
-        )
-
-        # Create a OpenAI client with a dummy model - will ignore this during routing
-        oai_kwargs = {k: v for k, v in kwargs.items() if k not in _ND_PARAMS}
-        self._oai_client = oai_client_cls(
-            *args, api_key=OPENAI_API_KEY, **oai_kwargs
-        )
-
-    def __getattr__(self, name):
-        return getattr(self._oai_client, name)
-
-    def __call__(self, *args, **kwargs):
-        return self._oai_client(*args, **kwargs)
-
-    def __dir__(self):
-        return dir(self._oai_client)
-
-    @property
-    def chat(self):
-        class ChatCompletions:
-            def __init__(self, parent):
-                self.parent = parent
-
-            @property
-            def completions(self):
-                return self
-
-            def create(self, *args, **kwargs):
-                return self.parent.create(*args, **kwargs)
-
-        return ChatCompletions(self)
-
-    def _create_prep(self, model: Union[str, List], **kwargs):
-        model = kwargs.get("model", model)
-
-        if model is None:
-            LOGGER.info(
-                "No LLM configs provided. Not Diamond will route to all OpenAI models."
-            )
-            llm_configs = [
-                str(p) for p in NDLLMProviders if p.provider == "openai"
-            ]
-        elif isinstance(model, str):
-            llm_configs = model.split(",")
-        elif isinstance(model, list):
-            llm_configs = self._nd_client._parse_llm_configs_data(model)
-
-        if "messages" not in kwargs:
-            raise ValueError("'messages' argument is required")
-
-        return llm_configs

-
-
-
-

-[docs]
-class OpenAI(_OpenAIBase):
-    """
-    Encapsulating class for an openai.OpenAI client. This supports the same methods as
-    the openai package, while also supporting routed prompts with calls to `completion`.
-    """
-
-    def __init__(self, *args, **kwargs):
-        from openai import OpenAI as OpenAIClient
-
-        super().__init__(OpenAIClient, *args, **kwargs)
-
-

-[docs]
-    def create(self, *args, model: Union[str, List] = None, **kwargs):
-        """
-        Perform chat completion using OpenAI's API, after routing the prompt to a
-        specific LLM via Not Diamond.
-        """
-        llm_configs = self._create_prep(model, **kwargs)
-        session_id, best_llm = self._nd_client.model_select(
-            *args, model=llm_configs, **kwargs
-        )
-        response = self._oai_client.chat.completions.create(
-            *args, model=str(best_llm.model), **kwargs
-        )
-        LOGGER.info(f"Routed prompt to {best_llm} for session ID {session_id}")
-        return response

-

-
-
-
-

-[docs]
-class AsyncOpenAI(_OpenAIBase):
-    """
-    Encapsulating class for an openai.OpenAI client. This supports the same methods as
-    the openai package, while also supporting routed prompts with calls to `completion`.
-    """
-
-    def __init__(self, *args, **kwargs):
-        from openai import AsyncOpenAI as OpenAIClient
-
-        super().__init__(OpenAIClient, *args, **kwargs)
-
-

-[docs]
-    async def create(self, *args, model: Union[str, List] = None, **kwargs):
-        """
-        Perform async chat completion using OpenAI's API, after routing the prompt to a
-        specific LLM via Not Diamond.
-        """
-        llm_configs = self._create_prep(model, **kwargs)
-        session_id, best_llm = await self._nd_client.amodel_select(
-            *args, model=llm_configs, **kwargs
-        )
-        response = await self._oai_client.chat.completions.create(
-            *args, model=str(best_llm.model), **kwargs
-        )
-        LOGGER.debug(
-            f"Routed prompt to {best_llm} for session ID {session_id}"
-        )
-        return response

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Feb 18, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/notdiamond/toolkit/rag/evaluation.html b/docs/_build/html/_modules/notdiamond/toolkit/rag/evaluation.html deleted file mode 100644 index ec664b03..00000000 --- a/docs/_build/html/_modules/notdiamond/toolkit/rag/evaluation.html +++ /dev/null @@ -1,373 +0,0 @@ - - - - - - notdiamond.toolkit.rag.evaluation — NotDiamond 0.3.43 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Module code
    • -
  • notdiamond.toolkit.rag.evaluation
    • -
  • -
    • -
-
-
-
-
- -

Source code for notdiamond.toolkit.rag.evaluation

-import logging
-from typing import Dict, List, Optional, Sequence, Tuple, Union
-
-import optuna
-import pandas as pd
-from langchain_core.callbacks import Callbacks
-from langchain_core.embeddings import Embeddings as LangchainEmbeddings
-from langchain_core.language_models import BaseLanguageModel as LangchainLLM
-from langchain_core.prompt_values import StringPromptValue
-from ragas import EvaluationDataset, SingleTurnSample
-from ragas._analytics import track_was_completed
-from ragas.cost import TokenUsageParser
-from ragas.embeddings.base import BaseRagasEmbeddings
-from ragas.evaluation import evaluate as ragas_evaluate
-from ragas.llms import BaseRagasLLM
-from ragas.metrics.base import Metric
-from ragas.run_config import RunConfig
-from tqdm import tqdm
-
-from notdiamond.llms.config import LLMConfig
-from notdiamond.toolkit.rag.evaluation_dataset import (
-    RAGEvaluationDataset,
-    RAGSample,
-)
-from notdiamond.toolkit.rag.llms import get_llm
-from notdiamond.toolkit.rag.workflow import BaseNDRagWorkflow
-
-LOGGER = logging.getLogger(__name__)
-LOGGER.setLevel(logging.INFO)
-
-_DEFAULT_GENERATION_TEMPERATURE = 0.7
-
-
-

-[docs]
-def get_eval_dataset(
-    test_queries: pd.DataFrame,
-    workflow: BaseNDRagWorkflow,
-    generation_prompt: str = None,
-    generator_llm: Union[LLMConfig, str] = None,
-):
-    """
-    Create a dataset of RAGSample objects to evaluate the performance of a RAG workflow.
-
-    Args:
-        test_queries: A pandas DataFrame with schema implied by the method below.
-        workflow: BaseNDRagWorkflow subclass created by the user.
-
-    Schema for test_queries can be found below
-    https://docs.ragas.io/en/stable/references/evaluation_schema/#ragas.dataset_schema.SingleTurnSample
-    """
-    samples = []
-    for _, row in test_queries.iterrows():
-        query = row["user_input"]
-        reference = row["reference"]
-        generation_prompt = generation_prompt or row.get("generation_prompt")
-        if generation_prompt is None:
-            raise ValueError(
-                "Provided test queries DataFrame does not include"
-                " 'generation_prompt' and 'generation_prompt' is not provided."
-            )
-        generator_llm = generator_llm or row.get("generator_llm")
-        if generator_llm is None:
-            raise ValueError(
-                "Provided test queries DataFrame does not include"
-                " 'generator_llm' and 'generator_llm' is not provided."
-            )
-
-        retrieved_contexts = workflow.get_retrieved_context(query)
-        response = workflow.get_response(query)
-
-        sample = RAGSample(
-            user_input=query,
-            retrieved_contexts=retrieved_contexts,
-            response=response,
-            reference=reference,
-            generation_prompt=generation_prompt,
-            generator_llm=generator_llm,
-        )
-        samples.append(sample)
-    eval_dataset = RAGEvaluationDataset(samples)
-    return eval_dataset

-
-
-
-

-[docs]
-def auto_optimize(workflow: BaseNDRagWorkflow, n_trials: int):
-    direction = "maximize" if workflow.objective_maximize else "minimize"
-    study = optuna.create_study(
-        study_name=workflow.job_name, direction=direction
-    )
-    study.optimize(workflow._outer_objective, n_trials=n_trials)
-    workflow._set_param_values(study.best_params)
-    return {"best_params": study.best_params, "trials": study.trials}

-
-
-
-def _map_to_ragas_samples(
-    dataset: RAGEvaluationDataset,
-) -> Tuple[EvaluationDataset, pd.DataFrame]:
-    ragas_samples = []
-    extra_columns = {
-        "generation_prompt": [],
-    }
-    for sample in dataset:
-        ragas_sample = SingleTurnSample(
-            user_input=sample.user_input,
-            retrieved_contexts=sample.retrieved_contexts,
-            reference_contexts=sample.reference_contexts,
-            response=sample.response,
-            multi_responses=sample.multi_responses,
-            reference=sample.reference,
-            rubrics=sample.rubrics,
-        )
-        ragas_samples.append(ragas_sample)
-        extra_columns["generation_prompt"].append(sample.generation_prompt)
-
-    extra_columns_df = pd.DataFrame.from_dict(extra_columns)
-    return EvaluationDataset(ragas_samples), extra_columns_df
-
-
-def _evaluate_dataset(
-    generator_llm: LLMConfig,
-    dataset: EvaluationDataset,
-    metrics: Optional[Sequence[Metric]] = None,
-    llm: Optional[Union[BaseRagasLLM, LangchainLLM]] = None,
-    embeddings: Optional[
-        Union[BaseRagasEmbeddings, LangchainEmbeddings]
-    ] = None,
-    callbacks: Callbacks = None,
-    in_ci: bool = False,
-    run_config: RunConfig = RunConfig(),
-    token_usage_parser: Optional[TokenUsageParser] = None,
-    raise_exceptions: bool = False,
-    column_map: Optional[Dict[str, str]] = None,
-    show_progress: bool = True,
-    batch_size: Optional[int] = None,
-) -> pd.DataFrame:
-    LOGGER.info(f"Evaluating generations from {str(generator_llm)}")
-
-    result = ragas_evaluate(
-        dataset,
-        metrics,
-        llm,
-        embeddings,
-        callbacks,
-        in_ci,
-        run_config,
-        token_usage_parser,
-        raise_exceptions,
-        column_map,
-        show_progress,
-        batch_size,
-    )
-    return result.to_pandas()
-
-
-def _generate_rag_eval_dataset(
-    generator_llm: LLMConfig,
-    dataset: RAGEvaluationDataset,
-    temperature: float = _DEFAULT_GENERATION_TEMPERATURE,
-) -> RAGEvaluationDataset:
-    LOGGER.info(f"Generating responses from {str(generator_llm)}")
-
-    llm = get_llm(generator_llm)
-    temperature = generator_llm.kwargs.get("temperature", temperature)
-
-    eval_samples = []
-    for sample in tqdm(dataset):
-        response = llm.generate_text(
-            StringPromptValue(text=sample.generation_prompt),
-            temperature=temperature,
-        )
-        eval_sample = RAGSample(
-            user_input=sample.user_input,
-            retrieved_contexts=sample.retrieved_contexts,
-            reference_contexts=sample.reference_contexts,
-            response=response.generations[0][0].text,
-            multi_responses=sample.multi_responses,
-            reference=sample.reference,
-            rubrics=sample.rubrics,
-            generation_prompt=sample.generation_prompt,
-            generator_llm=str(generator_llm),
-        )
-        eval_samples.append(eval_sample)
-    return RAGEvaluationDataset(eval_samples)
-
-
-

-[docs]
-@track_was_completed
-def evaluate(
-    dataset: RAGEvaluationDataset,
-    metrics: Optional[Sequence[Metric]] = None,
-    llm: Optional[Union[BaseRagasLLM, LangchainLLM]] = None,
-    embeddings: Optional[
-        Union[BaseRagasEmbeddings, LangchainEmbeddings]
-    ] = None,
-    callbacks: Callbacks = None,
-    in_ci: bool = False,
-    run_config: RunConfig = RunConfig(),
-    token_usage_parser: Optional[TokenUsageParser] = None,
-    raise_exceptions: bool = False,
-    column_map: Optional[Dict[str, str]] = None,
-    show_progress: bool = True,
-    batch_size: Optional[int] = None,
-    generator_llms: List[LLMConfig] = [],
-) -> Dict[str, pd.DataFrame]:
-    dataset_llm_str = dataset[0].generator_llm
-    dataset_llm_config = LLMConfig.from_string(dataset_llm_str)
-
-    if dataset_llm_config not in generator_llms:
-        generator_llms.append(dataset_llm_config)
-
-    ragas_dataset, extra_columns = _map_to_ragas_samples(dataset)
-
-    dataset_results = _evaluate_dataset(
-        dataset_llm_config,
-        ragas_dataset,
-        metrics,
-        llm,
-        embeddings,
-        callbacks,
-        in_ci,
-        run_config,
-        token_usage_parser,
-        raise_exceptions,
-        column_map,
-        show_progress,
-        batch_size,
-    )
-
-    evaluation_results = {
-        str(dataset_llm_config): pd.concat(
-            [dataset_results, extra_columns], axis=1
-        )
-    }
-
-    for llm_config in generator_llms:
-        if str(llm_config) in evaluation_results:
-            continue
-
-        llm_dataset = _generate_rag_eval_dataset(llm_config, dataset)
-        ragas_dataset, extra_columns = _map_to_ragas_samples(llm_dataset)
-        dataset_results = _evaluate_dataset(
-            llm_config,
-            ragas_dataset,
-            metrics,
-            llm,
-            embeddings,
-            callbacks,
-            in_ci,
-            run_config,
-            token_usage_parser,
-            raise_exceptions,
-            column_map,
-            show_progress,
-            batch_size,
-        )
-        evaluation_results[str(llm_config)] = pd.concat(
-            [dataset_results, extra_columns], axis=1
-        )
-    return evaluation_results

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Mar 27, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/toolkit/rag/evaluation_dataset.html b/docs/_build/html/_modules/notdiamond/toolkit/rag/evaluation_dataset.html deleted file mode 100644 index 0d1c9d4f..00000000 --- a/docs/_build/html/_modules/notdiamond/toolkit/rag/evaluation_dataset.html +++ /dev/null @@ -1,210 +0,0 @@ - - - - - - notdiamond.toolkit.rag.evaluation_dataset — NotDiamond 0.3.43 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Module code
    • -
  • notdiamond.toolkit.rag.evaluation_dataset
    • -
  • -
    • -
-
-
-
-
- -

Source code for notdiamond.toolkit.rag.evaluation_dataset

-from dataclasses import dataclass
-from typing import Dict, List, Union, overload
-
-from ragas import MultiTurnSample, SingleTurnSample
-from ragas.dataset_schema import RagasDataset
-
-
-

-[docs]
-class RAGSample(SingleTurnSample):
-    """
-    Represents RAG evaluation samples.
-
-    Attributes:
-        user_input (Optional[str]): The input query from the user.
-        retrieved_contexts (Optional[List[str]]): List of contexts retrieved for the query.
-        reference_contexts (Optional[List[str]]): List of reference contexts for the query.
-        response (Optional[str]): The generated response for the query.
-        generation_prompt (str): The input prompt to the generator LLM.
-        generator_llm (str): The LLM used to generate the response.
-        multi_responses (Optional[List[str]]): List of multiple responses generated for the query.
-        reference (Optional[str]): The reference answer for the query.
-        rubric (Optional[Dict[str, str]]): Evaluation rubric for the sample.
-    """
-
-    generation_prompt: str
-    generator_llm: str

-
-
-
-

-[docs]
-@dataclass
-class RAGEvaluationDataset(RagasDataset[RAGSample]):
-    """
-    Represents a dataset of RAG evaluation samples.
-
-    Attributes:
-        samples (List[BaseSample]): A list of evaluation samples.
-
-    Methods:
-        validate_samples(samples): Validates that all samples are of the same type.
-        get_sample_type(): Returns the type of the samples in the dataset.
-        to_hf_dataset(): Converts the dataset to a Hugging Face Dataset.
-        to_pandas(): Converts the dataset to a pandas DataFrame.
-        features(): Returns the features of the samples.
-        from_list(mapping): Creates an EvaluationDataset from a list of dictionaries.
-        from_dict(mapping): Creates an EvaluationDataset from a dictionary.
-        to_csv(path): Converts the dataset to a CSV file.
-        to_jsonl(path): Converts the dataset to a JSONL file.
-        from_jsonl(path): Creates an EvaluationDataset from a JSONL file.
-    """
-
-    @overload
-    def __getitem__(self, idx: int) -> RAGSample:
-        ...
-
-    @overload
-    def __getitem__(self, idx: slice) -> "RAGEvaluationDataset":
-        ...
-
-    def __getitem__(
-        self, idx: Union[int, slice]
-    ) -> Union[RAGSample, "RAGEvaluationDataset"]:
-        if isinstance(idx, int):
-            return self.samples[idx]
-        elif isinstance(idx, slice):
-            return type(self)(samples=self.samples[idx])
-        else:
-            raise TypeError("Index must be int or slice")
-
-

-[docs]
-    def is_multi_turn(self) -> bool:
-        return False

-
-
-

-[docs]
-    def to_list(self) -> List[Dict]:
-        rows = [sample.to_dict() for sample in self.samples]
-        return rows

-
-
-

-[docs]
-    @classmethod
-    def from_list(cls, data: List[Dict]):
-        samples = []
-        if all(
-            "user_input" in item and isinstance(data[0]["user_input"], list)
-            for item in data
-        ):
-            samples.extend(MultiTurnSample(**sample) for sample in data)
-        else:
-            samples.extend(SingleTurnSample(**sample) for sample in data)
-        return cls(samples=samples)

-
-
-    def __repr__(self) -> str:
-        return f"RAGEvaluationDataset(features={self.features()}, len={len(self.samples)})"

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Mar 27, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/toolkit/rag/llms.html b/docs/_build/html/_modules/notdiamond/toolkit/rag/llms.html deleted file mode 100644 index df3e0938..00000000 --- a/docs/_build/html/_modules/notdiamond/toolkit/rag/llms.html +++ /dev/null @@ -1,184 +0,0 @@ - - - - - - notdiamond.toolkit.rag.llms — NotDiamond 0.3.43 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.toolkit.rag.llms

-from typing import Union
-
-from langchain_cohere import CohereEmbeddings
-from langchain_mistralai import MistralAIEmbeddings
-from langchain_openai import OpenAIEmbeddings
-from ragas.embeddings import HuggingfaceEmbeddings, LangchainEmbeddingsWrapper
-from ragas.llms import LangchainLLMWrapper
-
-from ...exceptions import UnsupportedEmbeddingProvider
-from ...llms.client import NotDiamond
-from ...llms.config import EmbeddingConfig, LLMConfig
-
-
-

-[docs]
-def get_llm(llm_config_or_str: Union[LLMConfig, str]) -> LangchainLLMWrapper:
-    """
-    Build the LLM object compatible with evaluation metrics.
-
-    Parameters:
-        llm_config_or_str (Union[LLMConfig, str]): a LLMConfig object or a model string
-            that specifies the LLM to construct.
-    """
-    if isinstance(llm_config_or_str, str):
-        llm_config = LLMConfig.from_string(llm_config_or_str)
-    else:
-        llm_config = llm_config_or_str
-
-    lc_llm = NotDiamond._llm_from_config(llm_config)
-    return LangchainLLMWrapper(lc_llm)

-
-
-
-

-[docs]
-def get_embedding(
-    embedding_model_config_or_str: Union[EmbeddingConfig, str]
-) -> Union[LangchainEmbeddingsWrapper, HuggingfaceEmbeddings]:
-    """
-    Build the embedding model object compatible with evaluation metrics.
-
-    Parameters:
-        embedding_model_config_or_str (Union[EmbeddingConfig, str]): an EmbeddingConfig object
-            or an embedding model string that specifies the embedding model to construct.
-    """
-    if isinstance(embedding_model_config_or_str, str):
-        embedding_config = EmbeddingConfig.from_string(
-            embedding_model_config_or_str
-        )
-    else:
-        embedding_config = embedding_model_config_or_str
-
-    if embedding_config.provider == "openai":
-        lc_embedding = OpenAIEmbeddings(
-            model=embedding_config.model, **embedding_config.kwargs
-        )
-
-    elif embedding_config.provider == "cohere":
-        lc_embedding = CohereEmbeddings(
-            model=embedding_config.model, **embedding_config.kwargs
-        )
-
-    elif embedding_config.provider == "mistral":
-        lc_embedding = MistralAIEmbeddings(
-            model=embedding_config.model, **embedding_config.kwargs
-        )
-
-    elif embedding_config.provider == "huggingface":
-        return HuggingfaceEmbeddings(model_name=embedding_config.model)
-
-    else:
-        raise UnsupportedEmbeddingProvider(
-            f"Embedding model {str(embedding_config)} not supported."
-        )
-    return LangchainEmbeddingsWrapper(lc_embedding)

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Mar 27, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/toolkit/rag/testset.html b/docs/_build/html/_modules/notdiamond/toolkit/rag/testset.html deleted file mode 100644 index bd6df292..00000000 --- a/docs/_build/html/_modules/notdiamond/toolkit/rag/testset.html +++ /dev/null @@ -1,339 +0,0 @@ - - - - - - notdiamond.toolkit.rag.testset — NotDiamond 0.3.43 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Module code
    • -
  • notdiamond.toolkit.rag.testset
    • -
  • -
    • -
-
-
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- -

Source code for notdiamond.toolkit.rag.testset

-from typing import List, Optional, Sequence, Union
-
-import pandas as pd
-from langchain_core.callbacks import Callbacks
-from langchain_core.documents import Document as LCDocument
-from llama_index.core.base.embeddings.base import (
-    BaseEmbedding as LlamaIndexEmbedding,
-)
-from llama_index.core.base.llms.base import BaseLLM as LlamaIndexLLM
-from llama_index.core.schema import Document as LlamaIndexDocument
-from ragas.embeddings import BaseRagasEmbeddings, LlamaIndexEmbeddingsWrapper
-from ragas.llms import BaseRagasLLM, LlamaIndexLLMWrapper
-from ragas.run_config import RunConfig
-from ragas.testset import TestsetGenerator
-from ragas.testset.graph import KnowledgeGraph, Node, NodeType
-from ragas.testset.persona import Persona
-from ragas.testset.synthesizers import QueryDistribution
-from ragas.testset.transforms import (
-    Transforms,
-    apply_transforms,
-    default_transforms,
-)
-
-
-

-[docs]
-class TestDataGenerator(TestsetGenerator):
-

-[docs]
-    def __init__(
-        self,
-        llm: BaseRagasLLM,
-        embedding_model: BaseRagasEmbeddings,
-        knowledge_graph: KnowledgeGraph = KnowledgeGraph(),
-        persona_list: Optional[List[Persona]] = None,
-    ):
-        """
-        RAG Test data generator class.
-        Generates test cases from documents for evaluating RAG workflows.
-
-        Parameters:
-            llm (BaseRagasLLM): An LLM object inherited from BaseRagasLLM. Obtain this
-                via the get_llm tool.
-            embedding_model (BaseRagasEmbeddings): An embedding model object inherited
-                from BaseRagasEmbeddings. Obtain this via the get_embedding tool.
-            knowledge_graph (KnowledgeGraph): The knowledge graph to use for the generation
-                process. Default empty.
-        """
-        super().__init__(
-            llm=llm,
-            embedding_model=embedding_model,
-            knowledge_graph=knowledge_graph,
-            persona_list=persona_list,
-        )

-
-
-

-[docs]
-    def generate_from_docs(
-        self,
-        documents: Union[Sequence[LCDocument], Sequence[LlamaIndexDocument]],
-        testset_size: int,
-        transforms: Optional[Transforms] = None,
-        transforms_llm: Optional[Union[BaseRagasLLM, LlamaIndexLLM]] = None,
-        transforms_embedding_model: Optional[
-            Union[BaseRagasEmbeddings, LlamaIndexEmbedding]
-        ] = None,
-        query_distribution: Optional[QueryDistribution] = None,
-        run_config: Optional[RunConfig] = None,
-        callbacks: Optional[Callbacks] = None,
-        with_debugging_logs: bool = False,
-        raise_exceptions: bool = True,
-    ) -> pd.DataFrame:
-        """
-        Generates an evaluation dataset based on given Langchain or Llama Index documents and parameters.
-
-        Parameters:
-            documents : Sequence[LCDocument]
-                A sequence of Langchain documents to use as source material
-            testset_size : int
-                The number of test samples to generate
-            transforms : Optional[Transforms], optional
-                Custom transforms to apply to the documents, by default None
-            transforms_llm : Optional[BaseRagasLLM], optional
-                LLM to use for transforms if different from instance LLM, by default None
-            transforms_embedding_model : Optional[BaseRagasEmbeddings], optional
-                Embedding model to use for transforms if different from instance model, by default None
-            query_distribution : Optional[QueryDistribution], optional
-                Distribution of query types to generate, by default None
-            run_config : Optional[RunConfig], optional
-                Configuration for the generation run, by default None
-            callbacks : Optional[Callbacks], optional
-                Callbacks to use during generation, by default None
-            with_debugging_logs : bool, optional
-                Whether to include debug logs, by default False
-            raise_exceptions : bool, optional
-                Whether to raise exceptions during generation, by default True
-
-        Returns:
-            Testset
-                The generated evaluation dataset
-
-        Raises:
-            ValueError
-                If no LLM or embedding model is provided either during initialization or as arguments
-        """
-        assert isinstance(
-            documents, list
-        ), "Documents must be a list of langchain or llama-index documents."
-
-        if isinstance(documents[0], LCDocument):
-            dataset = self.generate_with_langchain_docs(
-                documents=documents,
-                testset_size=testset_size,
-                transforms=transforms,
-                transforms_llm=transforms_llm,
-                transforms_embedding_model=transforms_embedding_model,
-                query_distribution=query_distribution,
-                run_config=run_config,
-                callbacks=callbacks,
-                with_debugging_logs=with_debugging_logs,
-                raise_exceptions=raise_exceptions,
-            )
-            return dataset.to_pandas()
-
-        elif isinstance(documents[0], LlamaIndexDocument):
-            dataset = self.generate_with_llamaindex_docs(
-                documents=documents,
-                testset_size=testset_size,
-                transforms=transforms,
-                transforms_llm=transforms_llm,
-                transforms_embedding_model=transforms_embedding_model,
-                query_distribution=query_distribution,
-                run_config=run_config,
-                callbacks=callbacks,
-                with_debugging_logs=with_debugging_logs,
-                raise_exceptions=raise_exceptions,
-            )
-            return dataset.to_pandas()
-
-        raise ValueError(
-            "Documents must be a list of langchain or llama-index documents."
-        )

-
-
-

-[docs]
-    def generate_with_llamaindex_docs(
-        self,
-        documents: Sequence[LlamaIndexDocument],
-        testset_size: int,
-        transforms: Optional[Transforms] = None,
-        transforms_llm: Optional[LlamaIndexLLM] = None,
-        transforms_embedding_model: Optional[LlamaIndexEmbedding] = None,
-        query_distribution: Optional[QueryDistribution] = None,
-        run_config: Optional[RunConfig] = None,
-        callbacks: Optional[Callbacks] = None,
-        with_debugging_logs=False,
-        raise_exceptions: bool = True,
-    ):
-        """
-        Generates an evaluation dataset based on given scenarios and parameters.
-        """
-
-        run_config = run_config or RunConfig()
-
-        # force the user to provide an llm and embedding client to prevent use of default LLMs
-        if not self.llm and not transforms_llm:
-            raise ValueError(
-                "An llm client was not provided."
-                " Provide an LLM on init or as an argument to this method."
-                " Alternatively you can provide your own transforms through the `transforms` parameter."
-            )
-        if not self.embedding_model and not transforms_embedding_model:
-            raise ValueError(
-                "An embedding client was not provided."
-                " Provide an embedding through the transforms_embedding_model parameter."
-                " Alternatively you can provide your own transforms through the `transforms` parameter."
-            )
-
-        if not transforms:
-            # use TestsetGenerator's LLM and embedding model if no transforms_llm or transforms_embedding_model is provided
-            if transforms_llm is None:
-                llm_for_transforms = self.llm
-            else:
-                llm_for_transforms = LlamaIndexLLMWrapper(transforms_llm)
-            if transforms_embedding_model is None:
-                embedding_model_for_transforms = self.embedding_model
-            else:
-                embedding_model_for_transforms = LlamaIndexEmbeddingsWrapper(
-                    transforms_embedding_model
-                )
-
-            # create the transforms
-            transforms = default_transforms(
-                documents=[
-                    LCDocument(page_content=doc.text) for doc in documents
-                ],
-                llm=llm_for_transforms,
-                embedding_model=embedding_model_for_transforms,
-            )
-
-        # convert the documents to Ragas nodes
-        nodes = []
-        for doc in documents:
-            if doc.text is not None and doc.text.strip() != "":
-                node = Node(
-                    type=NodeType.DOCUMENT,
-                    properties={
-                        "page_content": doc.text,
-                        "document_metadata": doc.metadata,
-                    },
-                )
-                nodes.append(node)
-
-        kg = KnowledgeGraph(nodes=nodes)
-
-        # apply transforms and update the knowledge graph
-        apply_transforms(kg, transforms, run_config)
-        self.knowledge_graph = kg
-
-        return self.generate(
-            testset_size=testset_size,
-            query_distribution=query_distribution,
-            run_config=run_config,
-            callbacks=callbacks,
-            with_debugging_logs=with_debugging_logs,
-            raise_exceptions=raise_exceptions,
-        )

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Mar 27, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/toolkit/rag/workflow.html b/docs/_build/html/_modules/notdiamond/toolkit/rag/workflow.html deleted file mode 100644 index ea56faf0..00000000 --- a/docs/_build/html/_modules/notdiamond/toolkit/rag/workflow.html +++ /dev/null @@ -1,382 +0,0 @@ - - - - - - notdiamond.toolkit.rag.workflow — NotDiamond 0.3.43 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
-
    -
    • -
  • Module code
    • -
  • notdiamond.toolkit.rag.workflow
    • -
  • -
    • -
-
-
-
-
- -

Source code for notdiamond.toolkit.rag.workflow

-from dataclasses import dataclass
-from typing import Any, ClassVar, Dict, List, Optional, Type, Union, get_args
-
-import optuna
-
-from notdiamond.toolkit.rag.evaluation_dataset import RAGEvaluationDataset
-
-
-

-[docs]
-@dataclass
-class IntValueRange:
-    """
-    A range of int values for an auto-evaluated RAG pipeline. Useful for, eg. RAG context chunk size.
-    """
-
-    lo: int
-    hi: int
-    step: int
-
-    def __contains__(self, value: int) -> bool:
-        return (
-            self.lo <= value <= self.hi and (value - self.lo) % self.step == 0
-        )

-
-
-
-

-[docs]
-@dataclass
-class FloatValueRange:
-    """
-    A range of float values for an auto-evaluated RAG pipeline. Useful for, eg. LLM temperature.
-    """
-
-    lo: float
-    hi: float
-    step: float
-
-    def __contains__(self, value: float) -> bool:
-        return self.lo <= value <= self.hi

-
-
-
-

-[docs]
-@dataclass
-class CategoricalValueOptions:
-    """
-    A list of categorical values for an auto-evaluated RAG pipeline. Useful for, eg. embedding algorithms.
-    """
-
-    values: List[str]
-
-    def __contains__(self, value: str) -> bool:
-        return value in self.values

-
-
-
-_ALLOWED_TYPES = [IntValueRange, FloatValueRange, CategoricalValueOptions]
-
-
-

-[docs]
-class BaseNDRagWorkflow:
-    """
-    A base interface for a RAG workflow to be auto-evaluated by Not Diamond.
-
-    Subclasses should define parameter_specs to type parameters they need to optimize,
-    by using type annotations with the above dataclasses. For example:
-
-    .. code-block:: python
-
-        class ExampleNDRagWorkflow(BaseNDRagWorkflow):
-            parameter_specs = {
-                "chunk_size": (Annotated[int, IntValueRange(1000, 2500, 500)], 1000),
-                                "chunk_overlap": (Annotated[int, IntValueRange(50, 200, 25)], 100),
-                                "top_k": (Annotated[int, IntValueRange(1, 20, 1)], 5),
-                                "algo": (
-                                        Annotated[
-                                                str,
-                                                CategoricalValueOptions(
-                                                        [
-                                                                "BM25",
-                                                                "openai_small",
-                                                                "openai_large",
-                                                                "cohere_eng",
-                                                                "cohere_multi",
-                                                        ]
-                        ),
-                    ],
-                    "BM25",
-                ),
-                "temperature": (Annotated[float, FloatValueRange(0.0, 1.0, 0.1)], 0.9),
-            }
-
-    """
-
-    parameter_specs: ClassVar[Dict[str, tuple[Type, Any]]] = {}
-
-

-[docs]
-    def __init__(
-        self,
-        documents: Any,
-        test_queries: Optional[Any] = None,
-        objective_maximize: bool = True,
-        **kwargs,
-    ):
-        """
-        Args:
-            evaluation_dataset: A dataset of RAG evaluation samples.
-            documents: The documents to use for RAG.
-            objective_maximize: Whether to maximize or minimize the objective defined below.
-        """
-        if not self.parameter_specs:
-            raise NotImplementedError(
-                f"Class {self.__class__.__name__} must define parameter_specs"
-            )
-
-        self._param_ranges = {}
-        self._base_param_types = {}
-        for param_name, (
-            param_type,
-            default_value,
-        ) in self.parameter_specs.items():
-            type_args = get_args(param_type)
-            base_type, range_type = type_args
-            if range_type is None:
-                raise ValueError(
-                    f"Expected parameter type in {_ALLOWED_TYPES} but received {param_type}"
-                )
-            self._param_ranges[param_name] = range_type
-            self._base_param_types[param_name] = base_type
-            if not isinstance(default_value, base_type):
-                raise ValueError(
-                    f"Expected default value type {base_type} but received {type(default_value)}"
-                )
-            setattr(self, param_name, default_value)
-
-        self.documents = documents
-        self.test_queries = test_queries
-        self.objective_maximize = objective_maximize

-
-
-

-[docs]
-    def get_parameter_type(self, param_name: str) -> Type:
-        param_type = self._base_param_types.get(param_name)
-        if param_type is None:
-            raise ValueError(
-                f"Parameter {param_name} not found in parameter_specs"
-            )
-        return param_type

-
-
-

-[docs]
-    def get_parameter_range(self, param_name: str) -> Type:
-        param_range = self._param_ranges.get(param_name)
-        if param_range is None:
-            raise ValueError(
-                f"Parameter {param_name} not found in parameter_specs"
-            )
-        return param_range

-
-
-

-[docs]
-    def rag_workflow(
-        self, documents: Any, test_queries: Optional[List[str]]
-    ) -> RAGEvaluationDataset:
-        """
-        Define RAG workflow components here by setting instance attrs on `self`. Those attributes will be set
-        at init-time and available when retrieving context or generating responses.
-        """
-        raise NotImplementedError()

-
-
-

-[docs]
-    def objective(self):
-        """
-        Define the objective function for your RAG workflow. The workflow's hyperparameters will be optimized
-        according to values of this objective.
-        """
-        raise NotImplementedError()

-
-
-    def _outer_objective(self, trial: optuna.Trial):
-        for param_name in self.parameter_specs.keys():
-            param_range = self.get_parameter_range(param_name)
-            if isinstance(param_range, IntValueRange):
-                param_value = trial.suggest_int(
-                    param_name,
-                    param_range.lo,
-                    param_range.hi,
-                    step=param_range.step,
-                )
-            elif isinstance(param_range, FloatValueRange):
-                param_value = trial.suggest_float(
-                    param_name,
-                    param_range.lo,
-                    param_range.hi,
-                    step=param_range.step,
-                )
-            elif isinstance(param_range, CategoricalValueOptions):
-                param_value = trial.suggest_categorical(
-                    param_name, param_range.values
-                )
-            else:
-                raise ValueError(
-                    f"Expected parameter type in {_ALLOWED_TYPES} but received unknown parameter type: {param_range}"
-                )
-            setattr(self, param_name, param_value)
-
-        self.evaluation_dataset = self.rag_workflow(
-            self.documents, self.test_queries
-        )
-        result = self.objective()
-        self._reset_param_values()
-        return result
-
-    def _get_default_param_values(self):
-        return {
-            param_name: default_value
-            for param_name, (_, default_value) in self.parameter_specs.items()
-        }
-
-    def _set_param_values(
-        self, param_values: Dict[str, Union[int, float, str]]
-    ):
-        for param_name in self.parameter_specs.keys():
-            param_value = param_values.get(param_name)
-            param_type = self.get_parameter_type(param_name)
-            param_range = self.get_parameter_range(param_name)
-            if param_value is None:
-                raise ValueError(
-                    f"Best value for {param_name} not found. This should not happen."
-                )
-            elif not isinstance(param_value, param_type):
-                raise ValueError(
-                    f"Expected parameter type {param_type} but received {type(param_value)}"
-                )
-            elif param_value not in param_range:
-                raise ValueError(
-                    f"Parameter value {param_value} not in range {param_range}"
-                )
-            setattr(self, param_name, param_value)
-
-    def _reset_param_values(self):
-        for param_name in self.parameter_specs.keys():
-            setattr(
-                self, param_name, self._get_default_param_values()[param_name]
-            )
-
-

-[docs]
-    def job_name(self):
-        raise NotImplementedError()

-
-
-

-[docs]
-    def get_retrieved_context(self, query: str) -> List[str]:
-        raise NotImplementedError()

-
-
-

-[docs]
-    def get_response(self, query: str) -> str:
-        raise NotImplementedError()

-

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Mar 27, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/notdiamond/types.html b/docs/_build/html/_modules/notdiamond/types.html deleted file mode 100644 index f94ade39..00000000 --- a/docs/_build/html/_modules/notdiamond/types.html +++ /dev/null @@ -1,161 +0,0 @@ - - - - - - notdiamond.types — NotDiamond 0.3.38 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for notdiamond.types

-from typing import Any, Dict, List
-
-from pydantic import BaseModel, field_validator
-
-from notdiamond.exceptions import InvalidApiKey, MissingApiKey
-
-
-

-[docs]
-class NDApiKeyValidator(BaseModel):
-    api_key: str
-
-

-[docs]
-    @field_validator("api_key", mode="before")
-    @classmethod
-    def api_key_must_be_a_string(cls, v) -> str:
-        if not isinstance(v, str):
-            raise InvalidApiKey("ND API key should be a string")
-        return v

-
-
-

-[docs]
-    @field_validator("api_key", mode="after")
-    @classmethod
-    def string_must_not_be_empty(cls, v):
-        if len(v) == 0:
-            raise MissingApiKey("ND API key should be longer than 0")
-        return v

-

-
-
-
-

-[docs]
-class ModelSelectRequestPayload(BaseModel):
-    prompt_template: str
-    formatted_prompt: str
-    components: Dict[str, Dict]
-    llm_configs: List[Dict]
-    metric: str
-    max_model_depth: int

-
-
-
-

-[docs]
-class FeedbackRequestPayload(BaseModel):
-    session_id: str
-    provider: Dict[str, Any]
-    feedback: Dict[str, int]

-
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Feb 18, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/pydantic/_internal/_repr.html b/docs/_build/html/_modules/pydantic/_internal/_repr.html deleted file mode 100644 index 4d6a6675..00000000 --- a/docs/_build/html/_modules/pydantic/_internal/_repr.html +++ /dev/null @@ -1,226 +0,0 @@ - - - - - - pydantic._internal._repr — NotDiamond 0.3.25 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for pydantic._internal._repr

-"""Tools to provide pretty/human-readable display of objects."""
-
-from __future__ import annotations as _annotations
-
-import types
-import typing
-from typing import Any
-
-import typing_extensions
-
-from . import _typing_extra
-
-if typing.TYPE_CHECKING:
-    ReprArgs: typing_extensions.TypeAlias = 'typing.Iterable[tuple[str | None, Any]]'
-    RichReprResult: typing_extensions.TypeAlias = (
-        'typing.Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]'
-    )
-
-
-class PlainRepr(str):
-    """String class where repr doesn't include quotes. Useful with Representation when you want to return a string
-    representation of something that is valid (or pseudo-valid) python.
-    """
-
-    def __repr__(self) -> str:
-        return str(self)
-
-
-class Representation:
-    # Mixin to provide `__str__`, `__repr__`, and `__pretty__` and `__rich_repr__` methods.
-    # `__pretty__` is used by [devtools](https://python-devtools.helpmanual.io/).
-    # `__rich_repr__` is used by [rich](https://rich.readthedocs.io/en/stable/pretty.html).
-    # (this is not a docstring to avoid adding a docstring to classes which inherit from Representation)
-
-    # we don't want to use a type annotation here as it can break get_type_hints
-    __slots__ = ()  # type: typing.Collection[str]
-
-    def __repr_args__(self) -> ReprArgs:
-        """Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
-
-        Can either return:
-        * name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
-        * or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
-        """
-        attrs_names = self.__slots__
-        if not attrs_names and hasattr(self, '__dict__'):
-            attrs_names = self.__dict__.keys()
-        attrs = ((s, getattr(self, s)) for s in attrs_names)
-        return [(a, v) for a, v in attrs if v is not None]
-
-    def __repr_name__(self) -> str:
-        """Name of the instance's class, used in __repr__."""
-        return self.__class__.__name__
-
-    def __repr_str__(self, join_str: str) -> str:
-        return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__())
-
-    def __pretty__(self, fmt: typing.Callable[[Any], Any], **kwargs: Any) -> typing.Generator[Any, None, None]:
-        """Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects."""
-        yield self.__repr_name__() + '('
-        yield 1
-        for name, value in self.__repr_args__():
-            if name is not None:
-                yield name + '='
-            yield fmt(value)
-            yield ','
-            yield 0
-        yield -1
-        yield ')'
-
-    def __rich_repr__(self) -> RichReprResult:
-        """Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects."""
-        for name, field_repr in self.__repr_args__():
-            if name is None:
-                yield field_repr
-            else:
-                yield name, field_repr
-
-    def __str__(self) -> str:
-        return self.__repr_str__(' ')
-
-    def __repr__(self) -> str:
-        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
-
-
-def display_as_type(obj: Any) -> str:
-    """Pretty representation of a type, should be as close as possible to the original type definition string.
-
-    Takes some logic from `typing._type_repr`.
-    """
-    if isinstance(obj, types.FunctionType):
-        return obj.__name__
-    elif obj is ...:
-        return '...'
-    elif isinstance(obj, Representation):
-        return repr(obj)
-    elif isinstance(obj, typing_extensions.TypeAliasType):
-        return str(obj)
-
-    if not isinstance(obj, (_typing_extra.typing_base, _typing_extra.WithArgsTypes, type)):
-        obj = obj.__class__
-
-    if _typing_extra.origin_is_union(typing_extensions.get_origin(obj)):
-        args = ', '.join(map(display_as_type, typing_extensions.get_args(obj)))
-        return f'Union[{args}]'
-    elif isinstance(obj, _typing_extra.WithArgsTypes):
-        if typing_extensions.get_origin(obj) == typing_extensions.Literal:
-            args = ', '.join(map(repr, typing_extensions.get_args(obj)))
-        else:
-            args = ', '.join(map(display_as_type, typing_extensions.get_args(obj)))
-        try:
-            return f'{obj.__qualname__}[{args}]'
-        except AttributeError:
-            return str(obj)  # handles TypeAliasType in 3.12
-    elif isinstance(obj, type):
-        return obj.__qualname__
-    else:
-        return repr(obj).replace('typing.', '').replace('typing_extensions.', '')
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Nov 22, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/pydantic/main.html b/docs/_build/html/_modules/pydantic/main.html deleted file mode 100644 index ed0890af..00000000 --- a/docs/_build/html/_modules/pydantic/main.html +++ /dev/null @@ -1,1718 +0,0 @@ - - - - - - pydantic.main — NotDiamond 0.3.25 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for pydantic.main

-"""Logic for creating models."""
-
-from __future__ import annotations as _annotations
-
-import operator
-import sys
-import types
-import typing
-import warnings
-from copy import copy, deepcopy
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    ClassVar,
-    Dict,
-    Generator,
-    Literal,
-    Mapping,
-    Set,
-    Tuple,
-    TypeVar,
-    Union,
-    cast,
-    overload,
-)
-
-import pydantic_core
-import typing_extensions
-from pydantic_core import PydanticUndefined
-from typing_extensions import Self, TypeAlias, Unpack
-
-from ._internal import (
-    _config,
-    _decorators,
-    _fields,
-    _forward_ref,
-    _generics,
-    _import_utils,
-    _mock_val_ser,
-    _model_construction,
-    _repr,
-    _typing_extra,
-    _utils,
-)
-from ._migration import getattr_migration
-from .aliases import AliasChoices, AliasPath
-from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
-from .config import ConfigDict
-from .errors import PydanticUndefinedAnnotation, PydanticUserError
-from .json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema, JsonSchemaMode, JsonSchemaValue, model_json_schema
-from .plugin._schema_validator import PluggableSchemaValidator
-from .warnings import PydanticDeprecatedSince20
-
-if TYPE_CHECKING:
-    from inspect import Signature
-    from pathlib import Path
-
-    from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator
-
-    from ._internal._utils import AbstractSetIntStr, MappingIntStrAny
-    from .deprecated.parse import Protocol as DeprecatedParseProtocol
-    from .fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr
-else:
-    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
-    # and https://youtrack.jetbrains.com/issue/PY-51428
-    DeprecationWarning = PydanticDeprecatedSince20
-
-__all__ = 'BaseModel', 'create_model'
-
-# Keep these type aliases available at runtime:
-TupleGenerator: TypeAlias = Generator[Tuple[str, Any], None, None]
-# Keep this type alias in sync with the stub definition in `pydantic-core`:
-IncEx: TypeAlias = Union[
-    Set[int], Set[str], Mapping[int, Union['IncEx', Literal[True]]], Mapping[str, Union['IncEx', Literal[True]]]
-]
-
-_object_setattr = _model_construction.object_setattr
-
-
-class BaseModel(metaclass=_model_construction.ModelMetaclass):
-    """Usage docs: https://docs.pydantic.dev/2.9/concepts/models/
-
-    A base class for creating Pydantic models.
-
-    Attributes:
-        __class_vars__: The names of the class variables defined on the model.
-        __private_attributes__: Metadata about the private attributes of the model.
-        __signature__: The synthesized `__init__` [`Signature`][inspect.Signature] of the model.
-
-        __pydantic_complete__: Whether model building is completed, or if there are still undefined fields.
-        __pydantic_core_schema__: The core schema of the model.
-        __pydantic_custom_init__: Whether the model has a custom `__init__` function.
-        __pydantic_decorators__: Metadata containing the decorators defined on the model.
-            This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.
-        __pydantic_generic_metadata__: Metadata for generic models; contains data used for a similar purpose to
-            __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.
-        __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models.
-        __pydantic_post_init__: The name of the post-init method for the model, if defined.
-        __pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel].
-        __pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model.
-        __pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model.
-
-        __pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra]
-            is set to `'allow'`.
-        __pydantic_fields_set__: The names of fields explicitly set during instantiation.
-        __pydantic_private__: Values of private attributes set on the model instance.
-    """
-
-    # Class attributes:
-    # `model_fields` and `__pydantic_decorators__` must be set for
-    # `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation.
-
-    model_config: ClassVar[ConfigDict] = ConfigDict()
-    """
-    Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
-    """
-
-    # Because `dict` is in the local namespace of the `BaseModel` class, we use `Dict` for annotations.
-    # TODO v3 fallback to `dict` when the deprecated `dict` method gets removed.
-    model_fields: ClassVar[Dict[str, FieldInfo]] = {}  # noqa: UP006
-    """
-    Metadata about the fields defined on the model,
-    mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo] objects.
-
-    This replaces `Model.__fields__` from Pydantic V1.
-    """
-
-    model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}  # noqa: UP006
-    """A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects."""
-
-    __class_vars__: ClassVar[set[str]]
-    """The names of the class variables defined on the model."""
-
-    __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]]  # noqa: UP006
-    """Metadata about the private attributes of the model."""
-
-    __signature__: ClassVar[Signature]
-    """The synthesized `__init__` [`Signature`][inspect.Signature] of the model."""
-
-    __pydantic_complete__: ClassVar[bool] = False
-    """Whether model building is completed, or if there are still undefined fields."""
-
-    __pydantic_core_schema__: ClassVar[CoreSchema]
-    """The core schema of the model."""
-
-    __pydantic_custom_init__: ClassVar[bool]
-    """Whether the model has a custom `__init__` method."""
-
-    __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = _decorators.DecoratorInfos()
-    """Metadata containing the decorators defined on the model.
-    This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1."""
-
-    __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata]
-    """Metadata for generic models; contains data used for a similar purpose to
-    __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these."""
-
-    __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None  # noqa: UP006
-    """Parent namespace of the model, used for automatic rebuilding of models."""
-
-    __pydantic_post_init__: ClassVar[None | Literal['model_post_init']]
-    """The name of the post-init method for the model, if defined."""
-
-    __pydantic_root_model__: ClassVar[bool] = False
-    """Whether the model is a [`RootModel`][pydantic.root_model.RootModel]."""
-
-    __pydantic_serializer__: ClassVar[SchemaSerializer]
-    """The `pydantic-core` `SchemaSerializer` used to dump instances of the model."""
-
-    __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator]
-    """The `pydantic-core` `SchemaValidator` used to validate instances of the model."""
-
-    __pydantic_extra__: dict[str, Any] | None = _model_construction.NoInitField(init=False)
-    """A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`."""
-
-    __pydantic_fields_set__: set[str] = _model_construction.NoInitField(init=False)
-    """The names of fields explicitly set during instantiation."""
-
-    __pydantic_private__: dict[str, Any] | None = _model_construction.NoInitField(init=False)
-    """Values of private attributes set on the model instance."""
-
-    if not TYPE_CHECKING:
-        # Prevent `BaseModel` from being instantiated directly
-        # (defined in an `if not TYPE_CHECKING` block for clarity and to avoid type checking errors):
-        __pydantic_core_schema__ = _mock_val_ser.MockCoreSchema(
-            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
-            code='base-model-instantiated',
-        )
-        __pydantic_validator__ = _mock_val_ser.MockValSer(
-            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
-            val_or_ser='validator',
-            code='base-model-instantiated',
-        )
-        __pydantic_serializer__ = _mock_val_ser.MockValSer(
-            'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly',
-            val_or_ser='serializer',
-            code='base-model-instantiated',
-        )
-
-    __slots__ = '__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__'
-
-    def __init__(self, /, **data: Any) -> None:
-        """Create a new model by parsing and validating input data from keyword arguments.
-
-        Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
-        validated to form a valid model.
-
-        `self` is explicitly positional-only to allow `self` as a field name.
-        """
-        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
-        __tracebackhide__ = True
-        validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
-        if self is not validated_self:
-            warnings.warn(
-                'A custom validator is returning a value other than `self`.\n'
-                "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n"
-                'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.',
-                category=None,
-            )
-
-    # The following line sets a flag that we use to determine when `__init__` gets overridden by the user
-    __init__.__pydantic_base_init__ = True  # pyright: ignore[reportFunctionMemberAccess]
-
-    @property
-    def model_extra(self) -> dict[str, Any] | None:
-        """Get extra fields set during validation.
-
-        Returns:
-            A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`.
-        """
-        return self.__pydantic_extra__
-
-    @property
-    def model_fields_set(self) -> set[str]:
-        """Returns the set of fields that have been explicitly set on this model instance.
-
-        Returns:
-            A set of strings representing the fields that have been set,
-                i.e. that were not filled from defaults.
-        """
-        return self.__pydantic_fields_set__
-
-    @classmethod
-    def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: C901
-        """Creates a new instance of the `Model` class with validated data.
-
-        Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data.
-        Default values are respected, but no other validation is performed.
-
-        !!! note
-            `model_construct()` generally respects the `model_config.extra` setting on the provided model.
-            That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__`
-            and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored.
-            Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in
-            an error if extra values are passed, but they will be ignored.
-
-        Args:
-            _fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
-                this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute.
-                Otherwise, the field names from the `values` argument will be used.
-            values: Trusted or pre-validated data dictionary.
-
-        Returns:
-            A new instance of the `Model` class with validated data.
-        """
-        m = cls.__new__(cls)
-        fields_values: dict[str, Any] = {}
-        fields_set = set()
-
-        for name, field in cls.model_fields.items():
-            if field.alias is not None and field.alias in values:
-                fields_values[name] = values.pop(field.alias)
-                fields_set.add(name)
-
-            if (name not in fields_set) and (field.validation_alias is not None):
-                validation_aliases: list[str | AliasPath] = (
-                    field.validation_alias.choices
-                    if isinstance(field.validation_alias, AliasChoices)
-                    else [field.validation_alias]
-                )
-
-                for alias in validation_aliases:
-                    if isinstance(alias, str) and alias in values:
-                        fields_values[name] = values.pop(alias)
-                        fields_set.add(name)
-                        break
-                    elif isinstance(alias, AliasPath):
-                        value = alias.search_dict_for_path(values)
-                        if value is not PydanticUndefined:
-                            fields_values[name] = value
-                            fields_set.add(name)
-                            break
-
-            if name not in fields_set:
-                if name in values:
-                    fields_values[name] = values.pop(name)
-                    fields_set.add(name)
-                elif not field.is_required():
-                    fields_values[name] = field.get_default(call_default_factory=True)
-        if _fields_set is None:
-            _fields_set = fields_set
-
-        _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None
-        _object_setattr(m, '__dict__', fields_values)
-        _object_setattr(m, '__pydantic_fields_set__', _fields_set)
-        if not cls.__pydantic_root_model__:
-            _object_setattr(m, '__pydantic_extra__', _extra)
-
-        if cls.__pydantic_post_init__:
-            m.model_post_init(None)
-            # update private attributes with values set
-            if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None:
-                for k, v in values.items():
-                    if k in m.__private_attributes__:
-                        m.__pydantic_private__[k] = v
-
-        elif not cls.__pydantic_root_model__:
-            # Note: if there are any private attributes, cls.__pydantic_post_init__ would exist
-            # Since it doesn't, that means that `__pydantic_private__` should be set to None
-            _object_setattr(m, '__pydantic_private__', None)
-
-        return m
-
-    def model_copy(self, *, update: dict[str, Any] | None = None, deep: bool = False) -> Self:
-        """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
-
-        Returns a copy of the model.
-
-        Args:
-            update: Values to change/add in the new model. Note: the data is not validated
-                before creating the new model. You should trust this data.
-            deep: Set to `True` to make a deep copy of the model.
-
-        Returns:
-            New model instance.
-        """
-        copied = self.__deepcopy__() if deep else self.__copy__()
-        if update:
-            if self.model_config.get('extra') == 'allow':
-                for k, v in update.items():
-                    if k in self.model_fields:
-                        copied.__dict__[k] = v
-                    else:
-                        if copied.__pydantic_extra__ is None:
-                            copied.__pydantic_extra__ = {}
-                        copied.__pydantic_extra__[k] = v
-            else:
-                copied.__dict__.update(update)
-            copied.__pydantic_fields_set__.update(update.keys())
-        return copied
-
-    def model_dump(
-        self,
-        *,
-        mode: Literal['json', 'python'] | str = 'python',
-        include: IncEx | None = None,
-        exclude: IncEx | None = None,
-        context: Any | None = None,
-        by_alias: bool = False,
-        exclude_unset: bool = False,
-        exclude_defaults: bool = False,
-        exclude_none: bool = False,
-        round_trip: bool = False,
-        warnings: bool | Literal['none', 'warn', 'error'] = True,
-        serialize_as_any: bool = False,
-    ) -> dict[str, Any]:
-        """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
-
-        Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
-
-        Args:
-            mode: The mode in which `to_python` should run.
-                If mode is 'json', the output will only contain JSON serializable types.
-                If mode is 'python', the output may contain non-JSON-serializable Python objects.
-            include: A set of fields to include in the output.
-            exclude: A set of fields to exclude from the output.
-            context: Additional context to pass to the serializer.
-            by_alias: Whether to use the field's alias in the dictionary key if defined.
-            exclude_unset: Whether to exclude fields that have not been explicitly set.
-            exclude_defaults: Whether to exclude fields that are set to their default value.
-            exclude_none: Whether to exclude fields that have a value of `None`.
-            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
-            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
-                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
-            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
-
-        Returns:
-            A dictionary representation of the model.
-        """
-        return self.__pydantic_serializer__.to_python(
-            self,
-            mode=mode,
-            by_alias=by_alias,
-            include=include,
-            exclude=exclude,
-            context=context,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-            round_trip=round_trip,
-            warnings=warnings,
-            serialize_as_any=serialize_as_any,
-        )
-
-    def model_dump_json(
-        self,
-        *,
-        indent: int | None = None,
-        include: IncEx | None = None,
-        exclude: IncEx | None = None,
-        context: Any | None = None,
-        by_alias: bool = False,
-        exclude_unset: bool = False,
-        exclude_defaults: bool = False,
-        exclude_none: bool = False,
-        round_trip: bool = False,
-        warnings: bool | Literal['none', 'warn', 'error'] = True,
-        serialize_as_any: bool = False,
-    ) -> str:
-        """Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json
-
-        Generates a JSON representation of the model using Pydantic's `to_json` method.
-
-        Args:
-            indent: Indentation to use in the JSON output. If None is passed, the output will be compact.
-            include: Field(s) to include in the JSON output.
-            exclude: Field(s) to exclude from the JSON output.
-            context: Additional context to pass to the serializer.
-            by_alias: Whether to serialize using field aliases.
-            exclude_unset: Whether to exclude fields that have not been explicitly set.
-            exclude_defaults: Whether to exclude fields that are set to their default value.
-            exclude_none: Whether to exclude fields that have a value of `None`.
-            round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
-            warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
-                "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
-            serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
-
-        Returns:
-            A JSON string representation of the model.
-        """
-        return self.__pydantic_serializer__.to_json(
-            self,
-            indent=indent,
-            include=include,
-            exclude=exclude,
-            context=context,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-            round_trip=round_trip,
-            warnings=warnings,
-            serialize_as_any=serialize_as_any,
-        ).decode()
-
-    @classmethod
-    def model_json_schema(
-        cls,
-        by_alias: bool = True,
-        ref_template: str = DEFAULT_REF_TEMPLATE,
-        schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema,
-        mode: JsonSchemaMode = 'validation',
-    ) -> dict[str, Any]:
-        """Generates a JSON schema for a model class.
-
-        Args:
-            by_alias: Whether to use attribute aliases or not.
-            ref_template: The reference template.
-            schema_generator: To override the logic used to generate the JSON schema, as a subclass of
-                `GenerateJsonSchema` with your desired modifications
-            mode: The mode in which to generate the schema.
-
-        Returns:
-            The JSON schema for the given model class.
-        """
-        return model_json_schema(
-            cls, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator, mode=mode
-        )
-
-    @classmethod
-    def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str:
-        """Compute the class name for parametrizations of generic classes.
-
-        This method can be overridden to achieve a custom naming scheme for generic BaseModels.
-
-        Args:
-            params: Tuple of types of the class. Given a generic class
-                `Model` with 2 type variables and a concrete model `Model[str, int]`,
-                the value `(str, int)` would be passed to `params`.
-
-        Returns:
-            String representing the new class where `params` are passed to `cls` as type variables.
-
-        Raises:
-            TypeError: Raised when trying to generate concrete names for non-generic models.
-        """
-        if not issubclass(cls, typing.Generic):
-            raise TypeError('Concrete names should only be generated for generic models.')
-
-        # Any strings received should represent forward references, so we handle them specially below.
-        # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future,
-        # we may be able to remove this special case.
-        param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params]
-        params_component = ', '.join(param_names)
-        return f'{cls.__name__}[{params_component}]'
-
-    def model_post_init(self, __context: Any) -> None:
-        """Override this method to perform additional initialization after `__init__` and `model_construct`.
-        This is useful if you want to do some validation that requires the entire model to be initialized.
-        """
-        pass
-
-    @classmethod
-    def model_rebuild(
-        cls,
-        *,
-        force: bool = False,
-        raise_errors: bool = True,
-        _parent_namespace_depth: int = 2,
-        _types_namespace: dict[str, Any] | None = None,
-    ) -> bool | None:
-        """Try to rebuild the pydantic-core schema for the model.
-
-        This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
-        the initial attempt to build the schema, and automatic rebuilding fails.
-
-        Args:
-            force: Whether to force the rebuilding of the model schema, defaults to `False`.
-            raise_errors: Whether to raise errors, defaults to `True`.
-            _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
-            _types_namespace: The types namespace, defaults to `None`.
-
-        Returns:
-            Returns `None` if the schema is already "complete" and rebuilding was not required.
-            If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
-        """
-        if not force and cls.__pydantic_complete__:
-            return None
-        else:
-            if '__pydantic_core_schema__' in cls.__dict__:
-                delattr(cls, '__pydantic_core_schema__')  # delete cached value to ensure full rebuild happens
-            if _types_namespace is not None:
-                types_namespace: dict[str, Any] | None = _types_namespace.copy()
-            else:
-                if _parent_namespace_depth > 0:
-                    frame_parent_ns = (
-                        _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
-                    )
-                    cls_parent_ns = (
-                        _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {}
-                    )
-                    types_namespace = {**cls_parent_ns, **frame_parent_ns}
-                    cls.__pydantic_parent_namespace__ = _model_construction.build_lenient_weakvaluedict(types_namespace)
-                else:
-                    types_namespace = _model_construction.unpack_lenient_weakvaluedict(
-                        cls.__pydantic_parent_namespace__
-                    )
-
-                types_namespace = _typing_extra.merge_cls_and_parent_ns(cls, types_namespace)
-
-            # manually override defer_build so complete_model_class doesn't skip building the model again
-            config = {**cls.model_config, 'defer_build': False}
-            return _model_construction.complete_model_class(
-                cls,
-                cls.__name__,
-                _config.ConfigWrapper(config, check=False),
-                raise_errors=raise_errors,
-                types_namespace=types_namespace,
-            )
-
-    @classmethod
-    def model_validate(
-        cls,
-        obj: Any,
-        *,
-        strict: bool | None = None,
-        from_attributes: bool | None = None,
-        context: Any | None = None,
-    ) -> Self:
-        """Validate a pydantic model instance.
-
-        Args:
-            obj: The object to validate.
-            strict: Whether to enforce types strictly.
-            from_attributes: Whether to extract data from object attributes.
-            context: Additional context to pass to the validator.
-
-        Raises:
-            ValidationError: If the object could not be validated.
-
-        Returns:
-            The validated model instance.
-        """
-        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
-        __tracebackhide__ = True
-        return cls.__pydantic_validator__.validate_python(
-            obj, strict=strict, from_attributes=from_attributes, context=context
-        )
-
-    @classmethod
-    def model_validate_json(
-        cls,
-        json_data: str | bytes | bytearray,
-        *,
-        strict: bool | None = None,
-        context: Any | None = None,
-    ) -> Self:
-        """Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing
-
-        Validate the given JSON data against the Pydantic model.
-
-        Args:
-            json_data: The JSON data to validate.
-            strict: Whether to enforce types strictly.
-            context: Extra variables to pass to the validator.
-
-        Returns:
-            The validated Pydantic model.
-
-        Raises:
-            ValidationError: If `json_data` is not a JSON string or the object could not be validated.
-        """
-        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
-        __tracebackhide__ = True
-        return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context)
-
-    @classmethod
-    def model_validate_strings(
-        cls,
-        obj: Any,
-        *,
-        strict: bool | None = None,
-        context: Any | None = None,
-    ) -> Self:
-        """Validate the given object with string data against the Pydantic model.
-
-        Args:
-            obj: The object containing string data to validate.
-            strict: Whether to enforce types strictly.
-            context: Extra variables to pass to the validator.
-
-        Returns:
-            The validated Pydantic model.
-        """
-        # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
-        __tracebackhide__ = True
-        return cls.__pydantic_validator__.validate_strings(obj, strict=strict, context=context)
-
-    @classmethod
-    def __get_pydantic_core_schema__(cls, source: type[BaseModel], handler: GetCoreSchemaHandler, /) -> CoreSchema:
-        """Hook into generating the model's CoreSchema.
-
-        Args:
-            source: The class we are generating a schema for.
-                This will generally be the same as the `cls` argument if this is a classmethod.
-            handler: A callable that calls into Pydantic's internal CoreSchema generation logic.
-
-        Returns:
-            A `pydantic-core` `CoreSchema`.
-        """
-        # Only use the cached value from this _exact_ class; we don't want one from a parent class
-        # This is why we check `cls.__dict__` and don't use `cls.__pydantic_core_schema__` or similar.
-        schema = cls.__dict__.get('__pydantic_core_schema__')
-        if schema is not None and not isinstance(schema, _mock_val_ser.MockCoreSchema):
-            # Due to the way generic classes are built, it's possible that an invalid schema may be temporarily
-            # set on generic classes. I think we could resolve this to ensure that we get proper schema caching
-            # for generics, but for simplicity for now, we just always rebuild if the class has a generic origin.
-            if not cls.__pydantic_generic_metadata__['origin']:
-                return cls.__pydantic_core_schema__
-
-        return handler(source)
-
-    @classmethod
-    def __get_pydantic_json_schema__(
-        cls,
-        core_schema: CoreSchema,
-        handler: GetJsonSchemaHandler,
-        /,
-    ) -> JsonSchemaValue:
-        """Hook into generating the model's JSON schema.
-
-        Args:
-            core_schema: A `pydantic-core` CoreSchema.
-                You can ignore this argument and call the handler with a new CoreSchema,
-                wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`),
-                or just call the handler with the original schema.
-            handler: Call into Pydantic's internal JSON schema generation.
-                This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema
-                generation fails.
-                Since this gets called by `BaseModel.model_json_schema` you can override the
-                `schema_generator` argument to that function to change JSON schema generation globally
-                for a type.
-
-        Returns:
-            A JSON schema, as a Python object.
-        """
-        return handler(core_schema)
-
-    @classmethod
-    def __pydantic_init_subclass__(cls, **kwargs: Any) -> None:
-        """This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass`
-        only after the class is actually fully initialized. In particular, attributes like `model_fields` will
-        be present when this is called.
-
-        This is necessary because `__init_subclass__` will always be called by `type.__new__`,
-        and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that
-        `type.__new__` was called in such a manner that the class would already be sufficiently initialized.
-
-        This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely,
-        any kwargs passed to the class definition that aren't used internally by pydantic.
-
-        Args:
-            **kwargs: Any keyword arguments passed to the class definition that aren't used internally
-                by pydantic.
-        """
-        pass
-
-    def __class_getitem__(
-        cls, typevar_values: type[Any] | tuple[type[Any], ...]
-    ) -> type[BaseModel] | _forward_ref.PydanticRecursiveRef:
-        cached = _generics.get_cached_generic_type_early(cls, typevar_values)
-        if cached is not None:
-            return cached
-
-        if cls is BaseModel:
-            raise TypeError('Type parameters should be placed on typing.Generic, not BaseModel')
-        if not hasattr(cls, '__parameters__'):
-            raise TypeError(f'{cls} cannot be parametrized because it does not inherit from typing.Generic')
-        if not cls.__pydantic_generic_metadata__['parameters'] and typing.Generic not in cls.__bases__:
-            raise TypeError(f'{cls} is not a generic class')
-
-        if not isinstance(typevar_values, tuple):
-            typevar_values = (typevar_values,)
-        _generics.check_parameters_count(cls, typevar_values)
-
-        # Build map from generic typevars to passed params
-        typevars_map: dict[_typing_extra.TypeVarType, type[Any]] = dict(
-            zip(cls.__pydantic_generic_metadata__['parameters'], typevar_values)
-        )
-
-        if _utils.all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map:
-            submodel = cls  # if arguments are equal to parameters it's the same object
-            _generics.set_cached_generic_type(cls, typevar_values, submodel)
-        else:
-            parent_args = cls.__pydantic_generic_metadata__['args']
-            if not parent_args:
-                args = typevar_values
-            else:
-                args = tuple(_generics.replace_types(arg, typevars_map) for arg in parent_args)
-
-            origin = cls.__pydantic_generic_metadata__['origin'] or cls
-            model_name = origin.model_parametrized_name(args)
-            params = tuple(
-                {param: None for param in _generics.iter_contained_typevars(typevars_map.values())}
-            )  # use dict as ordered set
-
-            with _generics.generic_recursion_self_type(origin, args) as maybe_self_type:
-                if maybe_self_type is not None:
-                    return maybe_self_type
-
-                cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args)
-                if cached is not None:
-                    return cached
-
-                # Attempt to rebuild the origin in case new types have been defined
-                try:
-                    # depth 3 gets you above this __class_getitem__ call
-                    origin.model_rebuild(_parent_namespace_depth=3)
-                except PydanticUndefinedAnnotation:
-                    # It's okay if it fails, it just means there are still undefined types
-                    # that could be evaluated later.
-                    # TODO: Make sure validation fails if there are still undefined types, perhaps using MockValidator
-                    pass
-
-                submodel = _generics.create_generic_submodel(model_name, origin, args, params)
-
-                # Update cache
-                _generics.set_cached_generic_type(cls, typevar_values, submodel, origin, args)
-
-        return submodel
-
-    def __copy__(self) -> Self:
-        """Returns a shallow copy of the model."""
-        cls = type(self)
-        m = cls.__new__(cls)
-        _object_setattr(m, '__dict__', copy(self.__dict__))
-        _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__))
-        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
-
-        if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
-            _object_setattr(m, '__pydantic_private__', None)
-        else:
-            _object_setattr(
-                m,
-                '__pydantic_private__',
-                {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined},
-            )
-
-        return m
-
-    def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
-        """Returns a deep copy of the model."""
-        cls = type(self)
-        m = cls.__new__(cls)
-        _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
-        _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo))
-        # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
-        # and attempting a deepcopy would be marginally slower.
-        _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
-
-        if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None:
-            _object_setattr(m, '__pydantic_private__', None)
-        else:
-            _object_setattr(
-                m,
-                '__pydantic_private__',
-                deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo),
-            )
-
-        return m
-
-    if not TYPE_CHECKING:
-        # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access
-        # The same goes for __setattr__ and __delattr__, see: https://github.com/pydantic/pydantic/issues/8643
-
-        def __getattr__(self, item: str) -> Any:
-            private_attributes = object.__getattribute__(self, '__private_attributes__')
-            if item in private_attributes:
-                attribute = private_attributes[item]
-                if hasattr(attribute, '__get__'):
-                    return attribute.__get__(self, type(self))  # type: ignore
-
-                try:
-                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
-                    return self.__pydantic_private__[item]  # type: ignore
-                except KeyError as exc:
-                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
-            else:
-                # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
-                # See `BaseModel.__repr_args__` for more details
-                try:
-                    pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
-                except AttributeError:
-                    pydantic_extra = None
-
-                if pydantic_extra:
-                    try:
-                        return pydantic_extra[item]
-                    except KeyError as exc:
-                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
-                else:
-                    if hasattr(self.__class__, item):
-                        return super().__getattribute__(item)  # Raises AttributeError if appropriate
-                    else:
-                        # this is the current error
-                        raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
-
-        def __setattr__(self, name: str, value: Any) -> None:
-            if name in self.__class_vars__:
-                raise AttributeError(
-                    f'{name!r} is a ClassVar of `{self.__class__.__name__}` and cannot be set on an instance. '
-                    f'If you want to set a value on the class, use `{self.__class__.__name__}.{name} = value`.'
-                )
-            elif not _fields.is_valid_field_name(name):
-                if self.__pydantic_private__ is None or name not in self.__private_attributes__:
-                    _object_setattr(self, name, value)
-                else:
-                    attribute = self.__private_attributes__[name]
-                    if hasattr(attribute, '__set__'):
-                        attribute.__set__(self, value)  # type: ignore
-                    else:
-                        self.__pydantic_private__[name] = value
-                return
-
-            self._check_frozen(name, value)
-
-            attr = getattr(self.__class__, name, None)
-            if isinstance(attr, property):
-                attr.__set__(self, value)
-            elif self.model_config.get('validate_assignment', None):
-                self.__pydantic_validator__.validate_assignment(self, name, value)
-            elif self.model_config.get('extra') != 'allow' and name not in self.model_fields:
-                # TODO - matching error
-                raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
-            elif self.model_config.get('extra') == 'allow' and name not in self.model_fields:
-                if self.model_extra and name in self.model_extra:
-                    self.__pydantic_extra__[name] = value  # type: ignore
-                else:
-                    try:
-                        getattr(self, name)
-                    except AttributeError:
-                        # attribute does not already exist on instance, so put it in extra
-                        self.__pydantic_extra__[name] = value  # type: ignore
-                    else:
-                        # attribute _does_ already exist on instance, and was not in extra, so update it
-                        _object_setattr(self, name, value)
-            else:
-                self.__dict__[name] = value
-                self.__pydantic_fields_set__.add(name)
-
-        def __delattr__(self, item: str) -> Any:
-            if item in self.__private_attributes__:
-                attribute = self.__private_attributes__[item]
-                if hasattr(attribute, '__delete__'):
-                    attribute.__delete__(self)  # type: ignore
-                    return
-
-                try:
-                    # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items
-                    del self.__pydantic_private__[item]  # type: ignore
-                    return
-                except KeyError as exc:
-                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc
-
-            self._check_frozen(item, None)
-
-            if item in self.model_fields:
-                object.__delattr__(self, item)
-            elif self.__pydantic_extra__ is not None and item in self.__pydantic_extra__:
-                del self.__pydantic_extra__[item]
-            else:
-                try:
-                    object.__delattr__(self, item)
-                except AttributeError:
-                    raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')
-
-    def _check_frozen(self, name: str, value: Any) -> None:
-        if self.model_config.get('frozen', None):
-            typ = 'frozen_instance'
-        elif getattr(self.model_fields.get(name), 'frozen', False):
-            typ = 'frozen_field'
-        else:
-            return
-        error: pydantic_core.InitErrorDetails = {
-            'type': typ,
-            'loc': (name,),
-            'input': value,
-        }
-        raise pydantic_core.ValidationError.from_exception_data(self.__class__.__name__, [error])
-
-    def __getstate__(self) -> dict[Any, Any]:
-        private = self.__pydantic_private__
-        if private:
-            private = {k: v for k, v in private.items() if v is not PydanticUndefined}
-        return {
-            '__dict__': self.__dict__,
-            '__pydantic_extra__': self.__pydantic_extra__,
-            '__pydantic_fields_set__': self.__pydantic_fields_set__,
-            '__pydantic_private__': private,
-        }
-
-    def __setstate__(self, state: dict[Any, Any]) -> None:
-        _object_setattr(self, '__pydantic_fields_set__', state.get('__pydantic_fields_set__', {}))
-        _object_setattr(self, '__pydantic_extra__', state.get('__pydantic_extra__', {}))
-        _object_setattr(self, '__pydantic_private__', state.get('__pydantic_private__', {}))
-        _object_setattr(self, '__dict__', state.get('__dict__', {}))
-
-    if not TYPE_CHECKING:
-
-        def __eq__(self, other: Any) -> bool:
-            if isinstance(other, BaseModel):
-                # When comparing instances of generic types for equality, as long as all field values are equal,
-                # only require their generic origin types to be equal, rather than exact type equality.
-                # This prevents headaches like MyGeneric(x=1) != MyGeneric[Any](x=1).
-                self_type = self.__pydantic_generic_metadata__['origin'] or self.__class__
-                other_type = other.__pydantic_generic_metadata__['origin'] or other.__class__
-
-                # Perform common checks first
-                if not (
-                    self_type == other_type
-                    and getattr(self, '__pydantic_private__', None) == getattr(other, '__pydantic_private__', None)
-                    and self.__pydantic_extra__ == other.__pydantic_extra__
-                ):
-                    return False
-
-                # We only want to compare pydantic fields but ignoring fields is costly.
-                # We'll perform a fast check first, and fallback only when needed
-                # See GH-7444 and GH-7825 for rationale and a performance benchmark
-
-                # First, do the fast (and sometimes faulty) __dict__ comparison
-                if self.__dict__ == other.__dict__:
-                    # If the check above passes, then pydantic fields are equal, we can return early
-                    return True
-
-                # We don't want to trigger unnecessary costly filtering of __dict__ on all unequal objects, so we return
-                # early if there are no keys to ignore (we would just return False later on anyway)
-                model_fields = type(self).model_fields.keys()
-                if self.__dict__.keys() <= model_fields and other.__dict__.keys() <= model_fields:
-                    return False
-
-                # If we reach here, there are non-pydantic-fields keys, mapped to unequal values, that we need to ignore
-                # Resort to costly filtering of the __dict__ objects
-                # We use operator.itemgetter because it is much faster than dict comprehensions
-                # NOTE: Contrary to standard python class and instances, when the Model class has a default value for an
-                # attribute and the model instance doesn't have a corresponding attribute, accessing the missing attribute
-                # raises an error in BaseModel.__getattr__ instead of returning the class attribute
-                # So we can use operator.itemgetter() instead of operator.attrgetter()
-                getter = operator.itemgetter(*model_fields) if model_fields else lambda _: _utils._SENTINEL
-                try:
-                    return getter(self.__dict__) == getter(other.__dict__)
-                except KeyError:
-                    # In rare cases (such as when using the deprecated BaseModel.copy() method),
-                    # the __dict__ may not contain all model fields, which is how we can get here.
-                    # getter(self.__dict__) is much faster than any 'safe' method that accounts
-                    # for missing keys, and wrapping it in a `try` doesn't slow things down much
-                    # in the common case.
-                    self_fields_proxy = _utils.SafeGetItemProxy(self.__dict__)
-                    other_fields_proxy = _utils.SafeGetItemProxy(other.__dict__)
-                    return getter(self_fields_proxy) == getter(other_fields_proxy)
-
-            # other instance is not a BaseModel
-            else:
-                return NotImplemented  # delegate to the other item in the comparison
-
-    if TYPE_CHECKING:
-        # We put `__init_subclass__` in a TYPE_CHECKING block because, even though we want the type-checking benefits
-        # described in the signature of `__init_subclass__` below, we don't want to modify the default behavior of
-        # subclass initialization.
-
-        def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]):
-            """This signature is included purely to help type-checkers check arguments to class declaration, which
-            provides a way to conveniently set model_config key/value pairs.
-
-            ```py
-            from pydantic import BaseModel
-
-            class MyModel(BaseModel, extra='allow'): ...
-            ```
-
-            However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any
-            of the config arguments, and will only receive any keyword arguments passed during class initialization
-            that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.)
-
-            Args:
-                **kwargs: Keyword arguments passed to the class definition, which set model_config
-
-            Note:
-                You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called
-                *after* the class is fully initialized.
-            """
-
-    def __iter__(self) -> TupleGenerator:
-        """So `dict(model)` works."""
-        yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')]
-        extra = self.__pydantic_extra__
-        if extra:
-            yield from extra.items()
-
-    def __repr__(self) -> str:
-        return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
-
-    def __repr_args__(self) -> _repr.ReprArgs:
-        for k, v in self.__dict__.items():
-            field = self.model_fields.get(k)
-            if field and field.repr:
-                yield k, v
-
-        # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized.
-        # This can happen if a `ValidationError` is raised during initialization and the instance's
-        # repr is generated as part of the exception handling. Therefore, we use `getattr` here
-        # with a fallback, even though the type hints indicate the attribute will always be present.
-        try:
-            pydantic_extra = object.__getattribute__(self, '__pydantic_extra__')
-        except AttributeError:
-            pydantic_extra = None
-
-        if pydantic_extra is not None:
-            yield from ((k, v) for k, v in pydantic_extra.items())
-        yield from ((k, getattr(self, k)) for k, v in self.model_computed_fields.items() if v.repr)
-
-    # take logic from `_repr.Representation` without the side effects of inheritance, see #5740
-    __repr_name__ = _repr.Representation.__repr_name__
-    __repr_str__ = _repr.Representation.__repr_str__
-    __pretty__ = _repr.Representation.__pretty__
-    __rich_repr__ = _repr.Representation.__rich_repr__
-
-    def __str__(self) -> str:
-        return self.__repr_str__(' ')
-
-    # ##### Deprecated methods from v1 #####
-    @property
-    @typing_extensions.deprecated(
-        'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=None
-    )
-    def __fields__(self) -> dict[str, FieldInfo]:
-        warnings.warn(
-            'The `__fields__` attribute is deprecated, use `model_fields` instead.', category=PydanticDeprecatedSince20
-        )
-        return self.model_fields
-
-    @property
-    @typing_extensions.deprecated(
-        'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
-        category=None,
-    )
-    def __fields_set__(self) -> set[str]:
-        warnings.warn(
-            'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.',
-            category=PydanticDeprecatedSince20,
-        )
-        return self.__pydantic_fields_set__
-
-    @typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None)
-    def dict(  # noqa: D102
-        self,
-        *,
-        include: IncEx | None = None,
-        exclude: IncEx | None = None,
-        by_alias: bool = False,
-        exclude_unset: bool = False,
-        exclude_defaults: bool = False,
-        exclude_none: bool = False,
-    ) -> Dict[str, Any]:  # noqa UP006
-        warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)
-        return self.model_dump(
-            include=include,
-            exclude=exclude,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-        )
-
-    @typing_extensions.deprecated('The `json` method is deprecated; use `model_dump_json` instead.', category=None)
-    def json(  # noqa: D102
-        self,
-        *,
-        include: IncEx | None = None,
-        exclude: IncEx | None = None,
-        by_alias: bool = False,
-        exclude_unset: bool = False,
-        exclude_defaults: bool = False,
-        exclude_none: bool = False,
-        encoder: Callable[[Any], Any] | None = PydanticUndefined,  # type: ignore[assignment]
-        models_as_dict: bool = PydanticUndefined,  # type: ignore[assignment]
-        **dumps_kwargs: Any,
-    ) -> str:
-        warnings.warn(
-            'The `json` method is deprecated; use `model_dump_json` instead.', category=PydanticDeprecatedSince20
-        )
-        if encoder is not PydanticUndefined:
-            raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.')
-        if models_as_dict is not PydanticUndefined:
-            raise TypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.')
-        if dumps_kwargs:
-            raise TypeError('`dumps_kwargs` keyword arguments are no longer supported.')
-        return self.model_dump_json(
-            include=include,
-            exclude=exclude,
-            by_alias=by_alias,
-            exclude_unset=exclude_unset,
-            exclude_defaults=exclude_defaults,
-            exclude_none=exclude_none,
-        )
-
-    @classmethod
-    @typing_extensions.deprecated('The `parse_obj` method is deprecated; use `model_validate` instead.', category=None)
-    def parse_obj(cls, obj: Any) -> Self:  # noqa: D102
-        warnings.warn(
-            'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
-        )
-        return cls.model_validate(obj)
-
-    @classmethod
-    @typing_extensions.deprecated(
-        'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
-        'otherwise load the data then use `model_validate` instead.',
-        category=None,
-    )
-    def parse_raw(  # noqa: D102
-        cls,
-        b: str | bytes,
-        *,
-        content_type: str | None = None,
-        encoding: str = 'utf8',
-        proto: DeprecatedParseProtocol | None = None,
-        allow_pickle: bool = False,
-    ) -> Self:  # pragma: no cover
-        warnings.warn(
-            'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, '
-            'otherwise load the data then use `model_validate` instead.',
-            category=PydanticDeprecatedSince20,
-        )
-        from .deprecated import parse
-
-        try:
-            obj = parse.load_str_bytes(
-                b,
-                proto=proto,
-                content_type=content_type,
-                encoding=encoding,
-                allow_pickle=allow_pickle,
-            )
-        except (ValueError, TypeError) as exc:
-            import json
-
-            # try to match V1
-            if isinstance(exc, UnicodeDecodeError):
-                type_str = 'value_error.unicodedecode'
-            elif isinstance(exc, json.JSONDecodeError):
-                type_str = 'value_error.jsondecode'
-            elif isinstance(exc, ValueError):
-                type_str = 'value_error'
-            else:
-                type_str = 'type_error'
-
-            # ctx is missing here, but since we've added `input` to the error, we're not pretending it's the same
-            error: pydantic_core.InitErrorDetails = {
-                # The type: ignore on the next line is to ignore the requirement of LiteralString
-                'type': pydantic_core.PydanticCustomError(type_str, str(exc)),  # type: ignore
-                'loc': ('__root__',),
-                'input': b,
-            }
-            raise pydantic_core.ValidationError.from_exception_data(cls.__name__, [error])
-        return cls.model_validate(obj)
-
-    @classmethod
-    @typing_extensions.deprecated(
-        'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
-        'use `model_validate_json`, otherwise `model_validate` instead.',
-        category=None,
-    )
-    def parse_file(  # noqa: D102
-        cls,
-        path: str | Path,
-        *,
-        content_type: str | None = None,
-        encoding: str = 'utf8',
-        proto: DeprecatedParseProtocol | None = None,
-        allow_pickle: bool = False,
-    ) -> Self:
-        warnings.warn(
-            'The `parse_file` method is deprecated; load the data from file, then if your data is JSON '
-            'use `model_validate_json`, otherwise `model_validate` instead.',
-            category=PydanticDeprecatedSince20,
-        )
-        from .deprecated import parse
-
-        obj = parse.load_file(
-            path,
-            proto=proto,
-            content_type=content_type,
-            encoding=encoding,
-            allow_pickle=allow_pickle,
-        )
-        return cls.parse_obj(obj)
-
-    @classmethod
-    @typing_extensions.deprecated(
-        'The `from_orm` method is deprecated; set '
-        "`model_config['from_attributes']=True` and use `model_validate` instead.",
-        category=None,
-    )
-    def from_orm(cls, obj: Any) -> Self:  # noqa: D102
-        warnings.warn(
-            'The `from_orm` method is deprecated; set '
-            "`model_config['from_attributes']=True` and use `model_validate` instead.",
-            category=PydanticDeprecatedSince20,
-        )
-        if not cls.model_config.get('from_attributes', None):
-            raise PydanticUserError(
-                'You must set the config attribute `from_attributes=True` to use from_orm', code=None
-            )
-        return cls.model_validate(obj)
-
-    @classmethod
-    @typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.', category=None)
-    def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self:  # noqa: D102
-        warnings.warn(
-            'The `construct` method is deprecated; use `model_construct` instead.', category=PydanticDeprecatedSince20
-        )
-        return cls.model_construct(_fields_set=_fields_set, **values)
-
-    @typing_extensions.deprecated(
-        'The `copy` method is deprecated; use `model_copy` instead. '
-        'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
-        category=None,
-    )
-    def copy(
-        self,
-        *,
-        include: AbstractSetIntStr | MappingIntStrAny | None = None,
-        exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
-        update: Dict[str, Any] | None = None,  # noqa UP006
-        deep: bool = False,
-    ) -> Self:  # pragma: no cover
-        """Returns a copy of the model.
-
-        !!! warning "Deprecated"
-            This method is now deprecated; use `model_copy` instead.
-
-        If you need `include` or `exclude`, use:
-
-        ```py
-        data = self.model_dump(include=include, exclude=exclude, round_trip=True)
-        data = {**data, **(update or {})}
-        copied = self.model_validate(data)
-        ```
-
-        Args:
-            include: Optional set or mapping specifying which fields to include in the copied model.
-            exclude: Optional set or mapping specifying which fields to exclude in the copied model.
-            update: Optional dictionary of field-value pairs to override field values in the copied model.
-            deep: If True, the values of fields that are Pydantic models will be deep-copied.
-
-        Returns:
-            A copy of the model with included, excluded and updated fields as specified.
-        """
-        warnings.warn(
-            'The `copy` method is deprecated; use `model_copy` instead. '
-            'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.',
-            category=PydanticDeprecatedSince20,
-        )
-        from .deprecated import copy_internals
-
-        values = dict(
-            copy_internals._iter(
-                self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False
-            ),
-            **(update or {}),
-        )
-        if self.__pydantic_private__ is None:
-            private = None
-        else:
-            private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}
-
-        if self.__pydantic_extra__ is None:
-            extra: dict[str, Any] | None = None
-        else:
-            extra = self.__pydantic_extra__.copy()
-            for k in list(self.__pydantic_extra__):
-                if k not in values:  # k was in the exclude
-                    extra.pop(k)
-            for k in list(values):
-                if k in self.__pydantic_extra__:  # k must have come from extra
-                    extra[k] = values.pop(k)
-
-        # new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg
-        if update:
-            fields_set = self.__pydantic_fields_set__ | update.keys()
-        else:
-            fields_set = set(self.__pydantic_fields_set__)
-
-        # removing excluded fields from `__pydantic_fields_set__`
-        if exclude:
-            fields_set -= set(exclude)
-
-        return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep)
-
-    @classmethod
-    @typing_extensions.deprecated('The `schema` method is deprecated; use `model_json_schema` instead.', category=None)
-    def schema(  # noqa: D102
-        cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE
-    ) -> Dict[str, Any]:  # noqa UP006
-        warnings.warn(
-            'The `schema` method is deprecated; use `model_json_schema` instead.', category=PydanticDeprecatedSince20
-        )
-        return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template)
-
-    @classmethod
-    @typing_extensions.deprecated(
-        'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
-        category=None,
-    )
-    def schema_json(  # noqa: D102
-        cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any
-    ) -> str:  # pragma: no cover
-        warnings.warn(
-            'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.',
-            category=PydanticDeprecatedSince20,
-        )
-        import json
-
-        from .deprecated.json import pydantic_encoder
-
-        return json.dumps(
-            cls.model_json_schema(by_alias=by_alias, ref_template=ref_template),
-            default=pydantic_encoder,
-            **dumps_kwargs,
-        )
-
-    @classmethod
-    @typing_extensions.deprecated('The `validate` method is deprecated; use `model_validate` instead.', category=None)
-    def validate(cls, value: Any) -> Self:  # noqa: D102
-        warnings.warn(
-            'The `validate` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
-        )
-        return cls.model_validate(value)
-
-    @classmethod
-    @typing_extensions.deprecated(
-        'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
-        category=None,
-    )
-    def update_forward_refs(cls, **localns: Any) -> None:  # noqa: D102
-        warnings.warn(
-            'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.',
-            category=PydanticDeprecatedSince20,
-        )
-        if localns:  # pragma: no cover
-            raise TypeError('`localns` arguments are not longer accepted.')
-        cls.model_rebuild(force=True)
-
-    @typing_extensions.deprecated(
-        'The private method `_iter` will be removed and should no longer be used.', category=None
-    )
-    def _iter(self, *args: Any, **kwargs: Any) -> Any:
-        warnings.warn(
-            'The private method `_iter` will be removed and should no longer be used.',
-            category=PydanticDeprecatedSince20,
-        )
-        from .deprecated import copy_internals
-
-        return copy_internals._iter(self, *args, **kwargs)
-
-    @typing_extensions.deprecated(
-        'The private method `_copy_and_set_values` will be removed and should no longer be used.',
-        category=None,
-    )
-    def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any:
-        warnings.warn(
-            'The private method `_copy_and_set_values` will be removed and should no longer be used.',
-            category=PydanticDeprecatedSince20,
-        )
-        from .deprecated import copy_internals
-
-        return copy_internals._copy_and_set_values(self, *args, **kwargs)
-
-    @classmethod
-    @typing_extensions.deprecated(
-        'The private method `_get_value` will be removed and should no longer be used.',
-        category=None,
-    )
-    def _get_value(cls, *args: Any, **kwargs: Any) -> Any:
-        warnings.warn(
-            'The private method `_get_value` will be removed and should no longer be used.',
-            category=PydanticDeprecatedSince20,
-        )
-        from .deprecated import copy_internals
-
-        return copy_internals._get_value(cls, *args, **kwargs)
-
-    @typing_extensions.deprecated(
-        'The private method `_calculate_keys` will be removed and should no longer be used.',
-        category=None,
-    )
-    def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any:
-        warnings.warn(
-            'The private method `_calculate_keys` will be removed and should no longer be used.',
-            category=PydanticDeprecatedSince20,
-        )
-        from .deprecated import copy_internals
-
-        return copy_internals._calculate_keys(self, *args, **kwargs)
-
-
-ModelT = TypeVar('ModelT', bound=BaseModel)
-
-
-@overload
-def create_model(
-    model_name: str,
-    /,
-    *,
-    __config__: ConfigDict | None = None,
-    __doc__: str | None = None,
-    __base__: None = None,
-    __module__: str = __name__,
-    __validators__: dict[str, Callable[..., Any]] | None = None,
-    __cls_kwargs__: dict[str, Any] | None = None,
-    **field_definitions: Any,
-) -> type[BaseModel]: ...
-
-
-@overload
-def create_model(
-    model_name: str,
-    /,
-    *,
-    __config__: ConfigDict | None = None,
-    __doc__: str | None = None,
-    __base__: type[ModelT] | tuple[type[ModelT], ...],
-    __module__: str = __name__,
-    __validators__: dict[str, Callable[..., Any]] | None = None,
-    __cls_kwargs__: dict[str, Any] | None = None,
-    **field_definitions: Any,
-) -> type[ModelT]: ...
-
-
-def create_model(  # noqa: C901
-    model_name: str,
-    /,
-    *,
-    __config__: ConfigDict | None = None,
-    __doc__: str | None = None,
-    __base__: type[ModelT] | tuple[type[ModelT], ...] | None = None,
-    __module__: str | None = None,
-    __validators__: dict[str, Callable[..., Any]] | None = None,
-    __cls_kwargs__: dict[str, Any] | None = None,
-    __slots__: tuple[str, ...] | None = None,
-    **field_definitions: Any,
-) -> type[ModelT]:
-    """Usage docs: https://docs.pydantic.dev/2.9/concepts/models/#dynamic-model-creation
-
-    Dynamically creates and returns a new Pydantic model, in other words, `create_model` dynamically creates a
-    subclass of [`BaseModel`][pydantic.BaseModel].
-
-    Args:
-        model_name: The name of the newly created model.
-        __config__: The configuration of the new model.
-        __doc__: The docstring of the new model.
-        __base__: The base class or classes for the new model.
-        __module__: The name of the module that the model belongs to;
-            if `None`, the value is taken from `sys._getframe(1)`
-        __validators__: A dictionary of methods that validate fields. The keys are the names of the validation methods to
-            be added to the model, and the values are the validation methods themselves. You can read more about functional
-            validators [here](https://docs.pydantic.dev/2.9/concepts/validators/#field-validators).
-        __cls_kwargs__: A dictionary of keyword arguments for class creation, such as `metaclass`.
-        __slots__: Deprecated. Should not be passed to `create_model`.
-        **field_definitions: Attributes of the new model. They should be passed in the format:
-            `<name>=(<type>, <default value>)`, `<name>=(<type>, <FieldInfo>)`, or `typing.Annotated[<type>, <FieldInfo>]`.
-            Any additional metadata in `typing.Annotated[<type>, <FieldInfo>, ...]` will be ignored.
-
-    Returns:
-        The new [model][pydantic.BaseModel].
-
-    Raises:
-        PydanticUserError: If `__base__` and `__config__` are both passed.
-    """
-    if __slots__ is not None:
-        # __slots__ will be ignored from here on
-        warnings.warn('__slots__ should not be passed to create_model', RuntimeWarning)
-
-    if __base__ is not None:
-        if __config__ is not None:
-            raise PydanticUserError(
-                'to avoid confusion `__config__` and `__base__` cannot be used together',
-                code='create-model-config-base',
-            )
-        if not isinstance(__base__, tuple):
-            __base__ = (__base__,)
-    else:
-        __base__ = (cast('type[ModelT]', BaseModel),)
-
-    __cls_kwargs__ = __cls_kwargs__ or {}
-
-    fields = {}
-    annotations = {}
-
-    for f_name, f_def in field_definitions.items():
-        if not _fields.is_valid_field_name(f_name):
-            warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning)
-        if isinstance(f_def, tuple):
-            f_def = cast('tuple[str, Any]', f_def)
-            try:
-                f_annotation, f_value = f_def
-            except ValueError as e:
-                raise PydanticUserError(
-                    'Field definitions should be a `(<type>, <default>)`.',
-                    code='create-model-field-definitions',
-                ) from e
-
-        elif _typing_extra.is_annotated(f_def):
-            (f_annotation, f_value, *_) = typing_extensions.get_args(
-                f_def
-            )  # first two input are expected from Annotated, refer to https://docs.python.org/3/library/typing.html#typing.Annotated
-            FieldInfo = _import_utils.import_cached_field_info()
-
-            if not isinstance(f_value, FieldInfo):
-                raise PydanticUserError(
-                    'Field definitions should be a Annotated[<type>, <FieldInfo>]',
-                    code='create-model-field-definitions',
-                )
-
-        else:
-            f_annotation, f_value = None, f_def
-
-        if f_annotation:
-            annotations[f_name] = f_annotation
-        fields[f_name] = f_value
-
-    if __module__ is None:
-        f = sys._getframe(1)
-        __module__ = f.f_globals['__name__']
-
-    namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__}
-    if __doc__:
-        namespace.update({'__doc__': __doc__})
-    if __validators__:
-        namespace.update(__validators__)
-    namespace.update(fields)
-    if __config__:
-        namespace['model_config'] = _config.ConfigWrapper(__config__).config_dict
-    resolved_bases = types.resolve_bases(__base__)
-    meta, ns, kwds = types.prepare_class(model_name, resolved_bases, kwds=__cls_kwargs__)
-    if resolved_bases is not __base__:
-        ns['__orig_bases__'] = __base__
-    namespace.update(ns)
-
-    return meta(
-        model_name,
-        resolved_bases,
-        namespace,
-        __pydantic_reset_parent_namespace__=False,
-        _create_model_module=__module__,
-        **kwds,
-    )
-
-
-__getattr__ = getattr_migration(__name__)
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Nov 22, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_modules/ragas/dataset_schema.html b/docs/_build/html/_modules/ragas/dataset_schema.html deleted file mode 100644 index 3d173bca..00000000 --- a/docs/_build/html/_modules/ragas/dataset_schema.html +++ /dev/null @@ -1,1014 +0,0 @@ - - - - - - ragas.dataset_schema — NotDiamond 0.3.43 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for ragas.dataset_schema

-from __future__ import annotations
-
-import json
-import random
-import typing as t
-from abc import ABC, abstractmethod
-from collections import defaultdict
-from dataclasses import dataclass, field
-from uuid import UUID
-
-import numpy as np
-import requests
-from datasets import Dataset as HFDataset
-from pydantic import BaseModel, field_validator
-
-from ragas._version import __version__
-from ragas.callbacks import ChainRunEncoder, parse_run_traces
-from ragas.cost import CostCallbackHandler
-from ragas.exceptions import UploadException
-from ragas.messages import AIMessage, HumanMessage, ToolCall, ToolMessage
-from ragas.sdk import (
-    RAGAS_API_SOURCE,
-    build_evaluation_app_url,
-    check_api_response,
-    get_api_url,
-    get_app_token,
-    get_app_url,
-    upload_packet,
-)
-from ragas.utils import safe_nanmean
-
-if t.TYPE_CHECKING:
-    from pathlib import Path
-
-    from datasets import Dataset as HFDataset
-    from pandas import DataFrame as PandasDataframe
-
-    from ragas.callbacks import ChainRun
-    from ragas.cost import TokenUsage
-
-
-class BaseSample(BaseModel):
-    """
-    Base class for evaluation samples.
-    """
-
-    def to_dict(self) -> t.Dict:
-        """
-        Get the dictionary representation of the sample without attributes that are None.
-        """
-        return self.model_dump(exclude_none=True)
-
-    def get_features(self) -> t.List[str]:
-        """
-        Get the features of the sample that are not None.
-        """
-        return list(self.to_dict().keys())
-
-    def to_string(self) -> str:
-        """
-        Get the string representation of the sample.
-        """
-        sample_dict = self.to_dict()
-        return "".join(f"\n{key}:\n\t{val}\n" for key, val in sample_dict.items())
-
-
-class SingleTurnSample(BaseSample):
-    """
-    Represents evaluation samples for single-turn interactions.
-
-    Attributes
-    ----------
-    user_input : Optional[str]
-        The input query from the user.
-    retrieved_contexts : Optional[List[str]]
-        List of contexts retrieved for the query.
-    reference_contexts : Optional[List[str]]
-        List of reference contexts for the query.
-    response : Optional[str]
-        The generated response for the query.
-    multi_responses : Optional[List[str]]
-        List of multiple responses generated for the query.
-    reference : Optional[str]
-        The reference answer for the query.
-    rubric : Optional[Dict[str, str]]
-        Evaluation rubric for the sample.
-    """
-
-    user_input: t.Optional[str] = None
-    retrieved_contexts: t.Optional[t.List[str]] = None
-    reference_contexts: t.Optional[t.List[str]] = None
-    response: t.Optional[str] = None
-    multi_responses: t.Optional[t.List[str]] = None
-    reference: t.Optional[str] = None
-    rubrics: t.Optional[t.Dict[str, str]] = None
-
-
-class MultiTurnSample(BaseSample):
-    """
-    Represents evaluation samples for multi-turn interactions.
-
-    Attributes
-    ----------
-    user_input : List[Union[HumanMessage, AIMessage, ToolMessage]]
-        A list of messages representing the conversation turns.
-    reference : Optional[str], optional
-        The reference answer or expected outcome for the conversation.
-    reference_tool_calls : Optional[List[ToolCall]], optional
-        A list of expected tool calls for the conversation.
-    rubrics : Optional[Dict[str, str]], optional
-        Evaluation rubrics for the conversation.
-    reference_topics : Optional[List[str]], optional
-        A list of reference topics for the conversation.
-    """
-
-    user_input: t.List[t.Union[HumanMessage, AIMessage, ToolMessage]]
-    reference: t.Optional[str] = None
-    reference_tool_calls: t.Optional[t.List[ToolCall]] = None
-    rubrics: t.Optional[t.Dict[str, str]] = None
-    reference_topics: t.Optional[t.List[str]] = None
-
-    @field_validator("user_input")
-    @classmethod
-    def validate_user_input(
-        cls,
-        messages: t.List[t.Union[HumanMessage, AIMessage, ToolMessage]],
-    ) -> t.List[t.Union[HumanMessage, AIMessage, ToolMessage]]:
-        """Validates the user input messages."""
-        if not (
-            isinstance(m, (HumanMessage, AIMessage, ToolMessage)) for m in messages
-        ):
-            raise ValueError(
-                "All inputs must be instances of HumanMessage, AIMessage, or ToolMessage."
-            )
-
-        prev_message = None
-        for m in messages:
-            if isinstance(m, ToolMessage):
-                if not isinstance(prev_message, AIMessage):
-                    raise ValueError(
-                        "ToolMessage instances must be preceded by an AIMessage instance."
-                    )
-                if prev_message.tool_calls is None:
-                    raise ValueError(
-                        f"ToolMessage instances must be preceded by an AIMessage instance with tool_calls. Got {prev_message}"
-                    )
-            prev_message = m
-
-        return messages
-
-    def to_messages(self):
-        """Converts the user input messages to a list of dictionaries."""
-        return [m.model_dump() for m in self.user_input]
-
-    def pretty_repr(self):
-        """Returns a pretty string representation of the conversation."""
-        lines = []
-        for m in self.user_input:
-            lines.append(m.pretty_repr())
-
-        return "\n".join(lines)
-
-
-Sample = t.TypeVar("Sample", bound=BaseSample)
-T = t.TypeVar("T", bound="RagasDataset")
-
-
-@dataclass
-class RagasDataset(ABC, t.Generic[Sample]):
-    samples: t.List[Sample]
-
-    def __post_init__(self):
-        self.samples = self.validate_samples(self.samples)
-
-    @abstractmethod
-    def to_list(self) -> t.List[t.Dict]:
-        """Converts the samples to a list of dictionaries."""
-        pass
-
-    @classmethod
-    @abstractmethod
-    def from_list(cls: t.Type[T], data: t.List[t.Dict]) -> T:
-        """Creates an RagasDataset from a list of dictionaries."""
-        pass
-
-    def validate_samples(self, samples: t.List[Sample]) -> t.List[Sample]:
-        """Validates that all samples are of the same type."""
-        if len(samples) == 0:
-            return samples
-
-        first_sample_type = type(samples[0])
-        for i, sample in enumerate(samples):
-            if not isinstance(sample, first_sample_type):
-                raise ValueError(
-                    f"Sample at index {i} is of type {type(sample)}, expected {first_sample_type}"
-                )
-
-        return samples
-
-    def get_sample_type(self) -> t.Type[Sample]:
-        """Returns the type of the samples in the dataset."""
-        return type(self.samples[0])
-
-    def to_hf_dataset(self) -> HFDataset:
-        """Converts the dataset to a Hugging Face Dataset."""
-        try:
-            from datasets import Dataset as HFDataset
-        except ImportError:
-            raise ImportError(
-                "datasets is not installed. Please install it to use this function."
-            )
-
-        return HFDataset.from_list(self.to_list())
-
-    @classmethod
-    def from_hf_dataset(cls: t.Type[T], dataset: HFDataset) -> T:
-        """Creates an EvaluationDataset from a Hugging Face Dataset."""
-        return cls.from_list(dataset.to_list())
-
-    def to_pandas(self) -> PandasDataframe:
-        """Converts the dataset to a pandas DataFrame."""
-        try:
-            import pandas as pd
-        except ImportError:
-            raise ImportError(
-                "pandas is not installed. Please install it to use this function."
-            )
-
-        data = self.to_list()
-        return pd.DataFrame(data)
-
-    @classmethod
-    def from_pandas(cls, dataframe: PandasDataframe):
-        """Creates an EvaluationDataset from a pandas DataFrame."""
-        return cls.from_list(dataframe.to_dict(orient="records"))
-
-    def features(self):
-        """Returns the features of the samples."""
-        return self.samples[0].get_features()
-
-    @classmethod
-    def from_dict(cls: t.Type[T], mapping: t.Dict) -> T:
-        """Creates an EvaluationDataset from a dictionary."""
-        samples = []
-        if all(
-            "user_input" in item and isinstance(mapping[0]["user_input"], list)
-            for item in mapping
-        ):
-            samples.extend(MultiTurnSample(**sample) for sample in mapping)
-        else:
-            samples.extend(SingleTurnSample(**sample) for sample in mapping)
-        return cls(samples=samples)
-
-    def to_csv(self, path: t.Union[str, Path]):
-        """Converts the dataset to a CSV file."""
-        import csv
-
-        data = self.to_list()
-        if not data:
-            return
-
-        fieldnames = data[0].keys()
-
-        with open(path, "w", newline="") as csvfile:
-            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-            writer.writeheader()
-            for row in data:
-                writer.writerow(row)
-
-    def to_jsonl(self, path: t.Union[str, Path]):
-        """Converts the dataset to a JSONL file."""
-        with open(path, "w") as jsonlfile:
-            for sample in self.to_list():
-                jsonlfile.write(json.dumps(sample, ensure_ascii=False) + "\n")
-
-    @classmethod
-    def from_jsonl(cls: t.Type[T], path: t.Union[str, Path]) -> T:
-        """Creates an EvaluationDataset from a JSONL file."""
-        with open(path, "r") as jsonlfile:
-            data = [json.loads(line) for line in jsonlfile]
-        return cls.from_list(data)
-
-    def __iter__(self) -> t.Iterator[Sample]:  # type: ignore
-        return iter(self.samples)
-
-    def __len__(self) -> int:
-        return len(self.samples)
-
-    def __str__(self) -> str:
-        return f"EvaluationDataset(features={self.features()}, len={len(self.samples)})"
-
-    def __repr__(self) -> str:
-        return self.__str__()
-
-
-SingleTurnSampleOrMultiTurnSample = t.Union[SingleTurnSample, MultiTurnSample]
-
-
-@dataclass
-class EvaluationDataset(RagasDataset[SingleTurnSampleOrMultiTurnSample]):
-    """
-    Represents a dataset of evaluation samples.
-
-    Attributes
-    ----------
-    samples : List[BaseSample]
-        A list of evaluation samples.
-
-    Methods
-    -------
-    validate_samples(samples)
-        Validates that all samples are of the same type.
-    get_sample_type()
-        Returns the type of the samples in the dataset.
-    to_hf_dataset()
-        Converts the dataset to a Hugging Face Dataset.
-    to_pandas()
-        Converts the dataset to a pandas DataFrame.
-    features()
-        Returns the features of the samples.
-    from_list(mapping)
-        Creates an EvaluationDataset from a list of dictionaries.
-    from_dict(mapping)
-        Creates an EvaluationDataset from a dictionary.
-    to_csv(path)
-        Converts the dataset to a CSV file.
-    to_jsonl(path)
-        Converts the dataset to a JSONL file.
-    from_jsonl(path)
-        Creates an EvaluationDataset from a JSONL file.
-    """
-
-    @t.overload
-    def __getitem__(self, idx: int) -> SingleTurnSampleOrMultiTurnSample: ...
-
-    @t.overload
-    def __getitem__(self, idx: slice) -> "EvaluationDataset": ...
-
-    def __getitem__(
-        self, idx: t.Union[int, slice]
-    ) -> t.Union[SingleTurnSampleOrMultiTurnSample, "EvaluationDataset"]:
-        if isinstance(idx, int):
-            return self.samples[idx]
-        elif isinstance(idx, slice):
-            return type(self)(samples=self.samples[idx])
-        else:
-            raise TypeError("Index must be int or slice")
-
-    def is_multi_turn(self) -> bool:
-        return self.get_sample_type() == MultiTurnSample
-
-    def to_list(self) -> t.List[t.Dict]:
-        rows = [sample.to_dict() for sample in self.samples]
-
-        if self.get_sample_type() == MultiTurnSample:
-            for sample in rows:
-                for item in sample["user_input"]:
-                    if not isinstance(item["content"], str):
-                        item["content"] = json.dumps(
-                            item["content"], ensure_ascii=False
-                        )
-
-        return rows
-
-    @classmethod
-    def from_list(cls, data: t.List[t.Dict]) -> EvaluationDataset:
-        samples = []
-        if all(
-            "user_input" in item and isinstance(data[0]["user_input"], list)
-            for item in data
-        ):
-            samples.extend(MultiTurnSample(**sample) for sample in data)
-        else:
-            samples.extend(SingleTurnSample(**sample) for sample in data)
-        return cls(samples=samples)
-
-    def __repr__(self) -> str:
-        return f"EvaluationDataset(features={self.features()}, len={len(self.samples)})"
-
-
-@dataclass
-class EvaluationResult:
-    """
-    A class to store and process the results of the evaluation.
-
-    Attributes
-    ----------
-    scores : Dataset
-        The dataset containing the scores of the evaluation.
-    dataset : Dataset, optional
-        The original dataset used for the evaluation. Default is None.
-    binary_columns : list of str, optional
-        List of columns that are binary metrics. Default is an empty list.
-    cost_cb : CostCallbackHandler, optional
-        The callback handler for cost computation. Default is None.
-    """
-
-    scores: t.List[t.Dict[str, t.Any]]
-    dataset: EvaluationDataset
-    binary_columns: t.List[str] = field(default_factory=list)
-    cost_cb: t.Optional[CostCallbackHandler] = None
-    traces: t.List[t.Dict[str, t.Any]] = field(default_factory=list)
-    ragas_traces: t.Dict[str, ChainRun] = field(default_factory=dict, repr=False)
-    run_id: t.Optional[UUID] = None
-
-    def __post_init__(self):
-        # transform scores from list of dicts to dict of lists
-        self._scores_dict = {
-            k: [d[k] for d in self.scores] for k in self.scores[0].keys()
-        }
-
-        values = []
-        self._repr_dict = {}
-        for metric_name in self._scores_dict.keys():
-            value = safe_nanmean(self._scores_dict[metric_name])
-            self._repr_dict[metric_name] = value
-            if metric_name not in self.binary_columns:
-                value = t.cast(float, value)
-                values.append(value + 1e-10)
-
-        # parse the traces
-        run_id = str(self.run_id) if self.run_id is not None else None
-        self.traces = parse_run_traces(self.ragas_traces, run_id)
-
-    def __repr__(self) -> str:
-        score_strs = [f"'{k}': {v:0.4f}" for k, v in self._repr_dict.items()]
-        return "{" + ", ".join(score_strs) + "}"
-
-    def __getitem__(self, key: str) -> t.List[float]:
-        return self._scores_dict[key]
-
-    def to_pandas(self, batch_size: int | None = None, batched: bool = False):
-        """
-        Convert the result to a pandas DataFrame.
-
-        Parameters
-        ----------
-        batch_size : int, optional
-            The batch size for conversion. Default is None.
-        batched : bool, optional
-            Whether to convert in batches. Default is False.
-
-        Returns
-        -------
-        pandas.DataFrame
-            The result as a pandas DataFrame.
-
-        Raises
-        ------
-        ValueError
-            If the dataset is not provided.
-        """
-        try:
-            import pandas as pd
-        except ImportError:
-            raise ImportError(
-                "pandas is not installed. Please install it to use this function."
-            )
-
-        if self.dataset is None:
-            raise ValueError("dataset is not provided for the results class")
-        assert len(self.scores) == len(self.dataset)
-        # convert both to pandas dataframes and concatenate
-        scores_df = pd.DataFrame(self.scores)
-        dataset_df = self.dataset.to_pandas()
-        return pd.concat([dataset_df, scores_df], axis=1)
-
-    def total_tokens(self) -> t.Union[t.List[TokenUsage], TokenUsage]:
-        """
-        Compute the total tokens used in the evaluation.
-
-        Returns
-        -------
-        list of TokenUsage or TokenUsage
-            The total tokens used.
-
-        Raises
-        ------
-        ValueError
-            If the cost callback handler is not provided.
-        """
-        if self.cost_cb is None:
-            raise ValueError(
-                "The evaluate() run was not configured for computing cost. Please provide a token_usage_parser function to evaluate() to compute cost."
-            )
-        return self.cost_cb.total_tokens()
-
-    def total_cost(
-        self,
-        cost_per_input_token: t.Optional[float] = None,
-        cost_per_output_token: t.Optional[float] = None,
-        per_model_costs: t.Dict[str, t.Tuple[float, float]] = {},
-    ) -> float:
-        """
-        Compute the total cost of the evaluation.
-
-        Parameters
-        ----------
-        cost_per_input_token : float, optional
-            The cost per input token. Default is None.
-        cost_per_output_token : float, optional
-            The cost per output token. Default is None.
-        per_model_costs : dict of str to tuple of float, optional
-            The per model costs. Default is an empty dictionary.
-
-        Returns
-        -------
-        float
-            The total cost of the evaluation.
-
-        Raises
-        ------
-        ValueError
-            If the cost callback handler is not provided.
-        """
-        if self.cost_cb is None:
-            raise ValueError(
-                "The evaluate() run was not configured for computing cost. Please provide a token_usage_parser function to evaluate() to compute cost."
-            )
-        return self.cost_cb.total_cost(
-            cost_per_input_token, cost_per_output_token, per_model_costs
-        )
-
-    def upload(
-        self,
-        verbose: bool = True,
-    ) -> str:
-        from datetime import datetime, timezone
-
-        timestamp = datetime.now(timezone.utc).isoformat()
-        root_trace = [
-            trace for trace in self.ragas_traces.values() if trace.parent_run_id is None
-        ][0]
-        packet = json.dumps(
-            {
-                "run_id": str(root_trace.run_id),
-                "created_at": timestamp,
-                "evaluation_run": [t.model_dump() for t in self.ragas_traces.values()],
-            },
-            cls=ChainRunEncoder,
-        )
-        response = upload_packet(
-            path="/alignment/evaluation",
-            data_json_string=packet,
-        )
-
-        # check status codes
-        app_url = get_app_url()
-        evaluation_app_url = build_evaluation_app_url(app_url, root_trace.run_id)
-        if response.status_code == 409:
-            # this evalution already exists
-            if verbose:
-                print(f"Evaluation run already exists. View at {evaluation_app_url}")
-            return evaluation_app_url
-        elif response.status_code != 200:
-            # any other error
-            raise UploadException(
-                status_code=response.status_code,
-                message=f"Failed to upload results: {response.text}",
-            )
-
-        if verbose:
-            print(f"Evaluation results uploaded! View at {evaluation_app_url}")
-        return evaluation_app_url
-
-
-class PromptAnnotation(BaseModel):
-    prompt_input: t.Dict[str, t.Any]
-    prompt_output: t.Dict[str, t.Any]
-    edited_output: t.Optional[t.Dict[str, t.Any]] = None
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-
-class SampleAnnotation(BaseModel):
-    metric_input: t.Dict[str, t.Any]
-    metric_output: float
-    prompts: t.Dict[str, PromptAnnotation]
-    is_accepted: bool
-    target: t.Optional[float] = None
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-
-class MetricAnnotation(BaseModel):
-    root: t.Dict[str, t.List[SampleAnnotation]]
-
-    def __getitem__(self, key):
-        return SingleMetricAnnotation(name=key, samples=self.root[key])
-
-    @classmethod
-    def _process_dataset(
-        cls, dataset: dict, metric_name: t.Optional[str]
-    ) -> "MetricAnnotation":
-        """
-        Process raw dataset into MetricAnnotation format
-
-        Parameters
-        ----------
-        dataset : dict
-            Raw dataset to process
-        metric_name : str, optional
-            Name of the specific metric to filter
-
-        Returns
-        -------
-        MetricAnnotation
-            Processed annotation data
-        """
-        if metric_name is not None and metric_name not in dataset:
-            raise ValueError(f"Split {metric_name} not found in the dataset.")
-
-        return cls(
-            root={
-                key: [SampleAnnotation(**sample) for sample in value]
-                for key, value in dataset.items()
-                if metric_name is None or key == metric_name
-            }
-        )
-
-    @classmethod
-    def from_json(cls, path: str, metric_name: t.Optional[str]) -> "MetricAnnotation":
-        """Load annotations from a JSON file"""
-        dataset = json.load(open(path))
-        return cls._process_dataset(dataset, metric_name)
-
-    @classmethod
-    def from_app(
-        cls,
-        run_id: str,
-        metric_name: t.Optional[str] = None,
-    ) -> "MetricAnnotation":
-        """
-        Fetch annotations from a URL using either evaluation result or run_id
-
-        Parameters
-        ----------
-        run_id : str
-            Direct run ID to fetch annotations
-        metric_name : str, optional
-            Name of the specific metric to filter
-
-        Returns
-        -------
-        MetricAnnotation
-            Annotation data from the API
-
-        Raises
-        ------
-        ValueError
-            If run_id is not provided
-        """
-        if run_id is None:
-            raise ValueError("run_id must be provided")
-
-        endpoint = f"/api/v1/alignment/evaluation/annotation/{run_id}"
-
-        app_token = get_app_token()
-        base_url = get_api_url()
-        app_url = get_app_url()
-
-        response = requests.get(
-            f"{base_url}{endpoint}",
-            headers={
-                "Content-Type": "application/json",
-                "x-app-token": app_token,
-                "x-source": RAGAS_API_SOURCE,
-                "x-app-version": __version__,
-            },
-        )
-
-        check_api_response(response)
-        dataset = response.json()["data"]
-
-        if not dataset:
-            evaluation_url = build_evaluation_app_url(app_url, run_id)
-            raise ValueError(
-                f"No annotations found. Please annotate the Evaluation first then run this method. "
-                f"\nNote: you can annotate the evaluations using the Ragas app by going to {evaluation_url}"
-            )
-
-        return cls._process_dataset(dataset, metric_name)
-
-    def __len__(self):
-        return sum(len(value) for value in self.root.values())
-
-
-class SingleMetricAnnotation(BaseModel):
-    name: str
-    samples: t.List[SampleAnnotation]
-
-    def to_evaluation_dataset(self) -> EvaluationDataset:
-        samples = [sample.metric_input for sample in self.samples]
-        return EvaluationDataset.from_list(samples)
-
-    def __getitem__(self, idx):
-        return self.samples[idx]
-
-    def __repr__(self):
-        return f"SingleMetricAnnotation(name={self.name}, len={len(self.samples)})"
-
-    def __iter__(self) -> t.Iterator[SampleAnnotation]:  # type: ignore
-        return iter(self.samples)
-
-    def select(self, indices: t.List[int]) -> "SingleMetricAnnotation":
-        return SingleMetricAnnotation(
-            name=self.name,
-            samples=[self.samples[idx] for idx in indices],
-        )
-
-    @classmethod
-    def from_json(cls, path) -> "SingleMetricAnnotation":
-        dataset = json.load(open(path))
-
-        return cls(
-            name=dataset["name"],
-            samples=[SampleAnnotation(**sample) for sample in dataset["samples"]],
-        )
-
-    def filter(self, function: t.Optional[t.Callable] = None):
-        if function is None:
-            function = lambda x: True  # noqa: E731
-
-        return SingleMetricAnnotation(
-            name=self.name,
-            samples=[sample for sample in self.samples if function(sample)],
-        )
-
-    def __len__(self):
-        return len(self.samples)
-
-    def train_test_split(
-        self,
-        test_size: float = 0.2,
-        seed: int = 42,
-        stratify: t.Optional[t.List[t.Any]] = None,
-    ) -> t.Tuple["SingleMetricAnnotation", "SingleMetricAnnotation"]:
-        """
-        Split the dataset into training and testing sets.
-
-        Parameters:
-            test_size (float): The proportion of the dataset to include in the test split.
-            seed (int): Random seed for reproducibility.
-            stratify (list): The column values to stratify the split on.
-        """
-        raise NotImplementedError
-
-    def sample(
-        self, n: int, stratify_key: t.Optional[str] = None
-    ) -> "SingleMetricAnnotation":
-        """
-        Create a subset of the dataset.
-
-        Parameters:
-            n (int): The number of samples to include in the subset.
-            stratify_key (str): The column to stratify the subset on.
-
-        Returns:
-            SingleMetricAnnotation: A subset of the dataset with `n` samples.
-        """
-        if n > len(self.samples):
-            raise ValueError(
-                "Requested sample size exceeds the number of available samples."
-            )
-
-        if stratify_key is None:
-            # Simple random sampling
-            sampled_indices = random.sample(range(len(self.samples)), n)
-            sampled_samples = [self.samples[i] for i in sampled_indices]
-        else:
-            # Stratified sampling
-            class_groups = defaultdict(list)
-            for idx, sample in enumerate(self.samples):
-                key = sample[stratify_key]
-                class_groups[key].append(idx)
-
-            # Determine the proportion of samples to take from each class
-            total_samples = sum(len(indices) for indices in class_groups.values())
-            proportions = {
-                cls: len(indices) / total_samples
-                for cls, indices in class_groups.items()
-            }
-
-            sampled_indices = []
-            for cls, indices in class_groups.items():
-                cls_sample_count = int(np.round(proportions[cls] * n))
-                cls_sample_count = min(
-                    cls_sample_count, len(indices)
-                )  # Don't oversample
-                sampled_indices.extend(random.sample(indices, cls_sample_count))
-
-            # Handle any rounding discrepancies to ensure exactly `n` samples
-            while len(sampled_indices) < n:
-                remaining_indices = set(range(len(self.samples))) - set(sampled_indices)
-                if not remaining_indices:
-                    break
-                sampled_indices.append(random.choice(list(remaining_indices)))
-
-            sampled_samples = [self.samples[i] for i in sampled_indices]
-
-        return SingleMetricAnnotation(name=self.name, samples=sampled_samples)
-
-    def batch(
-        self,
-        batch_size: int,
-        drop_last_batch: bool = False,
-    ):
-        """
-        Create a batch iterator.
-
-        Parameters:
-            batch_size (int): The number of samples in each batch.
-            stratify (str): The column to stratify the batches on.
-            drop_last_batch (bool): Whether to drop the last batch if it is smaller than the specified batch size.
-        """
-
-        samples = self.samples[:]
-        random.shuffle(samples)
-
-        all_batches = [
-            samples[i : i + batch_size]
-            for i in range(0, len(samples), batch_size)
-            if len(samples[i : i + batch_size]) == batch_size or not drop_last_batch
-        ]
-
-        return all_batches
-
-    def stratified_batches(
-        self,
-        batch_size: int,
-        stratify_key: str,
-        drop_last_batch: bool = False,
-        replace: bool = False,
-    ) -> t.List[t.List[SampleAnnotation]]:
-        """
-        Create stratified batches based on a specified key, ensuring proportional representation.
-
-        Parameters:
-            batch_size (int): Number of samples per batch.
-            stratify_key (str): Key in `metric_input` used for stratification (e.g., class labels).
-            drop_last_batch (bool): If True, drops the last batch if it has fewer samples than `batch_size`.
-            replace (bool): If True, allows reusing samples from the same class to fill a batch if necessary.
-
-        Returns:
-            List[List[SampleAnnotation]]: A list of stratified batches, each batch being a list of SampleAnnotation objects.
-        """
-        # Group samples based on the stratification key
-        class_groups = defaultdict(list)
-        for sample in self.samples:
-            key = sample[stratify_key]
-            class_groups[key].append(sample)
-
-        # Shuffle each class group for randomness
-        for group in class_groups.values():
-            random.shuffle(group)
-
-        # Determine the number of batches required
-        total_samples = len(self.samples)
-        num_batches = (
-            np.ceil(total_samples / batch_size).astype(int)
-            if drop_last_batch
-            else np.floor(total_samples / batch_size).astype(int)
-        )
-        samples_per_class_per_batch = {
-            cls: max(1, len(samples) // num_batches)
-            for cls, samples in class_groups.items()
-        }
-
-        # Create stratified batches
-        all_batches = []
-        while len(all_batches) < num_batches:
-            batch = []
-            for cls, samples in list(class_groups.items()):
-                # Determine the number of samples to take from this class
-                count = min(
-                    samples_per_class_per_batch[cls],
-                    len(samples),
-                    batch_size - len(batch),
-                )
-                if count > 0:
-                    # Add samples from the current class
-                    batch.extend(samples[:count])
-                    class_groups[cls] = samples[count:]  # Remove used samples
-                elif replace and len(batch) < batch_size:
-                    # Reuse samples if `replace` is True
-                    batch.extend(random.choices(samples, k=batch_size - len(batch)))
-
-            # Shuffle the batch to mix classes
-            random.shuffle(batch)
-            if len(batch) == batch_size or not drop_last_batch:
-                all_batches.append(batch)
-
-        return all_batches
-
-    def get_prompt_annotations(self) -> t.Dict[str, t.List[PromptAnnotation]]:
-        """
-        Get all the prompt annotations for each prompt as a list.
-        """
-        prompt_annotations = defaultdict(list)
-        for sample in self.samples:
-            if sample.is_accepted:
-                for prompt_name, prompt_annotation in sample.prompts.items():
-                    prompt_annotations[prompt_name].append(prompt_annotation)
-        return prompt_annotations
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Mar 27, 2025. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/typing.html b/docs/_build/html/_modules/typing.html deleted file mode 100644 index 8110d892..00000000 --- a/docs/_build/html/_modules/typing.html +++ /dev/null @@ -1,3646 +0,0 @@ - - - - - - typing — NotDiamond 0.3.25 - - - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -

Source code for typing

-"""
-The typing module: Support for gradual typing as defined by PEP 484 and subsequent PEPs.
-
-Among other things, the module includes the following:
-* Generic, Protocol, and internal machinery to support generic aliases.
-  All subscripted types like X[int], Union[int, str] are generic aliases.
-* Various "special forms" that have unique meanings in type annotations:
-  NoReturn, Never, ClassVar, Self, Concatenate, Unpack, and others.
-* Classes whose instances can be type arguments to generic classes and functions:
-  TypeVar, ParamSpec, TypeVarTuple.
-* Public helper functions: get_type_hints, overload, cast, final, and others.
-* Several protocols to support duck-typing:
-  SupportsFloat, SupportsIndex, SupportsAbs, and others.
-* Special types: NewType, NamedTuple, TypedDict.
-* Deprecated wrapper submodules for re and io related types.
-* Deprecated aliases for builtin types and collections.abc ABCs.
-
-Any name not present in __all__ is an implementation detail
-that may be changed without notice. Use at your own risk!
-"""
-
-from abc import abstractmethod, ABCMeta
-import collections
-from collections import defaultdict
-import collections.abc
-import contextlib
-import functools
-import operator
-import re as stdlib_re  # Avoid confusion with the re we export.
-import sys
-import types
-import warnings
-from types import WrapperDescriptorType, MethodWrapperType, MethodDescriptorType, GenericAlias
-
-
-try:
-    from _typing import _idfunc
-except ImportError:
-    def _idfunc(_, x):
-        return x
-
-# Please keep __all__ alphabetized within each category.
-__all__ = [
-    # Super-special typing primitives.
-    'Annotated',
-    'Any',
-    'Callable',
-    'ClassVar',
-    'Concatenate',
-    'Final',
-    'ForwardRef',
-    'Generic',
-    'Literal',
-    'Optional',
-    'ParamSpec',
-    'Protocol',
-    'Tuple',
-    'Type',
-    'TypeVar',
-    'TypeVarTuple',
-    'Union',
-
-    # ABCs (from collections.abc).
-    'AbstractSet',  # collections.abc.Set.
-    'ByteString',
-    'Container',
-    'ContextManager',
-    'Hashable',
-    'ItemsView',
-    'Iterable',
-    'Iterator',
-    'KeysView',
-    'Mapping',
-    'MappingView',
-    'MutableMapping',
-    'MutableSequence',
-    'MutableSet',
-    'Sequence',
-    'Sized',
-    'ValuesView',
-    'Awaitable',
-    'AsyncIterator',
-    'AsyncIterable',
-    'Coroutine',
-    'Collection',
-    'AsyncGenerator',
-    'AsyncContextManager',
-
-    # Structural checks, a.k.a. protocols.
-    'Reversible',
-    'SupportsAbs',
-    'SupportsBytes',
-    'SupportsComplex',
-    'SupportsFloat',
-    'SupportsIndex',
-    'SupportsInt',
-    'SupportsRound',
-
-    # Concrete collection types.
-    'ChainMap',
-    'Counter',
-    'Deque',
-    'Dict',
-    'DefaultDict',
-    'List',
-    'OrderedDict',
-    'Set',
-    'FrozenSet',
-    'NamedTuple',  # Not really a type.
-    'TypedDict',  # Not really a type.
-    'Generator',
-
-    # Other concrete types.
-    'BinaryIO',
-    'IO',
-    'Match',
-    'Pattern',
-    'TextIO',
-
-    # One-off things.
-    'AnyStr',
-    'assert_type',
-    'assert_never',
-    'cast',
-    'clear_overloads',
-    'dataclass_transform',
-    'final',
-    'get_args',
-    'get_origin',
-    'get_overloads',
-    'get_type_hints',
-    'is_typeddict',
-    'LiteralString',
-    'Never',
-    'NewType',
-    'no_type_check',
-    'no_type_check_decorator',
-    'NoReturn',
-    'NotRequired',
-    'overload',
-    'ParamSpecArgs',
-    'ParamSpecKwargs',
-    'Required',
-    'reveal_type',
-    'runtime_checkable',
-    'Self',
-    'Text',
-    'TYPE_CHECKING',
-    'TypeAlias',
-    'TypeGuard',
-    'Unpack',
-]
-
-# The pseudo-submodules 're' and 'io' are part of the public
-# namespace, but excluded from __all__ because they might stomp on
-# legitimate imports of those modules.
-
-
-def _type_convert(arg, module=None, *, allow_special_forms=False):
-    """For converting None to type(None), and strings to ForwardRef."""
-    if arg is None:
-        return type(None)
-    if isinstance(arg, str):
-        return ForwardRef(arg, module=module, is_class=allow_special_forms)
-    return arg
-
-
-def _type_check(arg, msg, is_argument=True, module=None, *, allow_special_forms=False):
-    """Check that the argument is a type, and return it (internal helper).
-
-    As a special case, accept None and return type(None) instead. Also wrap strings
-    into ForwardRef instances. Consider several corner cases, for example plain
-    special forms like Union are not valid, while Union[int, str] is OK, etc.
-    The msg argument is a human-readable error message, e.g.::
-
-        "Union[arg, ...]: arg should be a type."
-
-    We append the repr() of the actual value (truncated to 100 chars).
-    """
-    invalid_generic_forms = (Generic, Protocol)
-    if not allow_special_forms:
-        invalid_generic_forms += (ClassVar,)
-        if is_argument:
-            invalid_generic_forms += (Final,)
-
-    arg = _type_convert(arg, module=module, allow_special_forms=allow_special_forms)
-    if (isinstance(arg, _GenericAlias) and
-            arg.__origin__ in invalid_generic_forms):
-        raise TypeError(f"{arg} is not valid as type argument")
-    if arg in (Any, LiteralString, NoReturn, Never, Self, TypeAlias):
-        return arg
-    if allow_special_forms and arg in (ClassVar, Final):
-        return arg
-    if isinstance(arg, _SpecialForm) or arg in (Generic, Protocol):
-        raise TypeError(f"Plain {arg} is not valid as type argument")
-    if type(arg) is tuple:
-        raise TypeError(f"{msg} Got {arg!r:.100}.")
-    return arg
-
-
-def _is_param_expr(arg):
-    return arg is ... or isinstance(arg,
-            (tuple, list, ParamSpec, _ConcatenateGenericAlias))
-
-
-def _should_unflatten_callable_args(typ, args):
-    """Internal helper for munging collections.abc.Callable's __args__.
-
-    The canonical representation for a Callable's __args__ flattens the
-    argument types, see https://github.com/python/cpython/issues/86361.
-
-    For example::
-
-        >>> import collections.abc
-        >>> P = ParamSpec('P')
-        >>> collections.abc.Callable[[int, int], str].__args__ == (int, int, str)
-        True
-        >>> collections.abc.Callable[P, str].__args__ == (P, str)
-        True
-
-    As a result, if we need to reconstruct the Callable from its __args__,
-    we need to unflatten it.
-    """
-    return (
-        typ.__origin__ is collections.abc.Callable
-        and not (len(args) == 2 and _is_param_expr(args[0]))
-    )
-
-
-def _type_repr(obj):
-    """Return the repr() of an object, special-casing types (internal helper).
-
-    If obj is a type, we return a shorter version than the default
-    type.__repr__, based on the module and qualified name, which is
-    typically enough to uniquely identify a type.  For everything
-    else, we fall back on repr(obj).
-    """
-    if isinstance(obj, types.GenericAlias):
-        return repr(obj)
-    if isinstance(obj, type):
-        if obj.__module__ == 'builtins':
-            return obj.__qualname__
-        return f'{obj.__module__}.{obj.__qualname__}'
-    if obj is ...:
-        return('...')
-    if isinstance(obj, types.FunctionType):
-        return obj.__name__
-    return repr(obj)
-
-
-def _collect_parameters(args):
-    """Collect all type variables and parameter specifications in args
-    in order of first appearance (lexicographic order).
-
-    For example::
-
-        >>> P = ParamSpec('P')
-        >>> T = TypeVar('T')
-        >>> _collect_parameters((T, Callable[P, T]))
-        (~T, ~P)
-    """
-    parameters = []
-    for t in args:
-        if isinstance(t, type):
-            # We don't want __parameters__ descriptor of a bare Python class.
-            pass
-        elif isinstance(t, tuple):
-            # `t` might be a tuple, when `ParamSpec` is substituted with
-            # `[T, int]`, or `[int, *Ts]`, etc.
-            for x in t:
-                for collected in _collect_parameters([x]):
-                    if collected not in parameters:
-                        parameters.append(collected)
-        elif hasattr(t, '__typing_subst__'):
-            if t not in parameters:
-                parameters.append(t)
-        else:
-            for x in getattr(t, '__parameters__', ()):
-                if x not in parameters:
-                    parameters.append(x)
-    return tuple(parameters)
-
-
-def _check_generic(cls, parameters, elen):
-    """Check correct count for parameters of a generic cls (internal helper).
-
-    This gives a nice error message in case of count mismatch.
-    """
-    if not elen:
-        raise TypeError(f"{cls} is not a generic class")
-    alen = len(parameters)
-    if alen != elen:
-        raise TypeError(f"Too {'many' if alen > elen else 'few'} arguments for {cls};"
-                        f" actual {alen}, expected {elen}")
-
-def _unpack_args(args):
-    newargs = []
-    for arg in args:
-        subargs = getattr(arg, '__typing_unpacked_tuple_args__', None)
-        if subargs is not None and not (subargs and subargs[-1] is ...):
-            newargs.extend(subargs)
-        else:
-            newargs.append(arg)
-    return newargs
-
-def _deduplicate(params, *, unhashable_fallback=False):
-    # Weed out strict duplicates, preserving the first of each occurrence.
-    try:
-        return dict.fromkeys(params)
-    except TypeError:
-        if not unhashable_fallback:
-            raise
-        # Happens for cases like `Annotated[dict, {'x': IntValidator()}]`
-        return _deduplicate_unhashable(params)
-
-def _deduplicate_unhashable(unhashable_params):
-    new_unhashable = []
-    for t in unhashable_params:
-        if t not in new_unhashable:
-            new_unhashable.append(t)
-    return new_unhashable
-
-def _compare_args_orderless(first_args, second_args):
-    first_unhashable = _deduplicate_unhashable(first_args)
-    second_unhashable = _deduplicate_unhashable(second_args)
-    t = list(second_unhashable)
-    try:
-        for elem in first_unhashable:
-            t.remove(elem)
-    except ValueError:
-        return False
-    return not t
-
-def _remove_dups_flatten(parameters):
-    """Internal helper for Union creation and substitution.
-
-    Flatten Unions among parameters, then remove duplicates.
-    """
-    # Flatten out Union[Union[...], ...].
-    params = []
-    for p in parameters:
-        if isinstance(p, (_UnionGenericAlias, types.UnionType)):
-            params.extend(p.__args__)
-        else:
-            params.append(p)
-
-    return tuple(_deduplicate(params, unhashable_fallback=True))
-
-
-def _flatten_literal_params(parameters):
-    """Internal helper for Literal creation: flatten Literals among parameters."""
-    params = []
-    for p in parameters:
-        if isinstance(p, _LiteralGenericAlias):
-            params.extend(p.__args__)
-        else:
-            params.append(p)
-    return tuple(params)
-
-
-_cleanups = []
-
-
-def _tp_cache(func=None, /, *, typed=False):
-    """Internal wrapper caching __getitem__ of generic types.
-
-    For non-hashable arguments, the original function is used as a fallback.
-    """
-    def decorator(func):
-        cached = functools.lru_cache(typed=typed)(func)
-        _cleanups.append(cached.cache_clear)
-
-        @functools.wraps(func)
-        def inner(*args, **kwds):
-            try:
-                return cached(*args, **kwds)
-            except TypeError:
-                pass  # All real errors (not unhashable args) are raised below.
-            return func(*args, **kwds)
-        return inner
-
-    if func is not None:
-        return decorator(func)
-
-    return decorator
-
-def _eval_type(t, globalns, localns, recursive_guard=frozenset()):
-    """Evaluate all forward references in the given type t.
-
-    For use of globalns and localns see the docstring for get_type_hints().
-    recursive_guard is used to prevent infinite recursion with a recursive
-    ForwardRef.
-    """
-    if isinstance(t, ForwardRef):
-        return t._evaluate(globalns, localns, recursive_guard)
-    if isinstance(t, (_GenericAlias, GenericAlias, types.UnionType)):
-        if isinstance(t, GenericAlias):
-            args = tuple(
-                ForwardRef(arg) if isinstance(arg, str) else arg
-                for arg in t.__args__
-            )
-            is_unpacked = t.__unpacked__
-            if _should_unflatten_callable_args(t, args):
-                t = t.__origin__[(args[:-1], args[-1])]
-            else:
-                t = t.__origin__[args]
-            if is_unpacked:
-                t = Unpack[t]
-        ev_args = tuple(_eval_type(a, globalns, localns, recursive_guard) for a in t.__args__)
-        if ev_args == t.__args__:
-            return t
-        if isinstance(t, GenericAlias):
-            return GenericAlias(t.__origin__, ev_args)
-        if isinstance(t, types.UnionType):
-            return functools.reduce(operator.or_, ev_args)
-        else:
-            return t.copy_with(ev_args)
-    return t
-
-
-class _Final:
-    """Mixin to prohibit subclassing."""
-
-    __slots__ = ('__weakref__',)
-
-    def __init_subclass__(cls, /, *args, **kwds):
-        if '_root' not in kwds:
-            raise TypeError("Cannot subclass special typing classes")
-
-class _Immutable:
-    """Mixin to indicate that object should not be copied."""
-
-    __slots__ = ()
-
-    def __copy__(self):
-        return self
-
-    def __deepcopy__(self, memo):
-        return self
-
-
-class _NotIterable:
-    """Mixin to prevent iteration, without being compatible with Iterable.
-
-    That is, we could do::
-
-        def __iter__(self): raise TypeError()
-
-    But this would make users of this mixin duck type-compatible with
-    collections.abc.Iterable - isinstance(foo, Iterable) would be True.
-
-    Luckily, we can instead prevent iteration by setting __iter__ to None, which
-    is treated specially.
-    """
-
-    __slots__ = ()
-    __iter__ = None
-
-
-# Internal indicator of special typing constructs.
-# See __doc__ instance attribute for specific docs.
-class _SpecialForm(_Final, _NotIterable, _root=True):
-    __slots__ = ('_name', '__doc__', '_getitem')
-
-    def __init__(self, getitem):
-        self._getitem = getitem
-        self._name = getitem.__name__
-        self.__doc__ = getitem.__doc__
-
-    def __getattr__(self, item):
-        if item in {'__name__', '__qualname__'}:
-            return self._name
-
-        raise AttributeError(item)
-
-    def __mro_entries__(self, bases):
-        raise TypeError(f"Cannot subclass {self!r}")
-
-    def __repr__(self):
-        return 'typing.' + self._name
-
-    def __reduce__(self):
-        return self._name
-
-    def __call__(self, *args, **kwds):
-        raise TypeError(f"Cannot instantiate {self!r}")
-
-    def __or__(self, other):
-        return Union[self, other]
-
-    def __ror__(self, other):
-        return Union[other, self]
-
-    def __instancecheck__(self, obj):
-        raise TypeError(f"{self} cannot be used with isinstance()")
-
-    def __subclasscheck__(self, cls):
-        raise TypeError(f"{self} cannot be used with issubclass()")
-
-    @_tp_cache
-    def __getitem__(self, parameters):
-        return self._getitem(self, parameters)
-
-
-class _LiteralSpecialForm(_SpecialForm, _root=True):
-    def __getitem__(self, parameters):
-        if not isinstance(parameters, tuple):
-            parameters = (parameters,)
-        return self._getitem(self, *parameters)
-
-
-class _AnyMeta(type):
-    def __instancecheck__(self, obj):
-        if self is Any:
-            raise TypeError("typing.Any cannot be used with isinstance()")
-        return super().__instancecheck__(obj)
-
-    def __repr__(self):
-        if self is Any:
-            return "typing.Any"
-        return super().__repr__()  # respect to subclasses
-
-
-class Any(metaclass=_AnyMeta):
-    """Special type indicating an unconstrained type.
-
-    - Any is compatible with every type.
-    - Any assumed to have all methods.
-    - All values assumed to be instances of Any.
-
-    Note that all the above statements are true from the point of view of
-    static type checkers. At runtime, Any should not be used with instance
-    checks.
-    """
-
-    def __new__(cls, *args, **kwargs):
-        if cls is Any:
-            raise TypeError("Any cannot be instantiated")
-        return super().__new__(cls)
-
-
-@_SpecialForm
-def NoReturn(self, parameters):
-    """Special type indicating functions that never return.
-
-    Example::
-
-        from typing import NoReturn
-
-        def stop() -> NoReturn:
-            raise Exception('no way')
-
-    NoReturn can also be used as a bottom type, a type that
-    has no values. Starting in Python 3.11, the Never type should
-    be used for this concept instead. Type checkers should treat the two
-    equivalently.
-    """
-    raise TypeError(f"{self} is not subscriptable")
-
-# This is semantically identical to NoReturn, but it is implemented
-# separately so that type checkers can distinguish between the two
-# if they want.
-@_SpecialForm
-def Never(self, parameters):
-    """The bottom type, a type that has no members.
-
-    This can be used to define a function that should never be
-    called, or a function that never returns::
-
-        from typing import Never
-
-        def never_call_me(arg: Never) -> None:
-            pass
-
-        def int_or_str(arg: int | str) -> None:
-            never_call_me(arg)  # type checker error
-            match arg:
-                case int():
-                    print("It's an int")
-                case str():
-                    print("It's a str")
-                case _:
-                    never_call_me(arg)  # OK, arg is of type Never
-    """
-    raise TypeError(f"{self} is not subscriptable")
-
-
-@_SpecialForm
-def Self(self, parameters):
-    """Used to spell the type of "self" in classes.
-
-    Example::
-
-        from typing import Self
-
-        class Foo:
-            def return_self(self) -> Self:
-                ...
-                return self
-
-    This is especially useful for:
-        - classmethods that are used as alternative constructors
-        - annotating an `__enter__` method which returns self
-    """
-    raise TypeError(f"{self} is not subscriptable")
-
-
-@_SpecialForm
-def LiteralString(self, parameters):
-    """Represents an arbitrary literal string.
-
-    Example::
-
-        from typing import LiteralString
-
-        def run_query(sql: LiteralString) -> None:
-            ...
-
-        def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
-            run_query("SELECT * FROM students")  # OK
-            run_query(literal_string)  # OK
-            run_query("SELECT * FROM " + literal_string)  # OK
-            run_query(arbitrary_string)  # type checker error
-            run_query(  # type checker error
-                f"SELECT * FROM students WHERE name = {arbitrary_string}"
-            )
-
-    Only string literals and other LiteralStrings are compatible
-    with LiteralString. This provides a tool to help prevent
-    security issues such as SQL injection.
-    """
-    raise TypeError(f"{self} is not subscriptable")
-
-
-@_SpecialForm
-def ClassVar(self, parameters):
-    """Special type construct to mark class variables.
-
-    An annotation wrapped in ClassVar indicates that a given
-    attribute is intended to be used as a class variable and
-    should not be set on instances of that class.
-
-    Usage::
-
-        class Starship:
-            stats: ClassVar[dict[str, int]] = {} # class variable
-            damage: int = 10                     # instance variable
-
-    ClassVar accepts only types and cannot be further subscribed.
-
-    Note that ClassVar is not a class itself, and should not
-    be used with isinstance() or issubclass().
-    """
-    item = _type_check(parameters, f'{self} accepts only single type.')
-    return _GenericAlias(self, (item,))
-
-@_SpecialForm
-def Final(self, parameters):
-    """Special typing construct to indicate final names to type checkers.
-
-    A final name cannot be re-assigned or overridden in a subclass.
-
-    For example::
-
-        MAX_SIZE: Final = 9000
-        MAX_SIZE += 1  # Error reported by type checker
-
-        class Connection:
-            TIMEOUT: Final[int] = 10
-
-        class FastConnector(Connection):
-            TIMEOUT = 1  # Error reported by type checker
-
-    There is no runtime checking of these properties.
-    """
-    item = _type_check(parameters, f'{self} accepts only single type.')
-    return _GenericAlias(self, (item,))
-
-@_SpecialForm
-def Union(self, parameters):
-    """Union type; Union[X, Y] means either X or Y.
-
-    On Python 3.10 and higher, the | operator
-    can also be used to denote unions;
-    X | Y means the same thing to the type checker as Union[X, Y].
-
-    To define a union, use e.g. Union[int, str]. Details:
-    - The arguments must be types and there must be at least one.
-    - None as an argument is a special case and is replaced by
-      type(None).
-    - Unions of unions are flattened, e.g.::
-
-        assert Union[Union[int, str], float] == Union[int, str, float]
-
-    - Unions of a single argument vanish, e.g.::
-
-        assert Union[int] == int  # The constructor actually returns int
-
-    - Redundant arguments are skipped, e.g.::
-
-        assert Union[int, str, int] == Union[int, str]
-
-    - When comparing unions, the argument order is ignored, e.g.::
-
-        assert Union[int, str] == Union[str, int]
-
-    - You cannot subclass or instantiate a union.
-    - You can use Optional[X] as a shorthand for Union[X, None].
-    """
-    if parameters == ():
-        raise TypeError("Cannot take a Union of no types.")
-    if not isinstance(parameters, tuple):
-        parameters = (parameters,)
-    msg = "Union[arg, ...]: each arg must be a type."
-    parameters = tuple(_type_check(p, msg) for p in parameters)
-    parameters = _remove_dups_flatten(parameters)
-    if len(parameters) == 1:
-        return parameters[0]
-    if len(parameters) == 2 and type(None) in parameters:
-        return _UnionGenericAlias(self, parameters, name="Optional")
-    return _UnionGenericAlias(self, parameters)
-
-@_SpecialForm
-def Optional(self, parameters):
-    """Optional[X] is equivalent to Union[X, None]."""
-    arg = _type_check(parameters, f"{self} requires a single type.")
-    return Union[arg, type(None)]
-
-@_LiteralSpecialForm
-@_tp_cache(typed=True)
-def Literal(self, *parameters):
-    """Special typing form to define literal types (a.k.a. value types).
-
-    This form can be used to indicate to type checkers that the corresponding
-    variable or function parameter has a value equivalent to the provided
-    literal (or one of several literals)::
-
-        def validate_simple(data: Any) -> Literal[True]:  # always returns True
-            ...
-
-        MODE = Literal['r', 'rb', 'w', 'wb']
-        def open_helper(file: str, mode: MODE) -> str:
-            ...
-
-        open_helper('/some/path', 'r')  # Passes type check
-        open_helper('/other/path', 'typo')  # Error in type checker
-
-    Literal[...] cannot be subclassed. At runtime, an arbitrary value
-    is allowed as type argument to Literal[...], but type checkers may
-    impose restrictions.
-    """
-    # There is no '_type_check' call because arguments to Literal[...] are
-    # values, not types.
-    parameters = _flatten_literal_params(parameters)
-
-    try:
-        parameters = tuple(p for p, _ in _deduplicate(list(_value_and_type_iter(parameters))))
-    except TypeError:  # unhashable parameters
-        pass
-
-    return _LiteralGenericAlias(self, parameters)
-
-
-@_SpecialForm
-def TypeAlias(self, parameters):
-    """Special form for marking type aliases.
-
-    Use TypeAlias to indicate that an assignment should
-    be recognized as a proper type alias definition by type
-    checkers.
-
-    For example::
-
-        Predicate: TypeAlias = Callable[..., bool]
-
-    It's invalid when used anywhere except as in the example above.
-    """
-    raise TypeError(f"{self} is not subscriptable")
-
-
-@_SpecialForm
-def Concatenate(self, parameters):
-    """Special form for annotating higher-order functions.
-
-    ``Concatenate`` can be used in conjunction with ``ParamSpec`` and
-    ``Callable`` to represent a higher-order function which adds, removes or
-    transforms the parameters of a callable.
-
-    For example::
-
-        Callable[Concatenate[int, P], int]
-
-    See PEP 612 for detailed information.
-    """
-    if parameters == ():
-        raise TypeError("Cannot take a Concatenate of no types.")
-    if not isinstance(parameters, tuple):
-        parameters = (parameters,)
-    if not (parameters[-1] is ... or isinstance(parameters[-1], ParamSpec)):
-        raise TypeError("The last parameter to Concatenate should be a "
-                        "ParamSpec variable or ellipsis.")
-    msg = "Concatenate[arg, ...]: each arg must be a type."
-    parameters = (*(_type_check(p, msg) for p in parameters[:-1]), parameters[-1])
-    return _ConcatenateGenericAlias(self, parameters,
-                                    _paramspec_tvars=True)
-
-
-@_SpecialForm
-def TypeGuard(self, parameters):
-    """Special typing construct for marking user-defined type guard functions.
-
-    ``TypeGuard`` can be used to annotate the return type of a user-defined
-    type guard function.  ``TypeGuard`` only accepts a single type argument.
-    At runtime, functions marked this way should return a boolean.
-
-    ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
-    type checkers to determine a more precise type of an expression within a
-    program's code flow.  Usually type narrowing is done by analyzing
-    conditional code flow and applying the narrowing to a block of code.  The
-    conditional expression here is sometimes referred to as a "type guard".
-
-    Sometimes it would be convenient to use a user-defined boolean function
-    as a type guard.  Such a function should use ``TypeGuard[...]`` as its
-    return type to alert static type checkers to this intention.
-
-    Using  ``-> TypeGuard`` tells the static type checker that for a given
-    function:
-
-    1. The return value is a boolean.
-    2. If the return value is ``True``, the type of its argument
-       is the type inside ``TypeGuard``.
-
-       For example::
-
-           def is_str(val: Union[str, float]):
-               # "isinstance" type guard
-               if isinstance(val, str):
-                   # Type of ``val`` is narrowed to ``str``
-                   ...
-               else:
-                   # Else, type of ``val`` is narrowed to ``float``.
-                   ...
-
-    Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
-    form of ``TypeA`` (it can even be a wider form) and this may lead to
-    type-unsafe results.  The main reason is to allow for things like
-    narrowing ``List[object]`` to ``List[str]`` even though the latter is not
-    a subtype of the former, since ``List`` is invariant.  The responsibility of
-    writing type-safe type guards is left to the user.
-
-    ``TypeGuard`` also works with type variables.  For more information, see
-    PEP 647 (User-Defined Type Guards).
-    """
-    item = _type_check(parameters, f'{self} accepts only single type.')
-    return _GenericAlias(self, (item,))
-
-
-class ForwardRef(_Final, _root=True):
-    """Internal wrapper to hold a forward reference."""
-
-    __slots__ = ('__forward_arg__', '__forward_code__',
-                 '__forward_evaluated__', '__forward_value__',
-                 '__forward_is_argument__', '__forward_is_class__',
-                 '__forward_module__')
-
-    def __init__(self, arg, is_argument=True, module=None, *, is_class=False):
-        if not isinstance(arg, str):
-            raise TypeError(f"Forward reference must be a string -- got {arg!r}")
-
-        # If we do `def f(*args: *Ts)`, then we'll have `arg = '*Ts'`.
-        # Unfortunately, this isn't a valid expression on its own, so we
-        # do the unpacking manually.
-        if arg.startswith('*'):
-            arg_to_compile = f'({arg},)[0]'  # E.g. (*Ts,)[0] or (*tuple[int, int],)[0]
-        else:
-            arg_to_compile = arg
-        try:
-            code = compile(arg_to_compile, '<string>', 'eval')
-        except SyntaxError:
-            raise SyntaxError(f"Forward reference must be an expression -- got {arg!r}")
-
-        self.__forward_arg__ = arg
-        self.__forward_code__ = code
-        self.__forward_evaluated__ = False
-        self.__forward_value__ = None
-        self.__forward_is_argument__ = is_argument
-        self.__forward_is_class__ = is_class
-        self.__forward_module__ = module
-
-    def _evaluate(self, globalns, localns, recursive_guard):
-        if self.__forward_arg__ in recursive_guard:
-            return self
-        if not self.__forward_evaluated__ or localns is not globalns:
-            if globalns is None and localns is None:
-                globalns = localns = {}
-            elif globalns is None:
-                globalns = localns
-            elif localns is None:
-                localns = globalns
-            if self.__forward_module__ is not None:
-                globalns = getattr(
-                    sys.modules.get(self.__forward_module__, None), '__dict__', globalns
-                )
-            type_ = _type_check(
-                eval(self.__forward_code__, globalns, localns),
-                "Forward references must evaluate to types.",
-                is_argument=self.__forward_is_argument__,
-                allow_special_forms=self.__forward_is_class__,
-            )
-            self.__forward_value__ = _eval_type(
-                type_, globalns, localns, recursive_guard | {self.__forward_arg__}
-            )
-            self.__forward_evaluated__ = True
-        return self.__forward_value__
-
-    def __eq__(self, other):
-        if not isinstance(other, ForwardRef):
-            return NotImplemented
-        if self.__forward_evaluated__ and other.__forward_evaluated__:
-            return (self.__forward_arg__ == other.__forward_arg__ and
-                    self.__forward_value__ == other.__forward_value__)
-        return (self.__forward_arg__ == other.__forward_arg__ and
-                self.__forward_module__ == other.__forward_module__)
-
-    def __hash__(self):
-        return hash((self.__forward_arg__, self.__forward_module__))
-
-    def __or__(self, other):
-        return Union[self, other]
-
-    def __ror__(self, other):
-        return Union[other, self]
-
-    def __repr__(self):
-        if self.__forward_module__ is None:
-            module_repr = ''
-        else:
-            module_repr = f', module={self.__forward_module__!r}'
-        return f'ForwardRef({self.__forward_arg__!r}{module_repr})'
-
-
-def _is_unpacked_typevartuple(x: Any) -> bool:
-    return ((not isinstance(x, type)) and
-            getattr(x, '__typing_is_unpacked_typevartuple__', False))
-
-
-def _is_typevar_like(x: Any) -> bool:
-    return isinstance(x, (TypeVar, ParamSpec)) or _is_unpacked_typevartuple(x)
-
-
-class _PickleUsingNameMixin:
-    """Mixin enabling pickling based on self.__name__."""
-
-    def __reduce__(self):
-        return self.__name__
-
-
-class _BoundVarianceMixin:
-    """Mixin giving __init__ bound and variance arguments.
-
-    This is used by TypeVar and ParamSpec, which both employ the notions of
-    a type 'bound' (restricting type arguments to be a subtype of some
-    specified type) and type 'variance' (determining subtype relations between
-    generic types).
-    """
-    def __init__(self, bound, covariant, contravariant):
-        """Used to setup TypeVars and ParamSpec's bound, covariant and
-        contravariant attributes.
-        """
-        if covariant and contravariant:
-            raise ValueError("Bivariant types are not supported.")
-        self.__covariant__ = bool(covariant)
-        self.__contravariant__ = bool(contravariant)
-        if bound:
-            self.__bound__ = _type_check(bound, "Bound must be a type.")
-        else:
-            self.__bound__ = None
-
-    def __or__(self, right):
-        return Union[self, right]
-
-    def __ror__(self, left):
-        return Union[left, self]
-
-    def __repr__(self):
-        if self.__covariant__:
-            prefix = '+'
-        elif self.__contravariant__:
-            prefix = '-'
-        else:
-            prefix = '~'
-        return prefix + self.__name__
-
-
-class TypeVar(_Final, _Immutable, _BoundVarianceMixin, _PickleUsingNameMixin,
-              _root=True):
-    """Type variable.
-
-    Usage::
-
-      T = TypeVar('T')  # Can be anything
-      A = TypeVar('A', str, bytes)  # Must be str or bytes
-
-    Type variables exist primarily for the benefit of static type
-    checkers.  They serve as the parameters for generic types as well
-    as for generic function definitions.  See class Generic for more
-    information on generic types.  Generic functions work as follows:
-
-      def repeat(x: T, n: int) -> List[T]:
-          '''Return a list containing n references to x.'''
-          return [x]*n
-
-      def longest(x: A, y: A) -> A:
-          '''Return the longest of two strings.'''
-          return x if len(x) >= len(y) else y
-
-    The latter example's signature is essentially the overloading
-    of (str, str) -> str and (bytes, bytes) -> bytes.  Also note
-    that if the arguments are instances of some subclass of str,
-    the return type is still plain str.
-
-    At runtime, isinstance(x, T) and issubclass(C, T) will raise TypeError.
-
-    Type variables defined with covariant=True or contravariant=True
-    can be used to declare covariant or contravariant generic types.
-    See PEP 484 for more details. By default generic types are invariant
-    in all type variables.
-
-    Type variables can be introspected. e.g.:
-
-      T.__name__ == 'T'
-      T.__constraints__ == ()
-      T.__covariant__ == False
-      T.__contravariant__ = False
-      A.__constraints__ == (str, bytes)
-
-    Note that only type variables defined in global scope can be pickled.
-    """
-
-    def __init__(self, name, *constraints, bound=None,
-                 covariant=False, contravariant=False):
-        self.__name__ = name
-        super().__init__(bound, covariant, contravariant)
-        if constraints and bound is not None:
-            raise TypeError("Constraints cannot be combined with bound=...")
-        if constraints and len(constraints) == 1:
-            raise TypeError("A single constraint is not allowed")
-        msg = "TypeVar(name, constraint, ...): constraints must be types."
-        self.__constraints__ = tuple(_type_check(t, msg) for t in constraints)
-        def_mod = _caller()
-        if def_mod != 'typing':
-            self.__module__ = def_mod
-
-    def __typing_subst__(self, arg):
-        msg = "Parameters to generic types must be types."
-        arg = _type_check(arg, msg, is_argument=True)
-        if ((isinstance(arg, _GenericAlias) and arg.__origin__ is Unpack) or
-            (isinstance(arg, GenericAlias) and getattr(arg, '__unpacked__', False))):
-            raise TypeError(f"{arg} is not valid as type argument")
-        return arg
-
-
-class TypeVarTuple(_Final, _Immutable, _PickleUsingNameMixin, _root=True):
-    """Type variable tuple.
-
-    Usage:
-
-      Ts = TypeVarTuple('Ts')  # Can be given any name
-
-    Just as a TypeVar (type variable) is a placeholder for a single type,
-    a TypeVarTuple is a placeholder for an *arbitrary* number of types. For
-    example, if we define a generic class using a TypeVarTuple:
-
-      class C(Generic[*Ts]): ...
-
-    Then we can parameterize that class with an arbitrary number of type
-    arguments:
-
-      C[int]       # Fine
-      C[int, str]  # Also fine
-      C[()]        # Even this is fine
-
-    For more details, see PEP 646.
-
-    Note that only TypeVarTuples defined in global scope can be pickled.
-    """
-
-    def __init__(self, name):
-        self.__name__ = name
-
-        # Used for pickling.
-        def_mod = _caller()
-        if def_mod != 'typing':
-            self.__module__ = def_mod
-
-    def __iter__(self):
-        yield Unpack[self]
-
-    def __repr__(self):
-        return self.__name__
-
-    def __typing_subst__(self, arg):
-        raise TypeError("Substitution of bare TypeVarTuple is not supported")
-
-    def __typing_prepare_subst__(self, alias, args):
-        params = alias.__parameters__
-        typevartuple_index = params.index(self)
-        for param in params[typevartuple_index + 1:]:
-            if isinstance(param, TypeVarTuple):
-                raise TypeError(f"More than one TypeVarTuple parameter in {alias}")
-
-        alen = len(args)
-        plen = len(params)
-        left = typevartuple_index
-        right = plen - typevartuple_index - 1
-        var_tuple_index = None
-        fillarg = None
-        for k, arg in enumerate(args):
-            if not isinstance(arg, type):
-                subargs = getattr(arg, '__typing_unpacked_tuple_args__', None)
-                if subargs and len(subargs) == 2 and subargs[-1] is ...:
-                    if var_tuple_index is not None:
-                        raise TypeError("More than one unpacked arbitrary-length tuple argument")
-                    var_tuple_index = k
-                    fillarg = subargs[0]
-        if var_tuple_index is not None:
-            left = min(left, var_tuple_index)
-            right = min(right, alen - var_tuple_index - 1)
-        elif left + right > alen:
-            raise TypeError(f"Too few arguments for {alias};"
-                            f" actual {alen}, expected at least {plen-1}")
-
-        return (
-            *args[:left],
-            *([fillarg]*(typevartuple_index - left)),
-            tuple(args[left: alen - right]),
-            *([fillarg]*(plen - right - left - typevartuple_index - 1)),
-            *args[alen - right:],
-        )
-
-
-class ParamSpecArgs(_Final, _Immutable, _root=True):
-    """The args for a ParamSpec object.
-
-    Given a ParamSpec object P, P.args is an instance of ParamSpecArgs.
-
-    ParamSpecArgs objects have a reference back to their ParamSpec:
-
-       P.args.__origin__ is P
-
-    This type is meant for runtime introspection and has no special meaning to
-    static type checkers.
-    """
-    def __init__(self, origin):
-        self.__origin__ = origin
-
-    def __repr__(self):
-        return f"{self.__origin__.__name__}.args"
-
-    def __eq__(self, other):
-        if not isinstance(other, ParamSpecArgs):
-            return NotImplemented
-        return self.__origin__ == other.__origin__
-
-
-class ParamSpecKwargs(_Final, _Immutable, _root=True):
-    """The kwargs for a ParamSpec object.
-
-    Given a ParamSpec object P, P.kwargs is an instance of ParamSpecKwargs.
-
-    ParamSpecKwargs objects have a reference back to their ParamSpec:
-
-       P.kwargs.__origin__ is P
-
-    This type is meant for runtime introspection and has no special meaning to
-    static type checkers.
-    """
-    def __init__(self, origin):
-        self.__origin__ = origin
-
-    def __repr__(self):
-        return f"{self.__origin__.__name__}.kwargs"
-
-    def __eq__(self, other):
-        if not isinstance(other, ParamSpecKwargs):
-            return NotImplemented
-        return self.__origin__ == other.__origin__
-
-
-class ParamSpec(_Final, _Immutable, _BoundVarianceMixin, _PickleUsingNameMixin,
-                _root=True):
-    """Parameter specification variable.
-
-    Usage::
-
-       P = ParamSpec('P')
-
-    Parameter specification variables exist primarily for the benefit of static
-    type checkers.  They are used to forward the parameter types of one
-    callable to another callable, a pattern commonly found in higher order
-    functions and decorators.  They are only valid when used in ``Concatenate``,
-    or as the first argument to ``Callable``, or as parameters for user-defined
-    Generics.  See class Generic for more information on generic types.  An
-    example for annotating a decorator::
-
-       T = TypeVar('T')
-       P = ParamSpec('P')
-
-       def add_logging(f: Callable[P, T]) -> Callable[P, T]:
-           '''A type-safe decorator to add logging to a function.'''
-           def inner(*args: P.args, **kwargs: P.kwargs) -> T:
-               logging.info(f'{f.__name__} was called')
-               return f(*args, **kwargs)
-           return inner
-
-       @add_logging
-       def add_two(x: float, y: float) -> float:
-           '''Add two numbers together.'''
-           return x + y
-
-    Parameter specification variables can be introspected. e.g.:
-
-       P.__name__ == 'P'
-
-    Note that only parameter specification variables defined in global scope can
-    be pickled.
-    """
-
-    @property
-    def args(self):
-        return ParamSpecArgs(self)
-
-    @property
-    def kwargs(self):
-        return ParamSpecKwargs(self)
-
-    def __init__(self, name, *, bound=None, covariant=False, contravariant=False):
-        self.__name__ = name
-        super().__init__(bound, covariant, contravariant)
-        def_mod = _caller()
-        if def_mod != 'typing':
-            self.__module__ = def_mod
-
-    def __typing_subst__(self, arg):
-        if isinstance(arg, (list, tuple)):
-            arg = tuple(_type_check(a, "Expected a type.") for a in arg)
-        elif not _is_param_expr(arg):
-            raise TypeError(f"Expected a list of types, an ellipsis, "
-                            f"ParamSpec, or Concatenate. Got {arg}")
-        return arg
-
-    def __typing_prepare_subst__(self, alias, args):
-        params = alias.__parameters__
-        i = params.index(self)
-        if i >= len(args):
-            raise TypeError(f"Too few arguments for {alias}")
-        # Special case where Z[[int, str, bool]] == Z[int, str, bool] in PEP 612.
-        if len(params) == 1 and not _is_param_expr(args[0]):
-            assert i == 0
-            args = (args,)
-        # Convert lists to tuples to help other libraries cache the results.
-        elif isinstance(args[i], list):
-            args = (*args[:i], tuple(args[i]), *args[i+1:])
-        return args
-
-def _is_dunder(attr):
-    return attr.startswith('__') and attr.endswith('__')
-
-class _BaseGenericAlias(_Final, _root=True):
-    """The central part of the internal API.
-
-    This represents a generic version of type 'origin' with type arguments 'params'.
-    There are two kind of these aliases: user defined and special. The special ones
-    are wrappers around builtin collections and ABCs in collections.abc. These must
-    have 'name' always set. If 'inst' is False, then the alias can't be instantiated;
-    this is used by e.g. typing.List and typing.Dict.
-    """
-
-    def __init__(self, origin, *, inst=True, name=None):
-        self._inst = inst
-        self._name = name
-        self.__origin__ = origin
-        self.__slots__ = None  # This is not documented.
-
-    def __call__(self, *args, **kwargs):
-        if not self._inst:
-            raise TypeError(f"Type {self._name} cannot be instantiated; "
-                            f"use {self.__origin__.__name__}() instead")
-        result = self.__origin__(*args, **kwargs)
-        try:
-            result.__orig_class__ = self
-        # Some objects raise TypeError (or something even more exotic)
-        # if you try to set attributes on them; we guard against that here
-        except Exception:
-            pass
-        return result
-
-    def __mro_entries__(self, bases):
-        res = []
-        if self.__origin__ not in bases:
-            res.append(self.__origin__)
-        i = bases.index(self)
-        for b in bases[i+1:]:
-            if isinstance(b, _BaseGenericAlias) or issubclass(b, Generic):
-                break
-        else:
-            res.append(Generic)
-        return tuple(res)
-
-    def __getattr__(self, attr):
-        if attr in {'__name__', '__qualname__'}:
-            return self._name or self.__origin__.__name__
-
-        # We are careful for copy and pickle.
-        # Also for simplicity we don't relay any dunder names
-        if '__origin__' in self.__dict__ and not _is_dunder(attr):
-            return getattr(self.__origin__, attr)
-        raise AttributeError(attr)
-
-    def __setattr__(self, attr, val):
-        if _is_dunder(attr) or attr in {'_name', '_inst', '_nparams',
-                                        '_paramspec_tvars'}:
-            super().__setattr__(attr, val)
-        else:
-            setattr(self.__origin__, attr, val)
-
-    def __instancecheck__(self, obj):
-        return self.__subclasscheck__(type(obj))
-
-    def __subclasscheck__(self, cls):
-        raise TypeError("Subscripted generics cannot be used with"
-                        " class and instance checks")
-
-    def __dir__(self):
-        return list(set(super().__dir__()
-                + [attr for attr in dir(self.__origin__) if not _is_dunder(attr)]))
-
-
-# Special typing constructs Union, Optional, Generic, Callable and Tuple
-# use three special attributes for internal bookkeeping of generic types:
-# * __parameters__ is a tuple of unique free type parameters of a generic
-#   type, for example, Dict[T, T].__parameters__ == (T,);
-# * __origin__ keeps a reference to a type that was subscripted,
-#   e.g., Union[T, int].__origin__ == Union, or the non-generic version of
-#   the type.
-# * __args__ is a tuple of all arguments used in subscripting,
-#   e.g., Dict[T, int].__args__ == (T, int).
-
-
-class _GenericAlias(_BaseGenericAlias, _root=True):
-    # The type of parameterized generics.
-    #
-    # That is, for example, `type(List[int])` is `_GenericAlias`.
-    #
-    # Objects which are instances of this class include:
-    # * Parameterized container types, e.g. `Tuple[int]`, `List[int]`.
-    #  * Note that native container types, e.g. `tuple`, `list`, use
-    #    `types.GenericAlias` instead.
-    # * Parameterized classes:
-    #     T = TypeVar('T')
-    #     class C(Generic[T]): pass
-    #     # C[int] is a _GenericAlias
-    # * `Callable` aliases, generic `Callable` aliases, and
-    #   parameterized `Callable` aliases:
-    #     T = TypeVar('T')
-    #     # _CallableGenericAlias inherits from _GenericAlias.
-    #     A = Callable[[], None]  # _CallableGenericAlias
-    #     B = Callable[[T], None]  # _CallableGenericAlias
-    #     C = B[int]  # _CallableGenericAlias
-    # * Parameterized `Final`, `ClassVar` and `TypeGuard`:
-    #     # All _GenericAlias
-    #     Final[int]
-    #     ClassVar[float]
-    #     TypeVar[bool]
-
-    def __init__(self, origin, args, *, inst=True, name=None,
-                 _paramspec_tvars=False):
-        super().__init__(origin, inst=inst, name=name)
-        if not isinstance(args, tuple):
-            args = (args,)
-        self.__args__ = tuple(... if a is _TypingEllipsis else
-                              a for a in args)
-        self.__parameters__ = _collect_parameters(args)
-        self._paramspec_tvars = _paramspec_tvars
-        if not name:
-            self.__module__ = origin.__module__
-
-    def __eq__(self, other):
-        if not isinstance(other, _GenericAlias):
-            return NotImplemented
-        return (self.__origin__ == other.__origin__
-                and self.__args__ == other.__args__)
-
-    def __hash__(self):
-        return hash((self.__origin__, self.__args__))
-
-    def __or__(self, right):
-        return Union[self, right]
-
-    def __ror__(self, left):
-        return Union[left, self]
-
-    @_tp_cache
-    def __getitem__(self, args):
-        # Parameterizes an already-parameterized object.
-        #
-        # For example, we arrive here doing something like:
-        #   T1 = TypeVar('T1')
-        #   T2 = TypeVar('T2')
-        #   T3 = TypeVar('T3')
-        #   class A(Generic[T1]): pass
-        #   B = A[T2]  # B is a _GenericAlias
-        #   C = B[T3]  # Invokes _GenericAlias.__getitem__
-        #
-        # We also arrive here when parameterizing a generic `Callable` alias:
-        #   T = TypeVar('T')
-        #   C = Callable[[T], None]
-        #   C[int]  # Invokes _GenericAlias.__getitem__
-
-        if self.__origin__ in (Generic, Protocol):
-            # Can't subscript Generic[...] or Protocol[...].
-            raise TypeError(f"Cannot subscript already-subscripted {self}")
-        if not self.__parameters__:
-            raise TypeError(f"{self} is not a generic class")
-
-        # Preprocess `args`.
-        if not isinstance(args, tuple):
-            args = (args,)
-        args = tuple(_type_convert(p) for p in args)
-        args = _unpack_args(args)
-        new_args = self._determine_new_args(args)
-        r = self.copy_with(new_args)
-        return r
-
-    def _determine_new_args(self, args):
-        # Determines new __args__ for __getitem__.
-        #
-        # For example, suppose we had:
-        #   T1 = TypeVar('T1')
-        #   T2 = TypeVar('T2')
-        #   class A(Generic[T1, T2]): pass
-        #   T3 = TypeVar('T3')
-        #   B = A[int, T3]
-        #   C = B[str]
-        # `B.__args__` is `(int, T3)`, so `C.__args__` should be `(int, str)`.
-        # Unfortunately, this is harder than it looks, because if `T3` is
-        # anything more exotic than a plain `TypeVar`, we need to consider
-        # edge cases.
-
-        params = self.__parameters__
-        # In the example above, this would be {T3: str}
-        for param in params:
-            prepare = getattr(param, '__typing_prepare_subst__', None)
-            if prepare is not None:
-                args = prepare(self, args)
-        alen = len(args)
-        plen = len(params)
-        if alen != plen:
-            raise TypeError(f"Too {'many' if alen > plen else 'few'} arguments for {self};"
-                            f" actual {alen}, expected {plen}")
-        new_arg_by_param = dict(zip(params, args))
-        return tuple(self._make_substitution(self.__args__, new_arg_by_param))
-
-    def _make_substitution(self, args, new_arg_by_param):
-        """Create a list of new type arguments."""
-        new_args = []
-        for old_arg in args:
-            if isinstance(old_arg, type):
-                new_args.append(old_arg)
-                continue
-
-            substfunc = getattr(old_arg, '__typing_subst__', None)
-            if substfunc:
-                new_arg = substfunc(new_arg_by_param[old_arg])
-            else:
-                subparams = getattr(old_arg, '__parameters__', ())
-                if not subparams:
-                    new_arg = old_arg
-                else:
-                    subargs = []
-                    for x in subparams:
-                        if isinstance(x, TypeVarTuple):
-                            subargs.extend(new_arg_by_param[x])
-                        else:
-                            subargs.append(new_arg_by_param[x])
-                    new_arg = old_arg[tuple(subargs)]
-
-            if self.__origin__ == collections.abc.Callable and isinstance(new_arg, tuple):
-                # Consider the following `Callable`.
-                #   C = Callable[[int], str]
-                # Here, `C.__args__` should be (int, str) - NOT ([int], str).
-                # That means that if we had something like...
-                #   P = ParamSpec('P')
-                #   T = TypeVar('T')
-                #   C = Callable[P, T]
-                #   D = C[[int, str], float]
-                # ...we need to be careful; `new_args` should end up as
-                # `(int, str, float)` rather than `([int, str], float)`.
-                new_args.extend(new_arg)
-            elif _is_unpacked_typevartuple(old_arg):
-                # Consider the following `_GenericAlias`, `B`:
-                #   class A(Generic[*Ts]): ...
-                #   B = A[T, *Ts]
-                # If we then do:
-                #   B[float, int, str]
-                # The `new_arg` corresponding to `T` will be `float`, and the
-                # `new_arg` corresponding to `*Ts` will be `(int, str)`. We
-                # should join all these types together in a flat list
-                # `(float, int, str)` - so again, we should `extend`.
-                new_args.extend(new_arg)
-            elif isinstance(old_arg, tuple):
-                # Corner case:
-                #    P = ParamSpec('P')
-                #    T = TypeVar('T')
-                #    class Base(Generic[P]): ...
-                # Can be substituted like this:
-                #    X = Base[[int, T]]
-                # In this case, `old_arg` will be a tuple:
-                new_args.append(
-                    tuple(self._make_substitution(old_arg, new_arg_by_param)),
-                )
-            else:
-                new_args.append(new_arg)
-        return new_args
-
-    def copy_with(self, args):
-        return self.__class__(self.__origin__, args, name=self._name, inst=self._inst,
-                              _paramspec_tvars=self._paramspec_tvars)
-
-    def __repr__(self):
-        if self._name:
-            name = 'typing.' + self._name
-        else:
-            name = _type_repr(self.__origin__)
-        if self.__args__:
-            args = ", ".join([_type_repr(a) for a in self.__args__])
-        else:
-            # To ensure the repr is eval-able.
-            args = "()"
-        return f'{name}[{args}]'
-
-    def __reduce__(self):
-        if self._name:
-            origin = globals()[self._name]
-        else:
-            origin = self.__origin__
-        args = tuple(self.__args__)
-        if len(args) == 1 and not isinstance(args[0], tuple):
-            args, = args
-        return operator.getitem, (origin, args)
-
-    def __mro_entries__(self, bases):
-        if isinstance(self.__origin__, _SpecialForm):
-            raise TypeError(f"Cannot subclass {self!r}")
-
-        if self._name:  # generic version of an ABC or built-in class
-            return super().__mro_entries__(bases)
-        if self.__origin__ is Generic:
-            if Protocol in bases:
-                return ()
-            i = bases.index(self)
-            for b in bases[i+1:]:
-                if isinstance(b, _BaseGenericAlias) and b is not self:
-                    return ()
-        return (self.__origin__,)
-
-    def __iter__(self):
-        yield Unpack[self]
-
-
-# _nparams is the number of accepted parameters, e.g. 0 for Hashable,
-# 1 for List and 2 for Dict.  It may be -1 if variable number of
-# parameters are accepted (needs custom __getitem__).
-
-class _SpecialGenericAlias(_NotIterable, _BaseGenericAlias, _root=True):
-    def __init__(self, origin, nparams, *, inst=True, name=None):
-        if name is None:
-            name = origin.__name__
-        super().__init__(origin, inst=inst, name=name)
-        self._nparams = nparams
-        if origin.__module__ == 'builtins':
-            self.__doc__ = f'A generic version of {origin.__qualname__}.'
-        else:
-            self.__doc__ = f'A generic version of {origin.__module__}.{origin.__qualname__}.'
-
-    @_tp_cache
-    def __getitem__(self, params):
-        if not isinstance(params, tuple):
-            params = (params,)
-        msg = "Parameters to generic types must be types."
-        params = tuple(_type_check(p, msg) for p in params)
-        _check_generic(self, params, self._nparams)
-        return self.copy_with(params)
-
-    def copy_with(self, params):
-        return _GenericAlias(self.__origin__, params,
-                             name=self._name, inst=self._inst)
-
-    def __repr__(self):
-        return 'typing.' + self._name
-
-    def __subclasscheck__(self, cls):
-        if isinstance(cls, _SpecialGenericAlias):
-            return issubclass(cls.__origin__, self.__origin__)
-        if not isinstance(cls, _GenericAlias):
-            return issubclass(cls, self.__origin__)
-        return super().__subclasscheck__(cls)
-
-    def __reduce__(self):
-        return self._name
-
-    def __or__(self, right):
-        return Union[self, right]
-
-    def __ror__(self, left):
-        return Union[left, self]
-
-class _CallableGenericAlias(_NotIterable, _GenericAlias, _root=True):
-    def __repr__(self):
-        assert self._name == 'Callable'
-        args = self.__args__
-        if len(args) == 2 and _is_param_expr(args[0]):
-            return super().__repr__()
-        return (f'typing.Callable'
-                f'[[{", ".join([_type_repr(a) for a in args[:-1]])}], '
-                f'{_type_repr(args[-1])}]')
-
-    def __reduce__(self):
-        args = self.__args__
-        if not (len(args) == 2 and _is_param_expr(args[0])):
-            args = list(args[:-1]), args[-1]
-        return operator.getitem, (Callable, args)
-
-
-class _CallableType(_SpecialGenericAlias, _root=True):
-    def copy_with(self, params):
-        return _CallableGenericAlias(self.__origin__, params,
-                                     name=self._name, inst=self._inst,
-                                     _paramspec_tvars=True)
-
-    def __getitem__(self, params):
-        if not isinstance(params, tuple) or len(params) != 2:
-            raise TypeError("Callable must be used as "
-                            "Callable[[arg, ...], result].")
-        args, result = params
-        # This relaxes what args can be on purpose to allow things like
-        # PEP 612 ParamSpec.  Responsibility for whether a user is using
-        # Callable[...] properly is deferred to static type checkers.
-        if isinstance(args, list):
-            params = (tuple(args), result)
-        else:
-            params = (args, result)
-        return self.__getitem_inner__(params)
-
-    @_tp_cache
-    def __getitem_inner__(self, params):
-        args, result = params
-        msg = "Callable[args, result]: result must be a type."
-        result = _type_check(result, msg)
-        if args is Ellipsis:
-            return self.copy_with((_TypingEllipsis, result))
-        if not isinstance(args, tuple):
-            args = (args,)
-        args = tuple(_type_convert(arg) for arg in args)
-        params = args + (result,)
-        return self.copy_with(params)
-
-
-class _TupleType(_SpecialGenericAlias, _root=True):
-    @_tp_cache
-    def __getitem__(self, params):
-        if not isinstance(params, tuple):
-            params = (params,)
-        if len(params) >= 2 and params[-1] is ...:
-            msg = "Tuple[t, ...]: t must be a type."
-            params = tuple(_type_check(p, msg) for p in params[:-1])
-            return self.copy_with((*params, _TypingEllipsis))
-        msg = "Tuple[t0, t1, ...]: each t must be a type."
-        params = tuple(_type_check(p, msg) for p in params)
-        return self.copy_with(params)
-
-
-class _UnionGenericAlias(_NotIterable, _GenericAlias, _root=True):
-    def copy_with(self, params):
-        return Union[params]
-
-    def __eq__(self, other):
-        if not isinstance(other, (_UnionGenericAlias, types.UnionType)):
-            return NotImplemented
-        try:  # fast path
-            return set(self.__args__) == set(other.__args__)
-        except TypeError:  # not hashable, slow path
-            return _compare_args_orderless(self.__args__, other.__args__)
-
-    def __hash__(self):
-        return hash(frozenset(self.__args__))
-
-    def __repr__(self):
-        args = self.__args__
-        if len(args) == 2:
-            if args[0] is type(None):
-                return f'typing.Optional[{_type_repr(args[1])}]'
-            elif args[1] is type(None):
-                return f'typing.Optional[{_type_repr(args[0])}]'
-        return super().__repr__()
-
-    def __instancecheck__(self, obj):
-        return self.__subclasscheck__(type(obj))
-
-    def __subclasscheck__(self, cls):
-        for arg in self.__args__:
-            if issubclass(cls, arg):
-                return True
-
-    def __reduce__(self):
-        func, (origin, args) = super().__reduce__()
-        return func, (Union, args)
-
-
-def _value_and_type_iter(parameters):
-    return ((p, type(p)) for p in parameters)
-
-
-class _LiteralGenericAlias(_GenericAlias, _root=True):
-    def __eq__(self, other):
-        if not isinstance(other, _LiteralGenericAlias):
-            return NotImplemented
-
-        return set(_value_and_type_iter(self.__args__)) == set(_value_and_type_iter(other.__args__))
-
-    def __hash__(self):
-        return hash(frozenset(_value_and_type_iter(self.__args__)))
-
-
-class _ConcatenateGenericAlias(_GenericAlias, _root=True):
-    def copy_with(self, params):
-        if isinstance(params[-1], (list, tuple)):
-            return (*params[:-1], *params[-1])
-        if isinstance(params[-1], _ConcatenateGenericAlias):
-            params = (*params[:-1], *params[-1].__args__)
-        return super().copy_with(params)
-
-
-@_SpecialForm
-def Unpack(self, parameters):
-    """Type unpack operator.
-
-    The type unpack operator takes the child types from some container type,
-    such as `tuple[int, str]` or a `TypeVarTuple`, and 'pulls them out'.
-
-    For example::
-
-        # For some generic class `Foo`:
-        Foo[Unpack[tuple[int, str]]]  # Equivalent to Foo[int, str]
-
-        Ts = TypeVarTuple('Ts')
-        # Specifies that `Bar` is generic in an arbitrary number of types.
-        # (Think of `Ts` as a tuple of an arbitrary number of individual
-        #  `TypeVar`s, which the `Unpack` is 'pulling out' directly into the
-        #  `Generic[]`.)
-        class Bar(Generic[Unpack[Ts]]): ...
-        Bar[int]  # Valid
-        Bar[int, str]  # Also valid
-
-    From Python 3.11, this can also be done using the `*` operator::
-
-        Foo[*tuple[int, str]]
-        class Bar(Generic[*Ts]): ...
-
-    Note that there is only some runtime checking of this operator. Not
-    everything the runtime allows may be accepted by static type checkers.
-
-    For more information, see PEP 646.
-    """
-    item = _type_check(parameters, f'{self} accepts only single type.')
-    return _UnpackGenericAlias(origin=self, args=(item,))
-
-
-class _UnpackGenericAlias(_GenericAlias, _root=True):
-    def __repr__(self):
-        # `Unpack` only takes one argument, so __args__ should contain only
-        # a single item.
-        return '*' + repr(self.__args__[0])
-
-    def __getitem__(self, args):
-        if self.__typing_is_unpacked_typevartuple__:
-            return args
-        return super().__getitem__(args)
-
-    @property
-    def __typing_unpacked_tuple_args__(self):
-        assert self.__origin__ is Unpack
-        assert len(self.__args__) == 1
-        arg, = self.__args__
-        if isinstance(arg, _GenericAlias):
-            assert arg.__origin__ is tuple
-            return arg.__args__
-        return None
-
-    @property
-    def __typing_is_unpacked_typevartuple__(self):
-        assert self.__origin__ is Unpack
-        assert len(self.__args__) == 1
-        return isinstance(self.__args__[0], TypeVarTuple)
-
-
-class Generic:
-    """Abstract base class for generic types.
-
-    A generic type is typically declared by inheriting from
-    this class parameterized with one or more type variables.
-    For example, a generic mapping type might be defined as::
-
-      class Mapping(Generic[KT, VT]):
-          def __getitem__(self, key: KT) -> VT:
-              ...
-          # Etc.
-
-    This class can then be used as follows::
-
-      def lookup_name(mapping: Mapping[KT, VT], key: KT, default: VT) -> VT:
-          try:
-              return mapping[key]
-          except KeyError:
-              return default
-    """
-    __slots__ = ()
-    _is_protocol = False
-
-    @_tp_cache
-    def __class_getitem__(cls, params):
-        """Parameterizes a generic class.
-
-        At least, parameterizing a generic class is the *main* thing this method
-        does. For example, for some generic class `Foo`, this is called when we
-        do `Foo[int]` - there, with `cls=Foo` and `params=int`.
-
-        However, note that this method is also called when defining generic
-        classes in the first place with `class Foo(Generic[T]): ...`.
-        """
-        if not isinstance(params, tuple):
-            params = (params,)
-
-        params = tuple(_type_convert(p) for p in params)
-        if cls in (Generic, Protocol):
-            # Generic and Protocol can only be subscripted with unique type variables.
-            if not params:
-                raise TypeError(
-                    f"Parameter list to {cls.__qualname__}[...] cannot be empty"
-                )
-            if not all(_is_typevar_like(p) for p in params):
-                raise TypeError(
-                    f"Parameters to {cls.__name__}[...] must all be type variables "
-                    f"or parameter specification variables.")
-            if len(set(params)) != len(params):
-                raise TypeError(
-                    f"Parameters to {cls.__name__}[...] must all be unique")
-        else:
-            # Subscripting a regular Generic subclass.
-            for param in cls.__parameters__:
-                prepare = getattr(param, '__typing_prepare_subst__', None)
-                if prepare is not None:
-                    params = prepare(cls, params)
-            _check_generic(cls, params, len(cls.__parameters__))
-
-            new_args = []
-            for param, new_arg in zip(cls.__parameters__, params):
-                if isinstance(param, TypeVarTuple):
-                    new_args.extend(new_arg)
-                else:
-                    new_args.append(new_arg)
-            params = tuple(new_args)
-
-        return _GenericAlias(cls, params,
-                             _paramspec_tvars=True)
-
-    def __init_subclass__(cls, *args, **kwargs):
-        super().__init_subclass__(*args, **kwargs)
-        tvars = []
-        if '__orig_bases__' in cls.__dict__:
-            error = Generic in cls.__orig_bases__
-        else:
-            error = (Generic in cls.__bases__ and
-                        cls.__name__ != 'Protocol' and
-                        type(cls) != _TypedDictMeta)
-        if error:
-            raise TypeError("Cannot inherit from plain Generic")
-        if '__orig_bases__' in cls.__dict__:
-            tvars = _collect_parameters(cls.__orig_bases__)
-            # Look for Generic[T1, ..., Tn].
-            # If found, tvars must be a subset of it.
-            # If not found, tvars is it.
-            # Also check for and reject plain Generic,
-            # and reject multiple Generic[...].
-            gvars = None
-            for base in cls.__orig_bases__:
-                if (isinstance(base, _GenericAlias) and
-                        base.__origin__ is Generic):
-                    if gvars is not None:
-                        raise TypeError(
-                            "Cannot inherit from Generic[...] multiple times.")
-                    gvars = base.__parameters__
-            if gvars is not None:
-                tvarset = set(tvars)
-                gvarset = set(gvars)
-                if not tvarset <= gvarset:
-                    s_vars = ', '.join(str(t) for t in tvars if t not in gvarset)
-                    s_args = ', '.join(str(g) for g in gvars)
-                    raise TypeError(f"Some type variables ({s_vars}) are"
-                                    f" not listed in Generic[{s_args}]")
-                tvars = gvars
-        cls.__parameters__ = tuple(tvars)
-
-
-class _TypingEllipsis:
-    """Internal placeholder for ... (ellipsis)."""
-
-
-_TYPING_INTERNALS = ['__parameters__', '__orig_bases__',  '__orig_class__',
-                     '_is_protocol', '_is_runtime_protocol', '__final__']
-
-_SPECIAL_NAMES = ['__abstractmethods__', '__annotations__', '__dict__', '__doc__',
-                  '__init__', '__module__', '__new__', '__slots__',
-                  '__subclasshook__', '__weakref__', '__class_getitem__']
-
-# These special attributes will be not collected as protocol members.
-EXCLUDED_ATTRIBUTES = _TYPING_INTERNALS + _SPECIAL_NAMES + ['_MutableMapping__marker']
-
-
-def _get_protocol_attrs(cls):
-    """Collect protocol members from a protocol class objects.
-
-    This includes names actually defined in the class dictionary, as well
-    as names that appear in annotations. Special names (above) are skipped.
-    """
-    attrs = set()
-    for base in cls.__mro__[:-1]:  # without object
-        if base.__name__ in ('Protocol', 'Generic'):
-            continue
-        annotations = getattr(base, '__annotations__', {})
-        for attr in list(base.__dict__.keys()) + list(annotations.keys()):
-            if not attr.startswith('_abc_') and attr not in EXCLUDED_ATTRIBUTES:
-                attrs.add(attr)
-    return attrs
-
-
-def _is_callable_members_only(cls):
-    # PEP 544 prohibits using issubclass() with protocols that have non-method members.
-    return all(callable(getattr(cls, attr, None)) for attr in _get_protocol_attrs(cls))
-
-
-def _no_init_or_replace_init(self, *args, **kwargs):
-    cls = type(self)
-
-    if cls._is_protocol:
-        raise TypeError('Protocols cannot be instantiated')
-
-    # Already using a custom `__init__`. No need to calculate correct
-    # `__init__` to call. This can lead to RecursionError. See bpo-45121.
-    if cls.__init__ is not _no_init_or_replace_init:
-        return
-
-    # Initially, `__init__` of a protocol subclass is set to `_no_init_or_replace_init`.
-    # The first instantiation of the subclass will call `_no_init_or_replace_init` which
-    # searches for a proper new `__init__` in the MRO. The new `__init__`
-    # replaces the subclass' old `__init__` (ie `_no_init_or_replace_init`). Subsequent
-    # instantiation of the protocol subclass will thus use the new
-    # `__init__` and no longer call `_no_init_or_replace_init`.
-    for base in cls.__mro__:
-        init = base.__dict__.get('__init__', _no_init_or_replace_init)
-        if init is not _no_init_or_replace_init:
-            cls.__init__ = init
-            break
-    else:
-        # should not happen
-        cls.__init__ = object.__init__
-
-    cls.__init__(self, *args, **kwargs)
-
-
-def _caller(depth=1, default='__main__'):
-    try:
-        return sys._getframe(depth + 1).f_globals.get('__name__', default)
-    except (AttributeError, ValueError):  # For platforms without _getframe()
-        return None
-
-
-def _allow_reckless_class_checks(depth=3):
-    """Allow instance and class checks for special stdlib modules.
-
-    The abc and functools modules indiscriminately call isinstance() and
-    issubclass() on the whole MRO of a user class, which may contain protocols.
-    """
-    return _caller(depth) in {'abc', 'functools', None}
-
-
-_PROTO_ALLOWLIST = {
-    'collections.abc': [
-        'Callable', 'Awaitable', 'Iterable', 'Iterator', 'AsyncIterable',
-        'Hashable', 'Sized', 'Container', 'Collection', 'Reversible',
-    ],
-    'contextlib': ['AbstractContextManager', 'AbstractAsyncContextManager'],
-}
-
-
-class _ProtocolMeta(ABCMeta):
-    # This metaclass is really unfortunate and exists only because of
-    # the lack of __instancehook__.
-    def __instancecheck__(cls, instance):
-        # We need this method for situations where attributes are
-        # assigned in __init__.
-        if (
-            getattr(cls, '_is_protocol', False) and
-            not getattr(cls, '_is_runtime_protocol', False) and
-            not _allow_reckless_class_checks(depth=2)
-        ):
-            raise TypeError("Instance and class checks can only be used with"
-                            " @runtime_checkable protocols")
-
-        if ((not getattr(cls, '_is_protocol', False) or
-                _is_callable_members_only(cls)) and
-                issubclass(instance.__class__, cls)):
-            return True
-        if cls._is_protocol:
-            if all(hasattr(instance, attr) and
-                    # All *methods* can be blocked by setting them to None.
-                    (not callable(getattr(cls, attr, None)) or
-                     getattr(instance, attr) is not None)
-                    for attr in _get_protocol_attrs(cls)):
-                return True
-        return super().__instancecheck__(instance)
-
-
-class Protocol(Generic, metaclass=_ProtocolMeta):
-    """Base class for protocol classes.
-
-    Protocol classes are defined as::
-
-        class Proto(Protocol):
-            def meth(self) -> int:
-                ...
-
-    Such classes are primarily used with static type checkers that recognize
-    structural subtyping (static duck-typing).
-
-    For example::
-
-        class C:
-            def meth(self) -> int:
-                return 0
-
-        def func(x: Proto) -> int:
-            return x.meth()
-
-        func(C())  # Passes static type check
-
-    See PEP 544 for details. Protocol classes decorated with
-    @typing.runtime_checkable act as simple-minded runtime protocols that check
-    only the presence of given attributes, ignoring their type signatures.
-    Protocol classes can be generic, they are defined as::
-
-        class GenProto(Protocol[T]):
-            def meth(self) -> T:
-                ...
-    """
-
-    __slots__ = ()
-    _is_protocol = True
-    _is_runtime_protocol = False
-
-    def __init_subclass__(cls, *args, **kwargs):
-        super().__init_subclass__(*args, **kwargs)
-
-        # Determine if this is a protocol or a concrete subclass.
-        if not cls.__dict__.get('_is_protocol', False):
-            cls._is_protocol = any(b is Protocol for b in cls.__bases__)
-
-        # Set (or override) the protocol subclass hook.
-        def _proto_hook(other):
-            if not cls.__dict__.get('_is_protocol', False):
-                return NotImplemented
-
-            # First, perform various sanity checks.
-            if not getattr(cls, '_is_runtime_protocol', False):
-                if _allow_reckless_class_checks():
-                    return NotImplemented
-                raise TypeError("Instance and class checks can only be used with"
-                                " @runtime_checkable protocols")
-            if not _is_callable_members_only(cls):
-                if _allow_reckless_class_checks():
-                    return NotImplemented
-                raise TypeError("Protocols with non-method members"
-                                " don't support issubclass()")
-            if not isinstance(other, type):
-                # Same error message as for issubclass(1, int).
-                raise TypeError('issubclass() arg 1 must be a class')
-
-            # Second, perform the actual structural compatibility check.
-            for attr in _get_protocol_attrs(cls):
-                for base in other.__mro__:
-                    # Check if the members appears in the class dictionary...
-                    if attr in base.__dict__:
-                        if base.__dict__[attr] is None:
-                            return NotImplemented
-                        break
-
-                    # ...or in annotations, if it is a sub-protocol.
-                    annotations = getattr(base, '__annotations__', {})
-                    if (isinstance(annotations, collections.abc.Mapping) and
-                            attr in annotations and
-                            issubclass(other, Generic) and other._is_protocol):
-                        break
-                else:
-                    return NotImplemented
-            return True
-
-        if '__subclasshook__' not in cls.__dict__:
-            cls.__subclasshook__ = _proto_hook
-
-        # We have nothing more to do for non-protocols...
-        if not cls._is_protocol:
-            return
-
-        # ... otherwise check consistency of bases, and prohibit instantiation.
-        for base in cls.__bases__:
-            if not (base in (object, Generic) or
-                    base.__module__ in _PROTO_ALLOWLIST and
-                    base.__name__ in _PROTO_ALLOWLIST[base.__module__] or
-                    issubclass(base, Generic) and base._is_protocol):
-                raise TypeError('Protocols can only inherit from other'
-                                ' protocols, got %r' % base)
-        if cls.__init__ is Protocol.__init__:
-            cls.__init__ = _no_init_or_replace_init
-
-
-class _AnnotatedAlias(_NotIterable, _GenericAlias, _root=True):
-    """Runtime representation of an annotated type.
-
-    At its core 'Annotated[t, dec1, dec2, ...]' is an alias for the type 't'
-    with extra annotations. The alias behaves like a normal typing alias.
-    Instantiating is the same as instantiating the underlying type; binding
-    it to types is also the same.
-
-    The metadata itself is stored in a '__metadata__' attribute as a tuple.
-    """
-
-    def __init__(self, origin, metadata):
-        if isinstance(origin, _AnnotatedAlias):
-            metadata = origin.__metadata__ + metadata
-            origin = origin.__origin__
-        super().__init__(origin, origin)
-        self.__metadata__ = metadata
-
-    def copy_with(self, params):
-        assert len(params) == 1
-        new_type = params[0]
-        return _AnnotatedAlias(new_type, self.__metadata__)
-
-    def __repr__(self):
-        return "typing.Annotated[{}, {}]".format(
-            _type_repr(self.__origin__),
-            ", ".join(repr(a) for a in self.__metadata__)
-        )
-
-    def __reduce__(self):
-        return operator.getitem, (
-            Annotated, (self.__origin__,) + self.__metadata__
-        )
-
-    def __eq__(self, other):
-        if not isinstance(other, _AnnotatedAlias):
-            return NotImplemented
-        return (self.__origin__ == other.__origin__
-                and self.__metadata__ == other.__metadata__)
-
-    def __hash__(self):
-        return hash((self.__origin__, self.__metadata__))
-
-    def __getattr__(self, attr):
-        if attr in {'__name__', '__qualname__'}:
-            return 'Annotated'
-        return super().__getattr__(attr)
-
-
-class Annotated:
-    """Add context-specific metadata to a type.
-
-    Example: Annotated[int, runtime_check.Unsigned] indicates to the
-    hypothetical runtime_check module that this type is an unsigned int.
-    Every other consumer of this type can ignore this metadata and treat
-    this type as int.
-
-    The first argument to Annotated must be a valid type.
-
-    Details:
-
-    - It's an error to call `Annotated` with less than two arguments.
-    - Access the metadata via the ``__metadata__`` attribute::
-
-        assert Annotated[int, '$'].__metadata__ == ('$',)
-
-    - Nested Annotated types are flattened::
-
-        assert Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3]
-
-    - Instantiating an annotated type is equivalent to instantiating the
-    underlying type::
-
-        assert Annotated[C, Ann1](5) == C(5)
-
-    - Annotated can be used as a generic type alias::
-
-        Optimized: TypeAlias = Annotated[T, runtime.Optimize()]
-        assert Optimized[int] == Annotated[int, runtime.Optimize()]
-
-        OptimizedList: TypeAlias = Annotated[list[T], runtime.Optimize()]
-        assert OptimizedList[int] == Annotated[list[int], runtime.Optimize()]
-
-    - Annotated cannot be used with an unpacked TypeVarTuple::
-
-        Variadic: TypeAlias = Annotated[*Ts, Ann1]  # NOT valid
-
-      This would be equivalent to::
-
-        Annotated[T1, T2, T3, ..., Ann1]
-
-      where T1, T2 etc. are TypeVars, which would be invalid, because
-      only one type should be passed to Annotated.
-    """
-
-    __slots__ = ()
-
-    def __new__(cls, *args, **kwargs):
-        raise TypeError("Type Annotated cannot be instantiated.")
-
-    def __class_getitem__(cls, params):
-        if not isinstance(params, tuple):
-            params = (params,)
-        return cls._class_getitem_inner(cls, *params)
-
-    @_tp_cache(typed=True)
-    def _class_getitem_inner(cls, *params):
-        if len(params) < 2:
-            raise TypeError("Annotated[...] should be used "
-                            "with at least two arguments (a type and an "
-                            "annotation).")
-        if _is_unpacked_typevartuple(params[0]):
-            raise TypeError("Annotated[...] should not be used with an "
-                            "unpacked TypeVarTuple")
-        msg = "Annotated[t, ...]: t must be a type."
-        origin = _type_check(params[0], msg, allow_special_forms=True)
-        metadata = tuple(params[1:])
-        return _AnnotatedAlias(origin, metadata)
-
-    def __init_subclass__(cls, *args, **kwargs):
-        raise TypeError(
-            "Cannot subclass {}.Annotated".format(cls.__module__)
-        )
-
-
-def runtime_checkable(cls):
-    """Mark a protocol class as a runtime protocol.
-
-    Such protocol can be used with isinstance() and issubclass().
-    Raise TypeError if applied to a non-protocol class.
-    This allows a simple-minded structural check very similar to
-    one trick ponies in collections.abc such as Iterable.
-
-    For example::
-
-        @runtime_checkable
-        class Closable(Protocol):
-            def close(self): ...
-
-        assert isinstance(open('/some/file'), Closable)
-
-    Warning: this will check only the presence of the required methods,
-    not their type signatures!
-    """
-    if not issubclass(cls, Generic) or not cls._is_protocol:
-        raise TypeError('@runtime_checkable can be only applied to protocol classes,'
-                        ' got %r' % cls)
-    cls._is_runtime_protocol = True
-    return cls
-
-
-def cast(typ, val):
-    """Cast a value to a type.
-
-    This returns the value unchanged.  To the type checker this
-    signals that the return value has the designated type, but at
-    runtime we intentionally don't check anything (we want this
-    to be as fast as possible).
-    """
-    return val
-
-
-def assert_type(val, typ, /):
-    """Ask a static type checker to confirm that the value is of the given type.
-
-    At runtime this does nothing: it returns the first argument unchanged with no
-    checks or side effects, no matter the actual type of the argument.
-
-    When a static type checker encounters a call to assert_type(), it
-    emits an error if the value is not of the specified type::
-
-        def greet(name: str) -> None:
-            assert_type(name, str)  # OK
-            assert_type(name, int)  # type checker error
-    """
-    return val
-
-
-_allowed_types = (types.FunctionType, types.BuiltinFunctionType,
-                  types.MethodType, types.ModuleType,
-                  WrapperDescriptorType, MethodWrapperType, MethodDescriptorType)
-
-
-def get_type_hints(obj, globalns=None, localns=None, include_extras=False):
-    """Return type hints for an object.
-
-    This is often the same as obj.__annotations__, but it handles
-    forward references encoded as string literals and recursively replaces all
-    'Annotated[T, ...]' with 'T' (unless 'include_extras=True').
-
-    The argument may be a module, class, method, or function. The annotations
-    are returned as a dictionary. For classes, annotations include also
-    inherited members.
-
-    TypeError is raised if the argument is not of a type that can contain
-    annotations, and an empty dictionary is returned if no annotations are
-    present.
-
-    BEWARE -- the behavior of globalns and localns is counterintuitive
-    (unless you are familiar with how eval() and exec() work).  The
-    search order is locals first, then globals.
-
-    - If no dict arguments are passed, an attempt is made to use the
-      globals from obj (or the respective module's globals for classes),
-      and these are also used as the locals.  If the object does not appear
-      to have globals, an empty dictionary is used.  For classes, the search
-      order is globals first then locals.
-
-    - If one dict argument is passed, it is used for both globals and
-      locals.
-
-    - If two dict arguments are passed, they specify globals and
-      locals, respectively.
-    """
-    if getattr(obj, '__no_type_check__', None):
-        return {}
-    # Classes require a special treatment.
-    if isinstance(obj, type):
-        hints = {}
-        for base in reversed(obj.__mro__):
-            if globalns is None:
-                base_globals = getattr(sys.modules.get(base.__module__, None), '__dict__', {})
-            else:
-                base_globals = globalns
-            ann = base.__dict__.get('__annotations__', {})
-            if isinstance(ann, types.GetSetDescriptorType):
-                ann = {}
-            base_locals = dict(vars(base)) if localns is None else localns
-            if localns is None and globalns is None:
-                # This is surprising, but required.  Before Python 3.10,
-                # get_type_hints only evaluated the globalns of
-                # a class.  To maintain backwards compatibility, we reverse
-                # the globalns and localns order so that eval() looks into
-                # *base_globals* first rather than *base_locals*.
-                # This only affects ForwardRefs.
-                base_globals, base_locals = base_locals, base_globals
-            for name, value in ann.items():
-                if value is None:
-                    value = type(None)
-                if isinstance(value, str):
-                    value = ForwardRef(value, is_argument=False, is_class=True)
-                value = _eval_type(value, base_globals, base_locals)
-                hints[name] = value
-        return hints if include_extras else {k: _strip_annotations(t) for k, t in hints.items()}
-
-    if globalns is None:
-        if isinstance(obj, types.ModuleType):
-            globalns = obj.__dict__
-        else:
-            nsobj = obj
-            # Find globalns for the unwrapped object.
-            while hasattr(nsobj, '__wrapped__'):
-                nsobj = nsobj.__wrapped__
-            globalns = getattr(nsobj, '__globals__', {})
-        if localns is None:
-            localns = globalns
-    elif localns is None:
-        localns = globalns
-    hints = getattr(obj, '__annotations__', None)
-    if hints is None:
-        # Return empty annotations for something that _could_ have them.
-        if isinstance(obj, _allowed_types):
-            return {}
-        else:
-            raise TypeError('{!r} is not a module, class, method, '
-                            'or function.'.format(obj))
-    hints = dict(hints)
-    for name, value in hints.items():
-        if value is None:
-            value = type(None)
-        if isinstance(value, str):
-            # class-level forward refs were handled above, this must be either
-            # a module-level annotation or a function argument annotation
-            value = ForwardRef(
-                value,
-                is_argument=not isinstance(obj, types.ModuleType),
-                is_class=False,
-            )
-        hints[name] = _eval_type(value, globalns, localns)
-    return hints if include_extras else {k: _strip_annotations(t) for k, t in hints.items()}
-
-
-def _strip_annotations(t):
-    """Strip the annotations from a given type."""
-    if isinstance(t, _AnnotatedAlias):
-        return _strip_annotations(t.__origin__)
-    if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired):
-        return _strip_annotations(t.__args__[0])
-    if isinstance(t, _GenericAlias):
-        stripped_args = tuple(_strip_annotations(a) for a in t.__args__)
-        if stripped_args == t.__args__:
-            return t
-        return t.copy_with(stripped_args)
-    if isinstance(t, GenericAlias):
-        stripped_args = tuple(_strip_annotations(a) for a in t.__args__)
-        if stripped_args == t.__args__:
-            return t
-        return GenericAlias(t.__origin__, stripped_args)
-    if isinstance(t, types.UnionType):
-        stripped_args = tuple(_strip_annotations(a) for a in t.__args__)
-        if stripped_args == t.__args__:
-            return t
-        return functools.reduce(operator.or_, stripped_args)
-
-    return t
-
-
-def get_origin(tp):
-    """Get the unsubscripted version of a type.
-
-    This supports generic types, Callable, Tuple, Union, Literal, Final, ClassVar,
-    Annotated, and others. Return None for unsupported types.
-
-    Examples::
-
-        >>> P = ParamSpec('P')
-        >>> assert get_origin(Literal[42]) is Literal
-        >>> assert get_origin(int) is None
-        >>> assert get_origin(ClassVar[int]) is ClassVar
-        >>> assert get_origin(Generic) is Generic
-        >>> assert get_origin(Generic[T]) is Generic
-        >>> assert get_origin(Union[T, int]) is Union
-        >>> assert get_origin(List[Tuple[T, T]][int]) is list
-        >>> assert get_origin(P.args) is P
-    """
-    if isinstance(tp, _AnnotatedAlias):
-        return Annotated
-    if isinstance(tp, (_BaseGenericAlias, GenericAlias,
-                       ParamSpecArgs, ParamSpecKwargs)):
-        return tp.__origin__
-    if tp is Generic:
-        return Generic
-    if isinstance(tp, types.UnionType):
-        return types.UnionType
-    return None
-
-
-def get_args(tp):
-    """Get type arguments with all substitutions performed.
-
-    For unions, basic simplifications used by Union constructor are performed.
-
-    Examples::
-
-        >>> T = TypeVar('T')
-        >>> assert get_args(Dict[str, int]) == (str, int)
-        >>> assert get_args(int) == ()
-        >>> assert get_args(Union[int, Union[T, int], str][int]) == (int, str)
-        >>> assert get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])
-        >>> assert get_args(Callable[[], T][int]) == ([], int)
-    """
-    if isinstance(tp, _AnnotatedAlias):
-        return (tp.__origin__,) + tp.__metadata__
-    if isinstance(tp, (_GenericAlias, GenericAlias)):
-        res = tp.__args__
-        if _should_unflatten_callable_args(tp, res):
-            res = (list(res[:-1]), res[-1])
-        return res
-    if isinstance(tp, types.UnionType):
-        return tp.__args__
-    return ()
-
-
-def is_typeddict(tp):
-    """Check if an annotation is a TypedDict class.
-
-    For example::
-
-        >>> from typing import TypedDict
-        >>> class Film(TypedDict):
-        ...     title: str
-        ...     year: int
-        ...
-        >>> is_typeddict(Film)
-        True
-        >>> is_typeddict(dict)
-        False
-    """
-    return isinstance(tp, _TypedDictMeta)
-
-
-_ASSERT_NEVER_REPR_MAX_LENGTH = 100
-
-
-def assert_never(arg: Never, /) -> Never:
-    """Statically assert that a line of code is unreachable.
-
-    Example::
-
-        def int_or_str(arg: int | str) -> None:
-            match arg:
-                case int():
-                    print("It's an int")
-                case str():
-                    print("It's a str")
-                case _:
-                    assert_never(arg)
-
-    If a type checker finds that a call to assert_never() is
-    reachable, it will emit an error.
-
-    At runtime, this throws an exception when called.
-    """
-    value = repr(arg)
-    if len(value) > _ASSERT_NEVER_REPR_MAX_LENGTH:
-        value = value[:_ASSERT_NEVER_REPR_MAX_LENGTH] + '...'
-    raise AssertionError(f"Expected code to be unreachable, but got: {value}")
-
-
-def no_type_check(arg):
-    """Decorator to indicate that annotations are not type hints.
-
-    The argument must be a class or function; if it is a class, it
-    applies recursively to all methods and classes defined in that class
-    (but not to methods defined in its superclasses or subclasses).
-
-    This mutates the function(s) or class(es) in place.
-    """
-    if isinstance(arg, type):
-        for key in dir(arg):
-            obj = getattr(arg, key)
-            if (
-                not hasattr(obj, '__qualname__')
-                or obj.__qualname__ != f'{arg.__qualname__}.{obj.__name__}'
-                or getattr(obj, '__module__', None) != arg.__module__
-            ):
-                # We only modify objects that are defined in this type directly.
-                # If classes / methods are nested in multiple layers,
-                # we will modify them when processing their direct holders.
-                continue
-            # Instance, class, and static methods:
-            if isinstance(obj, types.FunctionType):
-                obj.__no_type_check__ = True
-            if isinstance(obj, types.MethodType):
-                obj.__func__.__no_type_check__ = True
-            # Nested types:
-            if isinstance(obj, type):
-                no_type_check(obj)
-    try:
-        arg.__no_type_check__ = True
-    except TypeError:  # built-in classes
-        pass
-    return arg
-
-
-def no_type_check_decorator(decorator):
-    """Decorator to give another decorator the @no_type_check effect.
-
-    This wraps the decorator with something that wraps the decorated
-    function in @no_type_check.
-    """
-    @functools.wraps(decorator)
-    def wrapped_decorator(*args, **kwds):
-        func = decorator(*args, **kwds)
-        func = no_type_check(func)
-        return func
-
-    return wrapped_decorator
-
-
-def _overload_dummy(*args, **kwds):
-    """Helper for @overload to raise when called."""
-    raise NotImplementedError(
-        "You should not call an overloaded function. "
-        "A series of @overload-decorated functions "
-        "outside a stub module should always be followed "
-        "by an implementation that is not @overload-ed.")
-
-
-# {module: {qualname: {firstlineno: func}}}
-_overload_registry = defaultdict(functools.partial(defaultdict, dict))
-
-
-def overload(func):
-    """Decorator for overloaded functions/methods.
-
-    In a stub file, place two or more stub definitions for the same
-    function in a row, each decorated with @overload.
-
-    For example::
-
-        @overload
-        def utf8(value: None) -> None: ...
-        @overload
-        def utf8(value: bytes) -> bytes: ...
-        @overload
-        def utf8(value: str) -> bytes: ...
-
-    In a non-stub file (i.e. a regular .py file), do the same but
-    follow it with an implementation.  The implementation should *not*
-    be decorated with @overload::
-
-        @overload
-        def utf8(value: None) -> None: ...
-        @overload
-        def utf8(value: bytes) -> bytes: ...
-        @overload
-        def utf8(value: str) -> bytes: ...
-        def utf8(value):
-            ...  # implementation goes here
-
-    The overloads for a function can be retrieved at runtime using the
-    get_overloads() function.
-    """
-    # classmethod and staticmethod
-    f = getattr(func, "__func__", func)
-    try:
-        _overload_registry[f.__module__][f.__qualname__][f.__code__.co_firstlineno] = func
-    except AttributeError:
-        # Not a normal function; ignore.
-        pass
-    return _overload_dummy
-
-
-def get_overloads(func):
-    """Return all defined overloads for *func* as a sequence."""
-    # classmethod and staticmethod
-    f = getattr(func, "__func__", func)
-    if f.__module__ not in _overload_registry:
-        return []
-    mod_dict = _overload_registry[f.__module__]
-    if f.__qualname__ not in mod_dict:
-        return []
-    return list(mod_dict[f.__qualname__].values())
-
-
-def clear_overloads():
-    """Clear all overloads in the registry."""
-    _overload_registry.clear()
-
-
-def final(f):
-    """Decorator to indicate final methods and final classes.
-
-    Use this decorator to indicate to type checkers that the decorated
-    method cannot be overridden, and decorated class cannot be subclassed.
-
-    For example::
-
-        class Base:
-            @final
-            def done(self) -> None:
-                ...
-        class Sub(Base):
-            def done(self) -> None:  # Error reported by type checker
-                ...
-
-        @final
-        class Leaf:
-            ...
-        class Other(Leaf):  # Error reported by type checker
-            ...
-
-    There is no runtime checking of these properties. The decorator
-    attempts to set the ``__final__`` attribute to ``True`` on the decorated
-    object to allow runtime introspection.
-    """
-    try:
-        f.__final__ = True
-    except (AttributeError, TypeError):
-        # Skip the attribute silently if it is not writable.
-        # AttributeError happens if the object has __slots__ or a
-        # read-only property, TypeError if it's a builtin class.
-        pass
-    return f
-
-
-# Some unconstrained type variables.  These are used by the container types.
-# (These are not for export.)
-T = TypeVar('T')  # Any type.
-KT = TypeVar('KT')  # Key type.
-VT = TypeVar('VT')  # Value type.
-T_co = TypeVar('T_co', covariant=True)  # Any type covariant containers.
-V_co = TypeVar('V_co', covariant=True)  # Any type covariant containers.
-VT_co = TypeVar('VT_co', covariant=True)  # Value type covariant containers.
-T_contra = TypeVar('T_contra', contravariant=True)  # Ditto contravariant.
-# Internal type variable used for Type[].
-CT_co = TypeVar('CT_co', covariant=True, bound=type)
-
-# A useful type variable with constraints.  This represents string types.
-# (This one *is* for export!)
-AnyStr = TypeVar('AnyStr', bytes, str)
-
-
-# Various ABCs mimicking those in collections.abc.
-_alias = _SpecialGenericAlias
-
-Hashable = _alias(collections.abc.Hashable, 0)  # Not generic.
-Awaitable = _alias(collections.abc.Awaitable, 1)
-Coroutine = _alias(collections.abc.Coroutine, 3)
-AsyncIterable = _alias(collections.abc.AsyncIterable, 1)
-AsyncIterator = _alias(collections.abc.AsyncIterator, 1)
-Iterable = _alias(collections.abc.Iterable, 1)
-Iterator = _alias(collections.abc.Iterator, 1)
-Reversible = _alias(collections.abc.Reversible, 1)
-Sized = _alias(collections.abc.Sized, 0)  # Not generic.
-Container = _alias(collections.abc.Container, 1)
-Collection = _alias(collections.abc.Collection, 1)
-Callable = _CallableType(collections.abc.Callable, 2)
-Callable.__doc__ = \
-    """Deprecated alias to collections.abc.Callable.
-
-    Callable[[int], str] signifies a function that takes a single
-    parameter of type int and returns a str.
-
-    The subscription syntax must always be used with exactly two
-    values: the argument list and the return type.
-    The argument list must be a list of types, a ParamSpec,
-    Concatenate or ellipsis. The return type must be a single type.
-
-    There is no syntax to indicate optional or keyword arguments;
-    such function types are rarely used as callback types.
-    """
-AbstractSet = _alias(collections.abc.Set, 1, name='AbstractSet')
-MutableSet = _alias(collections.abc.MutableSet, 1)
-# NOTE: Mapping is only covariant in the value type.
-Mapping = _alias(collections.abc.Mapping, 2)
-MutableMapping = _alias(collections.abc.MutableMapping, 2)
-Sequence = _alias(collections.abc.Sequence, 1)
-MutableSequence = _alias(collections.abc.MutableSequence, 1)
-ByteString = _alias(collections.abc.ByteString, 0)  # Not generic
-# Tuple accepts variable number of parameters.
-Tuple = _TupleType(tuple, -1, inst=False, name='Tuple')
-Tuple.__doc__ = \
-    """Deprecated alias to builtins.tuple.
-
-    Tuple[X, Y] is the cross-product type of X and Y.
-
-    Example: Tuple[T1, T2] is a tuple of two elements corresponding
-    to type variables T1 and T2.  Tuple[int, float, str] is a tuple
-    of an int, a float and a string.
-
-    To specify a variable-length tuple of homogeneous type, use Tuple[T, ...].
-    """
-List = _alias(list, 1, inst=False, name='List')
-Deque = _alias(collections.deque, 1, name='Deque')
-Set = _alias(set, 1, inst=False, name='Set')
-FrozenSet = _alias(frozenset, 1, inst=False, name='FrozenSet')
-MappingView = _alias(collections.abc.MappingView, 1)
-KeysView = _alias(collections.abc.KeysView, 1)
-ItemsView = _alias(collections.abc.ItemsView, 2)
-ValuesView = _alias(collections.abc.ValuesView, 1)
-ContextManager = _alias(contextlib.AbstractContextManager, 1, name='ContextManager')
-AsyncContextManager = _alias(contextlib.AbstractAsyncContextManager, 1, name='AsyncContextManager')
-Dict = _alias(dict, 2, inst=False, name='Dict')
-DefaultDict = _alias(collections.defaultdict, 2, name='DefaultDict')
-OrderedDict = _alias(collections.OrderedDict, 2)
-Counter = _alias(collections.Counter, 1)
-ChainMap = _alias(collections.ChainMap, 2)
-Generator = _alias(collections.abc.Generator, 3)
-AsyncGenerator = _alias(collections.abc.AsyncGenerator, 2)
-Type = _alias(type, 1, inst=False, name='Type')
-Type.__doc__ = \
-    """Deprecated alias to builtins.type.
-
-    builtins.type or typing.Type can be used to annotate class objects.
-    For example, suppose we have the following classes::
-
-        class User: ...  # Abstract base for User classes
-        class BasicUser(User): ...
-        class ProUser(User): ...
-        class TeamUser(User): ...
-
-    And a function that takes a class argument that's a subclass of
-    User and returns an instance of the corresponding class::
-
-        U = TypeVar('U', bound=User)
-        def new_user(user_class: Type[U]) -> U:
-            user = user_class()
-            # (Here we could write the user object to a database)
-            return user
-
-        joe = new_user(BasicUser)
-
-    At this point the type checker knows that joe has type BasicUser.
-    """
-
-
-@runtime_checkable
-class SupportsInt(Protocol):
-    """An ABC with one abstract method __int__."""
-
-    __slots__ = ()
-
-    @abstractmethod
-    def __int__(self) -> int:
-        pass
-
-
-@runtime_checkable
-class SupportsFloat(Protocol):
-    """An ABC with one abstract method __float__."""
-
-    __slots__ = ()
-
-    @abstractmethod
-    def __float__(self) -> float:
-        pass
-
-
-@runtime_checkable
-class SupportsComplex(Protocol):
-    """An ABC with one abstract method __complex__."""
-
-    __slots__ = ()
-
-    @abstractmethod
-    def __complex__(self) -> complex:
-        pass
-
-
-@runtime_checkable
-class SupportsBytes(Protocol):
-    """An ABC with one abstract method __bytes__."""
-
-    __slots__ = ()
-
-    @abstractmethod
-    def __bytes__(self) -> bytes:
-        pass
-
-
-@runtime_checkable
-class SupportsIndex(Protocol):
-    """An ABC with one abstract method __index__."""
-
-    __slots__ = ()
-
-    @abstractmethod
-    def __index__(self) -> int:
-        pass
-
-
-@runtime_checkable
-class SupportsAbs(Protocol[T_co]):
-    """An ABC with one abstract method __abs__ that is covariant in its return type."""
-
-    __slots__ = ()
-
-    @abstractmethod
-    def __abs__(self) -> T_co:
-        pass
-
-
-@runtime_checkable
-class SupportsRound(Protocol[T_co]):
-    """An ABC with one abstract method __round__ that is covariant in its return type."""
-
-    __slots__ = ()
-
-    @abstractmethod
-    def __round__(self, ndigits: int = 0) -> T_co:
-        pass
-
-
-def _make_nmtuple(name, types, module, defaults = ()):
-    fields = [n for n, t in types]
-    types = {n: _type_check(t, f"field {n} annotation must be a type")
-             for n, t in types}
-    nm_tpl = collections.namedtuple(name, fields,
-                                    defaults=defaults, module=module)
-    nm_tpl.__annotations__ = nm_tpl.__new__.__annotations__ = types
-    return nm_tpl
-
-
-# attributes prohibited to set in NamedTuple class syntax
-_prohibited = frozenset({'__new__', '__init__', '__slots__', '__getnewargs__',
-                         '_fields', '_field_defaults',
-                         '_make', '_replace', '_asdict', '_source'})
-
-_special = frozenset({'__module__', '__name__', '__annotations__'})
-
-
-class NamedTupleMeta(type):
-    def __new__(cls, typename, bases, ns):
-        assert _NamedTuple in bases
-        for base in bases:
-            if base is not _NamedTuple and base is not Generic:
-                raise TypeError(
-                    'can only inherit from a NamedTuple type and Generic')
-        bases = tuple(tuple if base is _NamedTuple else base for base in bases)
-        types = ns.get('__annotations__', {})
-        default_names = []
-        for field_name in types:
-            if field_name in ns:
-                default_names.append(field_name)
-            elif default_names:
-                raise TypeError(f"Non-default namedtuple field {field_name} "
-                                f"cannot follow default field"
-                                f"{'s' if len(default_names) > 1 else ''} "
-                                f"{', '.join(default_names)}")
-        nm_tpl = _make_nmtuple(typename, types.items(),
-                               defaults=[ns[n] for n in default_names],
-                               module=ns['__module__'])
-        nm_tpl.__bases__ = bases
-        if Generic in bases:
-            class_getitem = Generic.__class_getitem__.__func__
-            nm_tpl.__class_getitem__ = classmethod(class_getitem)
-        # update from user namespace without overriding special namedtuple attributes
-        for key in ns:
-            if key in _prohibited:
-                raise AttributeError("Cannot overwrite NamedTuple attribute " + key)
-            elif key not in _special and key not in nm_tpl._fields:
-                setattr(nm_tpl, key, ns[key])
-        if Generic in bases:
-            nm_tpl.__init_subclass__()
-        return nm_tpl
-
-
-def NamedTuple(typename, fields=None, /, **kwargs):
-    """Typed version of namedtuple.
-
-    Usage::
-
-        class Employee(NamedTuple):
-            name: str
-            id: int
-
-    This is equivalent to::
-
-        Employee = collections.namedtuple('Employee', ['name', 'id'])
-
-    The resulting class has an extra __annotations__ attribute, giving a
-    dict that maps field names to types.  (The field names are also in
-    the _fields attribute, which is part of the namedtuple API.)
-    An alternative equivalent functional syntax is also accepted::
-
-        Employee = NamedTuple('Employee', [('name', str), ('id', int)])
-    """
-    if fields is None:
-        fields = kwargs.items()
-    elif kwargs:
-        raise TypeError("Either list of fields or keywords"
-                        " can be provided to NamedTuple, not both")
-    return _make_nmtuple(typename, fields, module=_caller())
-
-_NamedTuple = type.__new__(NamedTupleMeta, 'NamedTuple', (), {})
-
-def _namedtuple_mro_entries(bases):
-    assert NamedTuple in bases
-    return (_NamedTuple,)
-
-NamedTuple.__mro_entries__ = _namedtuple_mro_entries
-
-
-class _TypedDictMeta(type):
-    def __new__(cls, name, bases, ns, total=True):
-        """Create a new typed dict class object.
-
-        This method is called when TypedDict is subclassed,
-        or when TypedDict is instantiated. This way
-        TypedDict supports all three syntax forms described in its docstring.
-        Subclasses and instances of TypedDict return actual dictionaries.
-        """
-        for base in bases:
-            if type(base) is not _TypedDictMeta and base is not Generic:
-                raise TypeError('cannot inherit from both a TypedDict type '
-                                'and a non-TypedDict base class')
-
-        if any(issubclass(b, Generic) for b in bases):
-            generic_base = (Generic,)
-        else:
-            generic_base = ()
-
-        tp_dict = type.__new__(_TypedDictMeta, name, (*generic_base, dict), ns)
-
-        annotations = {}
-        own_annotations = ns.get('__annotations__', {})
-        msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type"
-        own_annotations = {
-            n: _type_check(tp, msg, module=tp_dict.__module__)
-            for n, tp in own_annotations.items()
-        }
-        required_keys = set()
-        optional_keys = set()
-
-        for base in bases:
-            annotations.update(base.__dict__.get('__annotations__', {}))
-
-            base_required = base.__dict__.get('__required_keys__', set())
-            required_keys |= base_required
-            optional_keys -= base_required
-
-            base_optional = base.__dict__.get('__optional_keys__', set())
-            required_keys -= base_optional
-            optional_keys |= base_optional
-
-        annotations.update(own_annotations)
-        for annotation_key, annotation_type in own_annotations.items():
-            annotation_origin = get_origin(annotation_type)
-            if annotation_origin is Annotated:
-                annotation_args = get_args(annotation_type)
-                if annotation_args:
-                    annotation_type = annotation_args[0]
-                    annotation_origin = get_origin(annotation_type)
-
-            if annotation_origin is Required:
-                is_required = True
-            elif annotation_origin is NotRequired:
-                is_required = False
-            else:
-                is_required = total
-
-            if is_required:
-                required_keys.add(annotation_key)
-                optional_keys.discard(annotation_key)
-            else:
-                optional_keys.add(annotation_key)
-                required_keys.discard(annotation_key)
-
-        assert required_keys.isdisjoint(optional_keys), (
-            f"Required keys overlap with optional keys in {name}:"
-            f" {required_keys=}, {optional_keys=}"
-        )
-        tp_dict.__annotations__ = annotations
-        tp_dict.__required_keys__ = frozenset(required_keys)
-        tp_dict.__optional_keys__ = frozenset(optional_keys)
-        if not hasattr(tp_dict, '__total__'):
-            tp_dict.__total__ = total
-        return tp_dict
-
-    __call__ = dict  # static method
-
-    def __subclasscheck__(cls, other):
-        # Typed dicts are only for static structural subtyping.
-        raise TypeError('TypedDict does not support instance and class checks')
-
-    __instancecheck__ = __subclasscheck__
-
-
-def TypedDict(typename, fields=None, /, *, total=True, **kwargs):
-    """A simple typed namespace. At runtime it is equivalent to a plain dict.
-
-    TypedDict creates a dictionary type such that a type checker will expect all
-    instances to have a certain set of keys, where each key is
-    associated with a value of a consistent type. This expectation
-    is not checked at runtime.
-
-    Usage::
-
-        >>> class Point2D(TypedDict):
-        ...     x: int
-        ...     y: int
-        ...     label: str
-        ...
-        >>> a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
-        >>> b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check
-        >>> Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
-        True
-
-    The type info can be accessed via the Point2D.__annotations__ dict, and
-    the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets.
-    TypedDict supports an additional equivalent form::
-
-        Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
-
-    By default, all keys must be present in a TypedDict. It is possible
-    to override this by specifying totality::
-
-        class Point2D(TypedDict, total=False):
-            x: int
-            y: int
-
-    This means that a Point2D TypedDict can have any of the keys omitted. A type
-    checker is only expected to support a literal False or True as the value of
-    the total argument. True is the default, and makes all items defined in the
-    class body be required.
-
-    The Required and NotRequired special forms can also be used to mark
-    individual keys as being required or not required::
-
-        class Point2D(TypedDict):
-            x: int               # the "x" key must always be present (Required is the default)
-            y: NotRequired[int]  # the "y" key can be omitted
-
-    See PEP 655 for more details on Required and NotRequired.
-    """
-    if fields is None:
-        fields = kwargs
-    elif kwargs:
-        raise TypeError("TypedDict takes either a dict or keyword arguments,"
-                        " but not both")
-    if kwargs:
-        warnings.warn(
-            "The kwargs-based syntax for TypedDict definitions is deprecated "
-            "in Python 3.11, will be removed in Python 3.13, and may not be "
-            "understood by third-party type checkers.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-    ns = {'__annotations__': dict(fields)}
-    module = _caller()
-    if module is not None:
-        # Setting correct module is necessary to make typed dict classes pickleable.
-        ns['__module__'] = module
-
-    return _TypedDictMeta(typename, (), ns, total=total)
-
-_TypedDict = type.__new__(_TypedDictMeta, 'TypedDict', (), {})
-TypedDict.__mro_entries__ = lambda bases: (_TypedDict,)
-
-
-@_SpecialForm
-def Required(self, parameters):
-    """Special typing construct to mark a TypedDict key as required.
-
-    This is mainly useful for total=False TypedDicts.
-
-    For example::
-
-        class Movie(TypedDict, total=False):
-            title: Required[str]
-            year: int
-
-        m = Movie(
-            title='The Matrix',  # typechecker error if key is omitted
-            year=1999,
-        )
-
-    There is no runtime checking that a required key is actually provided
-    when instantiating a related TypedDict.
-    """
-    item = _type_check(parameters, f'{self._name} accepts only a single type.')
-    return _GenericAlias(self, (item,))
-
-
-@_SpecialForm
-def NotRequired(self, parameters):
-    """Special typing construct to mark a TypedDict key as potentially missing.
-
-    For example::
-
-        class Movie(TypedDict):
-            title: str
-            year: NotRequired[int]
-
-        m = Movie(
-            title='The Matrix',  # typechecker error if key is omitted
-            year=1999,
-        )
-    """
-    item = _type_check(parameters, f'{self._name} accepts only a single type.')
-    return _GenericAlias(self, (item,))
-
-
-class NewType:
-    """NewType creates simple unique types with almost zero runtime overhead.
-
-    NewType(name, tp) is considered a subtype of tp
-    by static type checkers. At runtime, NewType(name, tp) returns
-    a dummy callable that simply returns its argument.
-
-    Usage::
-
-        UserId = NewType('UserId', int)
-
-        def name_by_id(user_id: UserId) -> str:
-            ...
-
-        UserId('user')          # Fails type check
-
-        name_by_id(42)          # Fails type check
-        name_by_id(UserId(42))  # OK
-
-        num = UserId(5) + 1     # type: int
-    """
-
-    __call__ = _idfunc
-
-    def __init__(self, name, tp):
-        self.__qualname__ = name
-        if '.' in name:
-            name = name.rpartition('.')[-1]
-        self.__name__ = name
-        self.__supertype__ = tp
-        def_mod = _caller()
-        if def_mod != 'typing':
-            self.__module__ = def_mod
-
-    def __mro_entries__(self, bases):
-        # We defined __mro_entries__ to get a better error message
-        # if a user attempts to subclass a NewType instance. bpo-46170
-        superclass_name = self.__name__
-
-        class Dummy:
-            def __init_subclass__(cls):
-                subclass_name = cls.__name__
-                raise TypeError(
-                    f"Cannot subclass an instance of NewType. Perhaps you were looking for: "
-                    f"`{subclass_name} = NewType({subclass_name!r}, {superclass_name})`"
-                )
-
-        return (Dummy,)
-
-    def __repr__(self):
-        return f'{self.__module__}.{self.__qualname__}'
-
-    def __reduce__(self):
-        return self.__qualname__
-
-    def __or__(self, other):
-        return Union[self, other]
-
-    def __ror__(self, other):
-        return Union[other, self]
-
-
-# Python-version-specific alias (Python 2: unicode; Python 3: str)
-Text = str
-
-
-# Constant that's True when type checking, but False here.
-TYPE_CHECKING = False
-
-
-class IO(Generic[AnyStr]):
-    """Generic base class for TextIO and BinaryIO.
-
-    This is an abstract, generic version of the return of open().
-
-    NOTE: This does not distinguish between the different possible
-    classes (text vs. binary, read vs. write vs. read/write,
-    append-only, unbuffered).  The TextIO and BinaryIO subclasses
-    below capture the distinctions between text vs. binary, which is
-    pervasive in the interface; however we currently do not offer a
-    way to track the other distinctions in the type system.
-    """
-
-    __slots__ = ()
-
-    @property
-    @abstractmethod
-    def mode(self) -> str:
-        pass
-
-    @property
-    @abstractmethod
-    def name(self) -> str:
-        pass
-
-    @abstractmethod
-    def close(self) -> None:
-        pass
-
-    @property
-    @abstractmethod
-    def closed(self) -> bool:
-        pass
-
-    @abstractmethod
-    def fileno(self) -> int:
-        pass
-
-    @abstractmethod
-    def flush(self) -> None:
-        pass
-
-    @abstractmethod
-    def isatty(self) -> bool:
-        pass
-
-    @abstractmethod
-    def read(self, n: int = -1) -> AnyStr:
-        pass
-
-    @abstractmethod
-    def readable(self) -> bool:
-        pass
-
-    @abstractmethod
-    def readline(self, limit: int = -1) -> AnyStr:
-        pass
-
-    @abstractmethod
-    def readlines(self, hint: int = -1) -> List[AnyStr]:
-        pass
-
-    @abstractmethod
-    def seek(self, offset: int, whence: int = 0) -> int:
-        pass
-
-    @abstractmethod
-    def seekable(self) -> bool:
-        pass
-
-    @abstractmethod
-    def tell(self) -> int:
-        pass
-
-    @abstractmethod
-    def truncate(self, size: int = None) -> int:
-        pass
-
-    @abstractmethod
-    def writable(self) -> bool:
-        pass
-
-    @abstractmethod
-    def write(self, s: AnyStr) -> int:
-        pass
-
-    @abstractmethod
-    def writelines(self, lines: List[AnyStr]) -> None:
-        pass
-
-    @abstractmethod
-    def __enter__(self) -> 'IO[AnyStr]':
-        pass
-
-    @abstractmethod
-    def __exit__(self, type, value, traceback) -> None:
-        pass
-
-
-class BinaryIO(IO[bytes]):
-    """Typed version of the return of open() in binary mode."""
-
-    __slots__ = ()
-
-    @abstractmethod
-    def write(self, s: Union[bytes, bytearray]) -> int:
-        pass
-
-    @abstractmethod
-    def __enter__(self) -> 'BinaryIO':
-        pass
-
-
-class TextIO(IO[str]):
-    """Typed version of the return of open() in text mode."""
-
-    __slots__ = ()
-
-    @property
-    @abstractmethod
-    def buffer(self) -> BinaryIO:
-        pass
-
-    @property
-    @abstractmethod
-    def encoding(self) -> str:
-        pass
-
-    @property
-    @abstractmethod
-    def errors(self) -> Optional[str]:
-        pass
-
-    @property
-    @abstractmethod
-    def line_buffering(self) -> bool:
-        pass
-
-    @property
-    @abstractmethod
-    def newlines(self) -> Any:
-        pass
-
-    @abstractmethod
-    def __enter__(self) -> 'TextIO':
-        pass
-
-
-class _DeprecatedType(type):
-    def __getattribute__(cls, name):
-        if name not in {"__dict__", "__module__", "__doc__"} and name in cls.__dict__:
-            warnings.warn(
-                f"{cls.__name__} is deprecated, import directly "
-                f"from typing instead. {cls.__name__} will be removed "
-                "in Python 3.12.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-        return super().__getattribute__(name)
-
-
-class io(metaclass=_DeprecatedType):
-    """Wrapper namespace for IO generic classes."""
-
-    __all__ = ['IO', 'TextIO', 'BinaryIO']
-    IO = IO
-    TextIO = TextIO
-    BinaryIO = BinaryIO
-
-
-io.__name__ = __name__ + '.io'
-sys.modules[io.__name__] = io
-
-Pattern = _alias(stdlib_re.Pattern, 1)
-Match = _alias(stdlib_re.Match, 1)
-
-class re(metaclass=_DeprecatedType):
-    """Wrapper namespace for re type aliases."""
-
-    __all__ = ['Pattern', 'Match']
-    Pattern = Pattern
-    Match = Match
-
-
-re.__name__ = __name__ + '.re'
-sys.modules[re.__name__] = re
-
-
-def reveal_type(obj: T, /) -> T:
-    """Ask a static type checker to reveal the inferred type of an expression.
-
-    When a static type checker encounters a call to ``reveal_type()``,
-    it will emit the inferred type of the argument::
-
-        x: int = 1
-        reveal_type(x)
-
-    Running a static type checker (e.g., mypy) on this example
-    will produce output similar to 'Revealed type is "builtins.int"'.
-
-    At runtime, the function prints the runtime type of the
-    argument and returns the argument unchanged.
-    """
-    print(f"Runtime type is {type(obj).__name__!r}", file=sys.stderr)
-    return obj
-
-
-def dataclass_transform(
-    *,
-    eq_default: bool = True,
-    order_default: bool = False,
-    kw_only_default: bool = False,
-    field_specifiers: tuple[type[Any] | Callable[..., Any], ...] = (),
-    **kwargs: Any,
-) -> Callable[[T], T]:
-    """Decorator to mark an object as providing dataclass-like behaviour.
-
-    The decorator can be applied to a function, class, or metaclass.
-
-    Example usage with a decorator function::
-
-        T = TypeVar("T")
-
-        @dataclass_transform()
-        def create_model(cls: type[T]) -> type[T]:
-            ...
-            return cls
-
-        @create_model
-        class CustomerModel:
-            id: int
-            name: str
-
-    On a base class::
-
-        @dataclass_transform()
-        class ModelBase: ...
-
-        class CustomerModel(ModelBase):
-            id: int
-            name: str
-
-    On a metaclass::
-
-        @dataclass_transform()
-        class ModelMeta(type): ...
-
-        class ModelBase(metaclass=ModelMeta): ...
-
-        class CustomerModel(ModelBase):
-            id: int
-            name: str
-
-    The ``CustomerModel`` classes defined above will
-    be treated by type checkers similarly to classes created with
-    ``@dataclasses.dataclass``.
-    For example, type checkers will assume these classes have
-    ``__init__`` methods that accept ``id`` and ``name``.
-
-    The arguments to this decorator can be used to customize this behavior:
-    - ``eq_default`` indicates whether the ``eq`` parameter is assumed to be
-        ``True`` or ``False`` if it is omitted by the caller.
-    - ``order_default`` indicates whether the ``order`` parameter is
-        assumed to be True or False if it is omitted by the caller.
-    - ``kw_only_default`` indicates whether the ``kw_only`` parameter is
-        assumed to be True or False if it is omitted by the caller.
-    - ``field_specifiers`` specifies a static list of supported classes
-        or functions that describe fields, similar to ``dataclasses.field()``.
-    - Arbitrary other keyword arguments are accepted in order to allow for
-        possible future extensions.
-
-    At runtime, this decorator records its arguments in the
-    ``__dataclass_transform__`` attribute on the decorated object.
-    It has no other runtime effect.
-
-    See PEP 681 for more details.
-    """
-    def decorator(cls_or_fn):
-        cls_or_fn.__dataclass_transform__ = {
-            "eq_default": eq_default,
-            "order_default": order_default,
-            "kw_only_default": kw_only_default,
-            "field_specifiers": field_specifiers,
-            "kwargs": kwargs,
-        }
-        return cls_or_fn
-    return decorator
-
- -
-
-
- -
- -
-

© Copyright 2024, NotDiamond. - Last updated on Nov 22, 2024. -

-
- - Built with Sphinx using a - theme - provided by Read the Docs. - - -
-
-
-
-
- - - - diff --git a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js deleted file mode 100644 index 81415803..00000000 --- a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js +++ /dev/null @@ -1,123 +0,0 @@ -/* Compatability shim for jQuery and underscores.js. - * - * Copyright Sphinx contributors - * Released under the two clause BSD licence - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css deleted file mode 100644 index f316efcb..00000000 --- a/docs/_build/html/_static/basic.css +++ /dev/null @@ -1,925 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - -div.section::after { - display: block; - content: ''; - clear: left; -} - -/* -- relbar ---------------------------------------------------------------- */ - -div.related { - width: 100%; - font-size: 90%; -} - -div.related h3 { - display: none; -} - -div.related ul { - margin: 0; - padding: 0 0 0 10px; - list-style: none; -} - -div.related li { - display: inline; -} - -div.related li.right { - float: right; - margin-right: 5px; -} - -/* -- sidebar --------------------------------------------------------------- */ - -div.sphinxsidebarwrapper { - padding: 10px 5px 0 10px; -} - -div.sphinxsidebar { - float: left; - width: 230px; - margin-left: -100%; - font-size: 90%; - word-wrap: break-word; - overflow-wrap : break-word; -} - -div.sphinxsidebar ul { - list-style: none; -} - -div.sphinxsidebar ul ul, -div.sphinxsidebar ul.want-points { - margin-left: 20px; - list-style: square; -} - -div.sphinxsidebar ul ul { - margin-top: 0; - margin-bottom: 0; -} - -div.sphinxsidebar form { - margin-top: 10px; -} - -div.sphinxsidebar input { - border: 1px solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - -div.sphinxsidebar #searchbox form.search { - overflow: hidden; -} - -div.sphinxsidebar #searchbox input[type="text"] { - float: left; - width: 80%; - padding: 0.25em; - box-sizing: border-box; -} - -div.sphinxsidebar #searchbox input[type="submit"] { - float: left; - width: 20%; - border-left: none; - padding: 0.25em; - box-sizing: border-box; -} - - -img { - border: 0; - max-width: 100%; -} - -/* -- search page ----------------------------------------------------------- */ - -ul.search { - margin: 10px 0 0 20px; - padding: 0; -} - -ul.search li { - padding: 5px 0 5px 20px; - background-image: url(file.png); - background-repeat: no-repeat; - background-position: 0 7px; -} - -ul.search li a { - font-weight: bold; -} - -ul.search li p.context { - color: #888; - margin: 2px 0 0 30px; - text-align: left; -} - -ul.keywordmatches li.goodmatch a { - font-weight: bold; -} - -/* -- index page ------------------------------------------------------------ */ - -table.contentstable { - width: 90%; - margin-left: auto; - margin-right: auto; -} - -table.contentstable p.biglink { - line-height: 150%; -} - -a.biglink { - font-size: 1.3em; -} - -span.linkdescr { - font-style: italic; - padding-top: 5px; - font-size: 90%; -} - -/* -- general index --------------------------------------------------------- */ - -table.indextable { - width: 100%; -} - -table.indextable td { - text-align: left; - vertical-align: top; -} - -table.indextable ul { - margin-top: 0; - margin-bottom: 0; - list-style-type: none; -} - -table.indextable > tbody > tr > td > ul { - padding-left: 0em; -} - -table.indextable tr.pcap { - height: 10px; -} - -table.indextable tr.cap { - margin-top: 10px; - background-color: #f2f2f2; -} - -img.toggler { - margin-right: 3px; - margin-top: 3px; - cursor: pointer; -} - -div.modindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -div.genindex-jumpbox { - border-top: 1px solid #ddd; - border-bottom: 1px solid #ddd; - margin: 1em 0 1em 0; - padding: 0.4em; -} - -/* -- domain module index --------------------------------------------------- */ - -table.modindextable td { - padding: 2px; - border-collapse: collapse; -} - -/* -- general body styles --------------------------------------------------- */ - -div.body { - min-width: 360px; - max-width: 800px; -} - -div.body p, div.body dd, div.body li, div.body blockquote { - -moz-hyphens: auto; - -ms-hyphens: auto; - -webkit-hyphens: auto; - hyphens: auto; -} - -a.headerlink { - visibility: hidden; -} - -a:visited { - color: #551A8B; -} - -h1:hover > a.headerlink, -h2:hover > a.headerlink, -h3:hover > a.headerlink, -h4:hover > a.headerlink, -h5:hover > a.headerlink, -h6:hover > a.headerlink, -dt:hover > a.headerlink, -caption:hover > a.headerlink, -p.caption:hover > a.headerlink, -div.code-block-caption:hover > a.headerlink { - visibility: visible; -} - -div.body p.caption { - text-align: inherit; -} - -div.body td { - text-align: left; -} - -.first { - margin-top: 0 !important; -} - -p.rubric { - margin-top: 30px; - font-weight: bold; -} - -img.align-left, figure.align-left, .figure.align-left, object.align-left { - clear: left; - float: left; - margin-right: 1em; -} - -img.align-right, figure.align-right, .figure.align-right, object.align-right { - clear: right; - float: right; - margin-left: 1em; -} - -img.align-center, figure.align-center, .figure.align-center, object.align-center { - display: block; - margin-left: auto; - margin-right: auto; -} - -img.align-default, figure.align-default, .figure.align-default { - display: block; - margin-left: auto; - margin-right: auto; -} - -.align-left { - text-align: left; -} - -.align-center { - text-align: center; -} - -.align-default { - text-align: center; -} - -.align-right { - text-align: right; -} - -/* -- sidebars -------------------------------------------------------------- */ - -div.sidebar, -aside.sidebar { - margin: 0 0 0.5em 1em; - border: 1px solid #ddb; - padding: 7px; - background-color: #ffe; - width: 40%; - float: right; - clear: right; - overflow-x: auto; -} - -p.sidebar-title { - font-weight: bold; -} - -nav.contents, -aside.topic, -div.admonition, div.topic, blockquote { - clear: left; -} - -/* -- topics ---------------------------------------------------------------- */ - -nav.contents, -aside.topic, -div.topic { - border: 1px solid #ccc; - padding: 7px; - margin: 10px 0 10px 0; -} - -p.topic-title { - font-size: 1.1em; - font-weight: bold; - margin-top: 10px; -} - -/* -- admonitions ----------------------------------------------------------- */ - -div.admonition { - margin-top: 10px; - margin-bottom: 10px; - padding: 7px; -} - -div.admonition dt { - font-weight: bold; -} - -p.admonition-title { - margin: 0px 10px 5px 0px; - font-weight: bold; -} - -div.body p.centered { - text-align: center; - margin-top: 25px; -} - -/* -- content of sidebars/topics/admonitions -------------------------------- */ - -div.sidebar > :last-child, -aside.sidebar > :last-child, -nav.contents > :last-child, -aside.topic > :last-child, -div.topic > :last-child, -div.admonition > :last-child { - margin-bottom: 0; -} - -div.sidebar::after, -aside.sidebar::after, -nav.contents::after, -aside.topic::after, -div.topic::after, -div.admonition::after, -blockquote::after { - display: block; - content: ''; - clear: both; -} - -/* -- tables ---------------------------------------------------------------- */ - -table.docutils { - margin-top: 10px; - margin-bottom: 10px; - border: 0; - border-collapse: collapse; -} - -table.align-center { - margin-left: auto; - margin-right: auto; -} - -table.align-default { - margin-left: auto; - margin-right: auto; -} - -table caption span.caption-number { - font-style: italic; -} - -table caption span.caption-text { -} - -table.docutils td, table.docutils th { - padding: 1px 8px 1px 5px; - border-top: 0; - border-left: 0; - border-right: 0; - border-bottom: 1px solid #aaa; -} - -th { - text-align: left; - padding-right: 5px; -} - -table.citation { - border-left: solid 1px gray; - margin-left: 1px; -} - -table.citation td { - border-bottom: none; -} - -th > :first-child, -td > :first-child { - margin-top: 0px; -} - -th > :last-child, -td > :last-child { - margin-bottom: 0px; -} - -/* -- figures --------------------------------------------------------------- */ - -div.figure, figure { - margin: 0.5em; - padding: 0.5em; -} - -div.figure p.caption, figcaption { - padding: 0.3em; -} - -div.figure p.caption span.caption-number, -figcaption span.caption-number { - font-style: italic; -} - -div.figure p.caption span.caption-text, -figcaption span.caption-text { -} - -/* -- field list styles ----------------------------------------------------- */ - -table.field-list td, table.field-list th { - border: 0 !important; -} - -.field-list ul { - margin: 0; - padding-left: 1em; -} - -.field-list p { - margin: 0; -} - -.field-name { - -moz-hyphens: manual; - -ms-hyphens: manual; - -webkit-hyphens: manual; - hyphens: manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist { - margin: 1em 0; -} - -table.hlist td { - vertical-align: top; -} - -/* -- object description styles --------------------------------------------- */ - -.sig { - font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; -} - -.sig-name, code.descname { - background-color: transparent; - font-weight: bold; -} - -.sig-name { - font-size: 1.1em; -} - -code.descname { - font-size: 1.2em; -} - -.sig-prename, code.descclassname { - background-color: transparent; -} - -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - -.sig-param.n { - font-style: italic; -} - -/* C++ specific styling */ - -.sig-inline.c-texpr, -.sig-inline.cpp-texpr { - font-family: unset; -} - -.sig.c .k, .sig.c .kt, -.sig.cpp .k, .sig.cpp .kt { - color: #0033B3; -} - -.sig.c .m, -.sig.cpp .m { - color: #1750EB; -} - -.sig.c .s, .sig.c .sc, -.sig.cpp .s, .sig.cpp .sc { - color: #067D17; -} - - -/* -- other body styles ----------------------------------------------------- */ - -ol.arabic { - list-style: decimal; -} - -ol.loweralpha { - list-style: lower-alpha; -} - -ol.upperalpha { - list-style: upper-alpha; -} - -ol.lowerroman { - list-style: lower-roman; -} - -ol.upperroman { - list-style: upper-roman; -} - -:not(li) > ol > li:first-child > :first-child, -:not(li) > ul > li:first-child > :first-child { - margin-top: 0px; -} - -:not(li) > ol > li:last-child > :last-child, -:not(li) > ul > li:last-child > :last-child { - margin-bottom: 0px; -} - -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, -ul.simple > li:not(:first-child) > p { - margin-top: 0; -} - -ol.simple p, -ul.simple p { - margin-bottom: 0; -} - -aside.footnote > span, -div.citation > span { - float: left; -} -aside.footnote > span:last-of-type, -div.citation > span:last-of-type { - padding-right: 0.5em; -} -aside.footnote > p { - margin-left: 2em; -} -div.citation > p { - margin-left: 4em; -} -aside.footnote > p:last-of-type, -div.citation > p:last-of-type { - margin-bottom: 0em; -} -aside.footnote > p:last-of-type:after, -div.citation > p:last-of-type:after { - content: ""; - clear: both; -} - -dl.field-list { - display: grid; - grid-template-columns: fit-content(30%) auto; -} - -dl.field-list > dt { - font-weight: bold; - word-break: break-word; - padding-left: 0.5em; - padding-right: 5px; -} - -dl.field-list > dd { - padding-left: 0.5em; - margin-top: 0em; - margin-left: 0em; - margin-bottom: 0em; -} - -dl { - margin-bottom: 15px; -} - -dd > :first-child { - margin-top: 0px; -} - -dd ul, dd table { - margin-bottom: 10px; -} - -dd { - margin-top: 3px; - margin-bottom: 10px; - margin-left: 30px; -} - -.sig dd { - margin-top: 0px; - margin-bottom: 0px; -} - -.sig dl { - margin-top: 0px; - margin-bottom: 0px; -} - -dl > dd:last-child, -dl > dd:last-child > :last-child { - margin-bottom: 0; -} - -dt:target, span.highlighted { - background-color: #fbe54e; -} - -rect.highlighted { - fill: #fbe54e; -} - -dl.glossary dt { - font-weight: bold; - font-size: 1.1em; -} - -.versionmodified { - font-style: italic; -} - -.system-message { - background-color: #fda; - padding: 5px; - border: 3px solid red; -} - -.footnote:target { - background-color: #ffa; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; - margin-bottom: 0; - margin-left: 1.5em; -} - -.guilabel, .menuselection { - font-family: sans-serif; -} - -.accelerator { - text-decoration: underline; -} - -.classifier { - font-style: oblique; -} - -.classifier:before { - font-style: normal; - margin: 0 0.5em; - content: ":"; - display: inline-block; -} - -abbr, acronym { - border-bottom: dotted 1px; - cursor: help; -} - -.translated { - background-color: rgba(207, 255, 207, 0.2) -} - -.untranslated { - background-color: rgba(255, 207, 207, 0.2) -} - -/* -- code displays --------------------------------------------------------- */ - -pre { - overflow: auto; - overflow-y: hidden; /* fixes display issues on Chrome browsers */ -} - -pre, div[class*="highlight-"] { - clear: both; -} - -span.pre { - -moz-hyphens: none; - -ms-hyphens: none; - -webkit-hyphens: none; - hyphens: none; - 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