[WIP] Implement document categorization and labeling approach

[Copilot]

Coding agent has begun work on Document Categorization and Labeling Approaches and will replace this description as work progresses.

See problem context

The user has attached the following files from their workspace:

• nbs/303-smol-course-ch1-sft.py

The following is a snapshot of a chat conversation between a user and an AI coding assistant. Prioritize later messages in the conversation.
User: I have millions of enterprise documents. I want to categorize and label them with document categories such as invoices, contracts etc.

What are the top ML/LLM/human based approaches to tagging, annotating and labeling these documents? What kind of custom tools can I vibe code to rapidly annotate a large set of documents with AI and human together?

AI: Approach overview

• Rule-based baselines: keyword/entity heuristics, template matchers to bootstrap labels.
• Traditional ML: TF-IDF or embeddings + classifiers (logistic regression, SVM, gradient boosting) trained on a modest labeled seed.
• Document vision models: LayoutLMv3, Donut, DocFormer for invoices/contracts with structured layouts.
• General LLMs: zero-/few-shot prompting on OCR text; improve with retrieval-augmented prompts and cost controls.
• Fine-tuned LLMs: instruction-tune smaller models on curated labels; add human-in-the-loop review to raise accuracy.
• Semi-supervised/active learning: propagate labels via clustering, embeddings; query humans on uncertain samples.
• Human QA: expert validation portals with consensus or conflict resolution workflows.

Tooling ideas

• Embed pipeline: OCR → text+layout embeddings → vector DB for search, dedupe, cluster.
• Active-learning dashboard: surface low-confidence predictions for reviewer confirmation.
• Prompt orchestration service: templated prompts, reference snippets, auto-logging outputs.
• Guideline-aware labeling UI: schema enforcement, shortcut macros, rich previews.
• Feedback loops: capture human edits, auto-sync to training dataset, periodic retraining jobs.
• Quality monitoring: drift dashboards, sampling audits, cost/latency telemetry.
  User: implement this.
  AI: Coding agent session cancelled.
  User: implement this.

TITLE: Document Categorization and Labeling Approaches

USER INTENT: The user aims to implement a system for categorizing and labeling millions of enterprise documents using a combination of machine learning, large language models (LLMs), and human input.

TASK DESCRIPTION: The user seeks to explore and implement effective methods for tagging, annotating, and labeling documents such as invoices and contracts. They are interested in both automated and human-assisted approaches, as well as custom tools to facilitate rapid annotation.

EXISTING: The assistant provided an overview of various approaches and tooling ideas for document categorization, including rule-based methods, traditional machine learning techniques, document vision models, and LLMs. The assistant also suggested several custom tool ideas to support the annotation process.

PENDING: The user has requested the implementation of the discussed approaches and tools but has not yet received a response or action from the assistant regarding this request.

CODE STATE: No specific code files or snippets have been discussed or modified in this conversation, as the focus has been on conceptual approaches and tool ideas.

RELEVANT CODE/DOCUMENTATION SNIPPETS:

• Approach Overview:

  • Rule-based baselines: keyword/entity heuristics, template matchers.
  • Traditional ML: TF-IDF or embeddings + classifiers (logistic regression, SVM).
  • Document vision models: LayoutLMv3, Donut, DocFormer.
  • General LLMs: zero-/few-shot prompting on OCR text.
  • Fine-tuned LLMs: instruction-tune smaller models on curated labels.
  • Semi-supervised/active learning: propagate labels via clustering.
  • Human QA: expert validation portals.

• Tooling Ideas:

  • Embed pipeline: OCR → text+layout embeddings → vector DB.
  • Active-learning dashboard: surface low-confidence predictions.
  • Prompt orchestration service: templated prompts and auto-logging.
  • Guideline-aware labeling UI: schema enforcement and rich previews.
  • Feedback loops: capture human edits and sync to training dataset.
  • Quality monitoring: drift dashboards and sampling audits.

OTHER NOTES: The user has expressed a strong desire to move forward with the implementation of the proposed solutions but has encountered a lack of follow-through from the assistant.

Original prompt

implement this.

The user has attached the following files from their workspace:

• nbs/303-smol-course-ch1-sft.py

The following is a snapshot of a chat conversation between a user and an AI coding assistant. Prioritize later messages in the conversation.
User: I have millions of enterprise documents. I want to categorize and label them with document categories such as invoices, contracts etc.

What are the top ML/LLM/human based approaches to tagging, annotating and labeling these documents? What kind of custom tools can I vibe code to rapidly annotate a large set of documents with AI and human together?

AI: Approach overview

• Rule-based baselines: keyword/entity heuristics, template matchers to bootstrap labels.
• Traditional ML: TF-IDF or embeddings + classifiers (logistic regression, SVM, gradient boosting) trained on a modest labeled seed.
• Document vision models: LayoutLMv3, Donut, DocFormer for invoices/contracts with structured layouts.
• General LLMs: zero-/few-shot prompting on OCR text; improve with retrieval-augmented prompts and cost controls.
• Fine-tuned LLMs: instruction-tune smaller models on curated labels; add human-in-the-loop review to raise accuracy.
• Semi-supervised/active learning: propagate labels via clustering, embeddings; query humans on uncertain samples.
• Human QA: expert validation portals with consensus or conflict resolution workflows.

Tooling ideas

• Embed pipeline: OCR → text+layout embeddings → vector DB for search, dedupe, cluster.
• Active-learning dashboard: surface low-confidence predictions for reviewer confirmation.
• Prompt orchestration service: templated prompts, reference snippets, auto-logging outputs.
• Guideline-aware labeling UI: schema enforcement, shortcut macros, rich previews.
• Feedback loops: capture human edits, auto-sync to training dataset, periodic retraining jobs.
• Quality monitoring: drift dashboards, sampling audits, cost/latency telemetry.
  User: implement this.
  AI: Coding agent session cancelled.
  User: implement this.

TITLE: Document Categorization and Labeling Approaches

USER INTENT: The user aims to implement a system for categorizing and labeling millions of enterprise documents using a combination of machine learning, large language models (LLMs), and human input.

TASK DESCRIPTION: The user seeks to explore and implement effective methods for tagging, annotating, and labeling documents such as invoices and contracts. They are interested in both automated and human-assisted approaches, as well as custom tools to facilitate rapid annotation.

EXISTING: The assistant provided an overview of various approaches and tooling ideas for document categorization, including rule-based methods, traditional machine learning techniques, document vision models, and LLMs. The assistant also suggested several custom tool ideas to support the annotation process.

PENDING: The user has requested the implementation of the discussed approaches and tools but has not yet received a response or action from the assistant regarding this request.

CODE STATE: No specific code files or snippets have been discussed or modified in this conversation, as the focus has been on conceptual approaches and tool ideas.

RELEVANT CODE/DOCUMENTATION SNIPPETS:

• Approach Overview:

  • Rule-based baselines: keyword/entity heuristics, template matchers.
  • Traditional ML: TF-IDF or embeddings + classifiers (logistic regression, SVM).
  • Document vision models: LayoutLMv3, Donut, DocFormer.
  • General LLMs: zero-/few-shot prompting on OCR text.
  • Fine-tuned LLMs: instruction-tune smaller models on curated labels.
  • Semi-supervised/active learning: propagate labels via clustering.
  • Human QA: expert validation portals.

• Tooling Ideas:

  • Embed pipeline: OCR → text+layout embeddings → vector DB.
  • Active-learning dashboard: surface low-confidence predictions.
  • Prompt orchestration service: templated prompts and auto-logging.
  • Guideline-aware labeling UI: schema enforcement and rich previews.
  • Feedback loops: capture human edits and sync to training dataset.
  • Quality monitoring: drift dashboards and sampling audits.

OTHER NOTES: The user has expressed a strong desire to move forward with the implementation of the proposed solutions but has encountered a lack of follow-through from the assistant.

Created from VS Code via the GitHub Pull Request extension.

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