feat(examples): add custom HTTP embedding example for LM Studio / Ollama

[cluster2600]

Summary

This PR adds a self-contained example showing how to use any OpenAI-compatible HTTP embedding endpoint (LM Studio, Ollama, vLLM, LocalAI, …) as the embedding source in zvec.

What's added

examples/custom_http_embedding.py

HTTPEmbeddingFunction	A zero-dependency class (stdlib only) that calls any /v1/embeddings endpoint, caches results with @lru_cache, and satisfies the DenseEmbeddingFunction protocol.
Collection setup	HNSW index with cosine similarity, dimension auto-detected from the server response.
Insert + query	5 sample documents embedded on the fly, then a semantic search query.
CLI interface	--base-url, --model, --api-key, --collection-path flags for easy customisation.
README-style header	Step-by-step instructions for LM Studio and Ollama embedded at the top of the file.

Usage

# LM Studio (default)
python examples/custom_http_embedding.py

# Ollama
python examples/custom_http_embedding.py \
    --base-url http://localhost:11434 \
    --model nomic-embed-text

Motivation

The existing extensions (OpenAIDenseEmbedding, etc.) require the openai package and are primarily designed for cloud APIs. Many developers want to use local inference servers without extra dependencies. This example shows the pattern using only Python stdlib, making it easy to adapt or inline.

Testing

The example runs end-to-end against a live LM Studio instance on localhost:1234. No new test infrastructure is required for a standalone script.

[CLAassistant]

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[Cuiyus]

Thank you for your submission!

This service-oriented model for invocation, which helps zvec achieve the RAG capability, is what we currently lack.
You can implement your HttpEmbedingFunction(OllmaEmbedingFunction) by inheriting DenseEmbedingFunction in the directory python/zvec/extension. This will make it easier for more users to use!

https://zvec.org/api-reference/python/extension/#zvec.extension.DenseEmbeddingFunction

[cluster2600]

Thanks for the feedback! Moved the implementation into python/zvec/extension/http_embedding_function.py as HTTPDenseEmbedding, inheriting from DenseEmbeddingFunction. It's now exported from zvec.extension and the example imports it from there instead of defining the class inline.