feat(llm):vector db

[fantasy-lotus]

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Summary of Changes

Hello @fantasy-lotus, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the hugegraph-llm project by integrating robust support for external vector databases, Milvus and Qdrant. The core vector indexing logic has been refactored into a modular design, allowing for easier addition of new vector store backends. This change introduces new configuration settings and updates the user interface to enable seamless selection and management of different vector engines, thereby improving the scalability and adaptability of the LLM application's vector search capabilities.

Highlights

• Vector Database Integration: Introduced support for external vector databases, specifically Milvus and Qdrant, alongside the existing Faiss implementation, providing more flexibility for vector indexing.
• Modular Vector Indexing: Refactored the vector index management by introducing a VectorStoreBase abstract class and separate implementations for Faiss, Milvus, and Qdrant, enhancing extensibility and maintainability.
• Configuration and UI Updates: Added new configuration options for Milvus and Qdrant in config.md and index_config.py, and updated the Gradio UI (configs_block.py) to allow users to select and configure their preferred vector engine.
• Embedding API Enhancements: Modified embedding models (LiteLLM, Ollama, OpenAI) to dynamically determine embedding dimensions and handle batch processing more efficiently, improving performance and robustness.
• Build System and Linting: Updated pyproject.toml to include optional dependencies for vector databases and added configurations for mypy and ruff for improved code quality and consistency.

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[Copilot]

Pull Request Overview

This PR implements vector database support, introducing Milvus and Qdrant as alternatives to the existing Faiss implementation. The changes establish a common interface for vector stores, add configuration management for vector database backends, and update embedding models to support dimension detection and batch processing.

Key changes:

• Adds abstract VectorStoreBase class and implementations for Faiss, Milvus, and Qdrant
• Introduces IndexConfig for managing vector database connection settings
• Enhances embedding models with get_embedding_dim() method and improved batch processing
• Updates operators and nodes to use the new vector store abstraction

Reviewed Changes

Copilot reviewed 50 out of 51 changed files in this pull request and generated 8 comments.

Show a summary per file

File	Description
hugegraph-llm/src/hugegraph_llm/indices/vector_index/base.py	Defines abstract base class for vector stores
hugegraph-llm/src/hugegraph_llm/indices/vector_index/faiss_vector_store.py	Refactored Faiss implementation conforming to base interface
hugegraph-llm/src/hugegraph_llm/indices/vector_index/milvus_vector_store.py	New Milvus vector store implementation
hugegraph-llm/src/hugegraph_llm/indices/vector_index/qdrant_vector_store.py	New Qdrant vector store implementation
hugegraph-llm/src/hugegraph_llm/config/index_config.py	Configuration class for vector database settings
hugegraph-llm/src/hugegraph_llm/models/embeddings/*.py	Enhanced embedding classes with dimension detection and batch processing
hugegraph-llm/src/hugegraph_llm/utils/vector_index_utils.py	Utility functions for vector index selection and initialization
hugegraph-llm/src/hugegraph_llm/operators/index_op/*.py	Updated operators to use new vector store abstraction
hugegraph-llm/pyproject.toml	Added optional vectordb dependencies

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[gemini-code-assist]

Code Review

This pull request introduces a significant and valuable refactoring to support multiple vector database backends (Faiss, Milvus, Qdrant), moving from a concrete Faiss implementation to a flexible VectorStoreBase abstraction. The changes are extensive, touching configuration, the Gradio UI, core operators, and embedding models. While the overall direction is excellent, I've identified a critical issue with ID generation in the Qdrant implementation that could lead to data loss, along with several high and medium-severity issues related to configuration safety, code clarity, and maintainability. Addressing these points will solidify this new abstraction and ensure its robustness.