Add local vector store implementation as Azure Search Documents alter…

[jerryao]

…native

Description

Added a complete local vector store implementation as an alternative to Azure Search Documents. This implementation allows users to store and retrieve vector data on the local file system without relying on cloud services.

Related Issues

This implementation addresses the need for a local alternative in the following scenarios:

• Development and testing environments
• Scenarios with high data sensitivity
• Network-restricted or offline environments
• Projects with limited budgets

Proposed Changes

• Added LocalVectorStore class implementing the BaseVectorStore interface
• Updated factory.py to support the local vector store type
• Added chunked storage and compression features to optimize large dataset handling
• Implemented separation of metadata and vector data to improve query performance
• Added complete import/export functionality
• Added attribute-based filtering capabilities
• Added basic and advanced test scripts
• Added configuration example files

Checklist

• I have tested these changes locally
• I have reviewed the code changes
• I have updated the documentation (added configuration examples and comments)
• I have added appropriate unit tests (basic and advanced test scripts)

Additional Notes

This implementation includes several optimizations to make it suitable for large datasets:

1. Reduced memory usage through chunked storage
2. Reduced disk usage with data compression support
3. Improved performance with lazy loading of vector data
4. Faster search speed by separating metadata and vector data

Benchmark testing shows that the local implementation performs well with medium-sized datasets (200-1000 records), meeting the needs of most development and testing scenarios.

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