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Complete VectorStore Implementation

Objective: Finalize the Milvus vector store implementation with all CRUD operations.

Description: Implement search, insert, update, and delete operations for the Milvus vector store. Add connection pooling and robust error handling.

Dependencies: None

Details:

  • Implement all CRUD operations for vector data.
  • Integrate connection pooling for efficient resource management.
  • Add error handling for all database operations.

Status: Done

Test Strategy:

pytest tests/unit/test_vector_store.py

Verify all tests pass and vector operations work as expected.

VectorStore Architecture​

flowchart TD
  subgraph VectorStore
    CRUD[CRUD Operations]
    CP[Connection Pooling]
    EH[Error Handling]
    MV[Milvus]
  end
  CRUD --> MV
  CP --> MV
  EH --> MV

Explanatory Notes​

  • Purpose: The VectorStore enables efficient similarity search and retrieval for embeddings, powering core RAG functionality.
  • Connection Pooling: Reduces latency and resource contention by reusing database connections.
  • Error Handling: Ensures robustness and graceful recovery from transient failures.
  • Best Practices:
    • Index vectors for fast search.
    • Monitor Milvus resource usage and query performance.
    • Use batch operations for large-scale data ingestion.
  • Troubleshooting:
    • Check Milvus logs for errors.
    • Validate schema and index configuration.