Description
Investigate and implement hybrid search functionality in Qdrant, combining vector similarity search with traditional filtering and keyword-based search capabilities. The goal is to encapsulate this functionality within a custom IMemoryDb type for seamless integration into the kernel-memory library
Technical Notes
- The hybrid search logic must be fully encapsulated within a custom
IMemoryDb implementation, adhering to the architecture and design principles of the kernel-memory library
- Leverage the official Qdrant.Client library for interfacing with Qdrant. Ensure compatibility and efficient usage of its features
- Implement functionality to construct both
dense and sparse vectors as required by the hybrid search process. Evaluate the need for and potentially develop a custom embedding generator for creating optimal vector representations
Description
Investigate and implement hybrid search functionality in Qdrant, combining vector similarity search with traditional filtering and keyword-based search capabilities. The goal is to encapsulate this functionality within a custom
IMemoryDbtype for seamless integration into thekernel-memorylibraryTechnical Notes
IMemoryDbimplementation, adhering to the architecture and design principles of the kernel-memory librarydenseandsparsevectors as required by the hybrid search process. Evaluate the need for and potentially develop a custom embedding generator for creating optimal vector representations