rag-integration

To set up LanceDB for RAG retrieval, you prepare a configuration file to initialize the vector database and index your codebase. This creates structured storage to rapidly retrieve past code chunks and design decisions.

Hybrid search in LanceDB combines BM25 text search with vector embedding retrieval and metadata filtering. This mechanism improves code retrieval by matching both semantic meaning and exact keyword overlaps across indexed data.

Yes, you can use different embedding providers for RAG code indexing. The system supports a 4-Provider embedding tier configuration, allowing you to flexibly mix free and paid embedding strategies across your development workflow.

You store and retrieve failures and histories by centralizing them into structured LanceDB tables designed for conversations and errors. This enables rapid retrieval of past error resolutions and context during software development.

Yes, this RAG integration supports metadata filtering across its four structured data schemas. You can filter retrieved design decisions, code chunks, and failure histories by specific metadata fields during your searches.

The best way to manage schemas and migrations for this RAG vector database is utilizing its built-in migration capabilities across four structured tables. This ensures consistent indexing and retrieval of design decisions, code, and failures.