🚀 Describe the new functionality needed

Currently, Llama-Stack supports optimized chunked writes (PR #1094) for efficient SQLite-based storage. However, there is no built-in Hybrid Search API that combines FTS5 and sqlite-vss to enable semantic and lexical retrieval.

This issue proposes the addition of a Hybrid Search API that allows users to:

1. Store text documents with both full-text and vector embeddings.
2. Perform hybrid search that ranks results by combining BM25-based text relevance and vector similarity.
3. Utilize chunked writes (from PR feat: Chunk sqlite-vec writes #1094) to optimize insertions for large datasets.

Ref: https://github.com/liamca/sqlite-hybrid-search/tree/main - The idea would be take Reciprocal Rank Fusion between FTS5 and vector-based search results to ensure that highly ranked documents across multiple lists are prioritized.

💡 Why is this needed? What if we don't build it?

Building Hybrid Search with RRF will ensure better accuracy, more relevant results inside Llama-Stack's current sqlite vector DB implementation.

Other thoughts

No response