abd-search-memory

Semantic search over a FAISS index retrieves the top-k most relevant memory chunks by meaning rather than exact wording. It matches query intent against ingested content to surface topic-specific knowledge for informed responses.

Running semantic search on memory chunks requires an OPENAI_API_KEY, a pre-built FAISS index located under the memory/rag directory, and Python dependencies including faiss-cpu, numpy, and openai installed in your environment.

Yes, semantic search applies to dynamic conversations requiring memory recall. It retrieves relevant memory chunks from previously ingested content and injects them into responses with source references for traceability across various topics.

Semantic search with FAISS retrieves content by meaning, not by exact keyword matching. It uses embeddings to find the most relevant memory chunks based on semantic similarity, enabling recall even when wording differs from the original ingested content.

A local FAISS index for memory retrieval requires a pre-built index under memory/rag and specific Python dependencies. It is limited to recalling previously ingested content and cannot retrieve information outside its existing memory chunks.