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You can retrieve details from past sessions using semantic vector search by asking to recall information, which scans indexed memory chunks to surface relevant decisions, dates, and outcomes with their original source and date.

Semantic vector search handles session recall failures by falling back to a SQLite keyword search, ensuring robust retrieval of memory chunks and context extraction even when the primary vector index misses.

Yes, semantic search for session memory requires LanceDB for local vector indexing and sentence-transformers to generate the embeddings needed to match and retrieve relevant archived content.

Yes, you can extract project context, dates, and types from memory notes during recall, as the semantic search enriches results by presenting content, source, date, and context in a structured format.

The best way to search across archived sessions for a specific decision is using semantic vector search, which matches the meaning of your query against indexed memory chunks and falls back to keyword search if needed.

Session recall might return incomplete context if the vector search index lacks matching memory chunks, triggering a fallback to SQLite keyword search which may surface less semantic context than vector-based similarity matches.