Powers semantic search and RAG across your entire knowledge base. Find anything by meaning, not just keywords.
The intelligence layer that makes River and Moltbot truly understand your data.
Searcher uses two-layer retrieval: first searching atomic memories extracted by Reader, then injecting full source content for complete context. This gives you precision and richness in one query.
Generates vector embeddings for atomic memories from Reader. Small, focused memories = more precise search results.
Layer 1: Search atomic memories for precision. Layer 2: Inject source entry chunks for full context.
Checks new memories against existing ones before storage. Prevents duplicates, links related knowledge.
Combines semantic similarity with keyword matching (PostgreSQL FTS) for the best of both worlds.
When entries arrive, Reader extracts atomic memories - discrete facts, insights, and knowledge units.
Searcher generates vector embeddings for each atomic memory (not the full entry). Smaller text = more precise embeddings.
When you query, Searcher first finds the most relevant atomic memories. Fast and precise.
Searcher then retrieves the full source entries for context. River/Moltbot gets both the precise match and surrounding information.
Searcher works hand-in-hand with Reader to provide intelligent knowledge retrieval.
Breaks entries into atomic memories, detects relationships, suggests tags
Embeds memories, checks for duplicates, powers semantic search and RAG
Before storing a new memory, Searcher checks for similar existing memories. This helps:
Searcher powers the intelligent retrieval that makes River and Moltbot truly understand your knowledge base.