Clawdie AI
cc37d2c8cf
--- Build: pass | Tests: pass - 603 passed (44 files) --- Build: pass | Tests: pass — Tests 603 passed (603)
68 lines
2.1 KiB
PL/PgSQL
68 lines
2.1 KiB
PL/PgSQL
-- Hybrid search function for ai_brain
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-- Date: 10.mar.2026 (updated: 27.mar.2026)
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--
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-- Uses Reciprocal Rank Fusion (RRF) to merge full-text and vector results.
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-- Embedding model: bge-m3 at 1024 dims via local llama-server.
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-- Degrades to FTS-only if no embeddings are stored.
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--
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-- Usage:
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-- SELECT * FROM search_memories('query text', '<embedding>'::vector, 10);
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CREATE OR REPLACE FUNCTION search_memories(
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query_text TEXT,
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query_embedding vector(1024),
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match_limit INTEGER DEFAULT 10
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)
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RETURNS TABLE (
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memory_id UUID,
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chunk_text TEXT,
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summary TEXT,
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importance INTEGER,
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topics TEXT[],
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created_at TIMESTAMPTZ,
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combined_score FLOAT
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) AS $$
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BEGIN
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RETURN QUERY
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WITH text_results AS (
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SELECT
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mc.id AS chunk_id,
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mc.memory_id,
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mc.chunk_text,
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row_number() OVER (
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ORDER BY ts_rank(mc.search_vector,
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plainto_tsquery('english', query_text)) DESC
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) AS text_rank
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FROM memory_chunks mc
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WHERE mc.search_vector @@ plainto_tsquery('english', query_text)
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LIMIT 20
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),
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vector_results AS (
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SELECT
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mc.id AS chunk_id,
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mc.memory_id,
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mc.chunk_text,
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row_number() OVER (
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ORDER BY me.embedding <=> query_embedding
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) AS vector_rank
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FROM memory_embeddings me
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JOIN memory_chunks mc ON mc.id = me.chunk_id
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ORDER BY me.embedding <=> query_embedding
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LIMIT 20
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)
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SELECT
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COALESCE(t.memory_id, v.memory_id) AS memory_id,
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COALESCE(t.chunk_text, v.chunk_text) AS chunk_text,
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m.summary,
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m.importance,
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m.topics,
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m.created_at,
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(COALESCE(1.0 / (60 + t.text_rank), 0) +
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COALESCE(1.0 / (60 + v.vector_rank), 0))::FLOAT AS combined_score
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FROM text_results t
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FULL OUTER JOIN vector_results v ON t.chunk_id = v.chunk_id
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JOIN memories m ON m.id = COALESCE(t.memory_id, v.memory_id)
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ORDER BY combined_score DESC
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LIMIT match_limit;
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END;
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$$ LANGUAGE plpgsql;
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