53 lines
2 KiB
MySQL
53 lines
2 KiB
MySQL
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-- ============================================================
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-- RAG Knowledge Base Schema for agent_memory database
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-- Embedding dimensions: 768 (nomic-embed-text-v1.5)
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-- ============================================================
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-- Enable pgvector extension
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CREATE EXTENSION IF NOT EXISTS vector;
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-- Documents table for RAG knowledge base
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CREATE TABLE IF NOT EXISTS documents (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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content TEXT NOT NULL,
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metadata JSONB DEFAULT '{}'::jsonb,
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embedding vector(768),
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created_at TIMESTAMPTZ NOT NULL DEFAULT now(),
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updated_at TIMESTAMPTZ NOT NULL DEFAULT now()
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);
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-- Index on content for full-text search
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CREATE INDEX IF NOT EXISTS idx_documents_content ON documents USING gin (to_tsvector('english', content));
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-- HNSW index for vector similarity search (cosine distance)
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CREATE INDEX IF NOT EXISTS idx_documents_embedding_hnsw ON documents USING hnsw (embedding vector_cosine_ops);
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-- Index on metadata for filtering
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CREATE INDEX IF NOT EXISTS idx_documents_metadata ON documents USING gin (metadata);
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-- Updated_at trigger
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CREATE OR REPLACE FUNCTION update_documents_updated_at()
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RETURNS TRIGGER AS $$
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BEGIN
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NEW.updated_at = now();
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RETURN NEW;
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END;
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$$ LANGUAGE plpgsql;
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CREATE TRIGGER trg_documents_updated_at
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BEFORE UPDATE ON documents
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FOR EACH ROW
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EXECUTE FUNCTION update_documents_updated_at();
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-- Comments for documentation
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COMMENT ON TABLE documents IS 'RAG knowledge base documents with vector embeddings';
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COMMENT ON COLUMN documents.content IS 'Full text content of the document';
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COMMENT ON COLUMN documents.metadata IS 'JSON metadata: source, chunk_id, title, tags, etc.';
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COMMENT ON COLUMN documents.embedding IS '768-dim vector embedding (nomic-embed-text-v1.5)';
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-- Example query for similarity search:
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-- SELECT id, content, metadata, 1 - (embedding <=> 'your_embedding_here'::vector) AS similarity
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-- FROM documents
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-- ORDER BY embedding <=> 'your_embedding_here'::vector
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-- LIMIT 5;
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