AI Agent Factory with Claude Code Subagents

use-cases/agent-factory-with-subagents/examples/rag_pipeline/sql/schema.sql

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+CREATE EXTENSION IF NOT EXISTS vector;
+CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
+CREATE EXTENSION IF NOT EXISTS pg_trgm;
+
+DROP TABLE IF EXISTS chunks CASCADE;
+DROP TABLE IF EXISTS documents CASCADE;
+DROP INDEX IF EXISTS idx_chunks_embedding;
+DROP INDEX IF EXISTS idx_chunks_document_id;
+DROP INDEX IF EXISTS idx_documents_metadata;
+DROP INDEX IF EXISTS idx_chunks_content_trgm;
+
+CREATE TABLE documents (
+    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
+    title TEXT NOT NULL,
+    source TEXT NOT NULL,
+    content TEXT NOT NULL,
+    metadata JSONB DEFAULT '{}',
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
+);
+
+CREATE INDEX idx_documents_metadata ON documents USING GIN (metadata);
+CREATE INDEX idx_documents_created_at ON documents (created_at DESC);
+
+CREATE TABLE chunks (
+    id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
+    document_id UUID NOT NULL REFERENCES documents(id) ON DELETE CASCADE,
+    content TEXT NOT NULL,
+    embedding vector(1536),
+    chunk_index INTEGER NOT NULL,
+    metadata JSONB DEFAULT '{}',
+    token_count INTEGER,
+    created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
+);
+
+CREATE INDEX idx_chunks_embedding ON chunks USING ivfflat (embedding vector_cosine_ops) WITH (lists = 1);
+CREATE INDEX idx_chunks_document_id ON chunks (document_id);
+CREATE INDEX idx_chunks_chunk_index ON chunks (document_id, chunk_index);
+CREATE INDEX idx_chunks_content_trgm ON chunks USING GIN (content gin_trgm_ops);
+
+CREATE OR REPLACE FUNCTION match_chunks(
+    query_embedding vector(1536),
+    match_count INT DEFAULT 10
+)
+RETURNS TABLE (
+    chunk_id UUID,
+    document_id UUID,
+    content TEXT,
+    similarity FLOAT,
+    metadata JSONB,
+    document_title TEXT,
+    document_source TEXT
+)
+LANGUAGE plpgsql
+AS $$
+BEGIN
+    RETURN QUERY
+    SELECT 
+        c.id AS chunk_id,
+        c.document_id,
+        c.content,
+        1 - (c.embedding <=> query_embedding) AS similarity,
+        c.metadata,
+        d.title AS document_title,
+        d.source AS document_source
+    FROM chunks c
+    JOIN documents d ON c.document_id = d.id
+    WHERE c.embedding IS NOT NULL
+    ORDER BY c.embedding <=> query_embedding
+    LIMIT match_count;
+END;
+$$;
+
+CREATE OR REPLACE FUNCTION hybrid_search(
+    query_embedding vector(1536),
+    query_text TEXT,
+    match_count INT DEFAULT 10,
+    text_weight FLOAT DEFAULT 0.3
+)
+RETURNS TABLE (
+    chunk_id UUID,
+    document_id UUID,
+    content TEXT,
+    combined_score FLOAT,
+    vector_similarity FLOAT,
+    text_similarity FLOAT,
+    metadata JSONB,
+    document_title TEXT,
+    document_source TEXT
+)
+LANGUAGE plpgsql
+AS $$
+BEGIN
+    RETURN QUERY
+    WITH vector_results AS (
+        SELECT 
+            c.id AS chunk_id,
+            c.document_id,
+            c.content,
+            1 - (c.embedding <=> query_embedding) AS vector_sim,
+            c.metadata,
+            d.title AS doc_title,
+            d.source AS doc_source
+        FROM chunks c
+        JOIN documents d ON c.document_id = d.id
+        WHERE c.embedding IS NOT NULL
+    ),
+    text_results AS (
+        SELECT 
+            c.id AS chunk_id,
+            c.document_id,
+            c.content,
+            ts_rank_cd(to_tsvector('english', c.content), plainto_tsquery('english', query_text)) AS text_sim,
+            c.metadata,
+            d.title AS doc_title,
+            d.source AS doc_source
+        FROM chunks c
+        JOIN documents d ON c.document_id = d.id
+        WHERE to_tsvector('english', c.content) @@ plainto_tsquery('english', query_text)
+    )
+    SELECT 
+        COALESCE(v.chunk_id, t.chunk_id) AS chunk_id,
+        COALESCE(v.document_id, t.document_id) AS document_id,
+        COALESCE(v.content, t.content) AS content,
+        (COALESCE(v.vector_sim, 0) * (1 - text_weight) + COALESCE(t.text_sim, 0) * text_weight) AS combined_score,
+        COALESCE(v.vector_sim, 0) AS vector_similarity,
+        COALESCE(t.text_sim, 0) AS text_similarity,
+        COALESCE(v.metadata, t.metadata) AS metadata,
+        COALESCE(v.doc_title, t.doc_title) AS document_title,
+        COALESCE(v.doc_source, t.doc_source) AS document_source
+    FROM vector_results v
+    FULL OUTER JOIN text_results t ON v.chunk_id = t.chunk_id
+    ORDER BY combined_score DESC
+    LIMIT match_count;
+END;
+$$;
+
+CREATE OR REPLACE FUNCTION get_document_chunks(doc_id UUID)
+RETURNS TABLE (
+    chunk_id UUID,
+    content TEXT,
+    chunk_index INTEGER,
+    metadata JSONB
+)
+LANGUAGE plpgsql
+AS $$
+BEGIN
+    RETURN QUERY
+    SELECT 
+        id AS chunk_id,
+        chunks.content,
+        chunks.chunk_index,
+        chunks.metadata
+    FROM chunks
+    WHERE document_id = doc_id
+    ORDER BY chunk_index;
+END;
+$$;
+
+CREATE OR REPLACE FUNCTION update_updated_at_column()
+RETURNS TRIGGER AS $$
+BEGIN
+    NEW.updated_at = CURRENT_TIMESTAMP;
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+CREATE TRIGGER update_documents_updated_at BEFORE UPDATE ON documents
+    FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();
+