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rUvandClaude Opus 4.5
6adc8e9532
Automatic checkpoint created by Claude Code - Branch: main - Timestamp: 2026-01-17T20:26:34Z - Changes: 1 file(s) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
107 lines
3.4 KiB
PL/PgSQL
107 lines
3.4 KiB
PL/PgSQL
-- RuVector PostgreSQL Initialization Script
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-- This script runs automatically when the Docker container starts
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-- Enable pgvector extension
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CREATE EXTENSION IF NOT EXISTS vector;
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-- Create the claude_flow schema
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CREATE SCHEMA IF NOT EXISTS claude_flow;
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-- Grant permissions
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GRANT ALL ON SCHEMA claude_flow TO claude;
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-- Create embeddings table
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CREATE TABLE IF NOT EXISTS claude_flow.embeddings (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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content TEXT NOT NULL,
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embedding vector(384), -- Default dimension for MiniLM
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW(),
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updated_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Create patterns table for learned patterns
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CREATE TABLE IF NOT EXISTS claude_flow.patterns (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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name VARCHAR(255) NOT NULL,
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description TEXT,
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embedding vector(384),
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pattern_type VARCHAR(50),
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confidence FLOAT DEFAULT 0.0,
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Create agents table for agent memory
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CREATE TABLE IF NOT EXISTS claude_flow.agents (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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agent_id VARCHAR(255) NOT NULL UNIQUE,
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agent_type VARCHAR(50),
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state JSONB DEFAULT '{}',
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memory_embedding vector(384),
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last_active TIMESTAMPTZ DEFAULT NOW(),
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Create HNSW indices for fast similarity search
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CREATE INDEX IF NOT EXISTS idx_embeddings_hnsw
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ON claude_flow.embeddings
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USING hnsw (embedding vector_cosine_ops)
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WITH (m = 16, ef_construction = 64);
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CREATE INDEX IF NOT EXISTS idx_patterns_hnsw
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ON claude_flow.patterns
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USING hnsw (embedding vector_cosine_ops)
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WITH (m = 16, ef_construction = 64);
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CREATE INDEX IF NOT EXISTS idx_agents_hnsw
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ON claude_flow.agents
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USING hnsw (memory_embedding vector_cosine_ops)
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WITH (m = 16, ef_construction = 64);
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-- Create helper functions
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-- Cosine similarity search function
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CREATE OR REPLACE FUNCTION claude_flow.search_similar(
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query_embedding vector(384),
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limit_count INT DEFAULT 10,
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min_similarity FLOAT DEFAULT 0.5
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)
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RETURNS TABLE (
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id UUID,
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content TEXT,
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similarity FLOAT,
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metadata JSONB
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) AS $$
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BEGIN
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RETURN QUERY
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SELECT
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e.id,
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e.content,
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1 - (e.embedding <=> query_embedding) AS similarity,
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e.metadata
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FROM claude_flow.embeddings e
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WHERE 1 - (e.embedding <=> query_embedding) >= min_similarity
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ORDER BY e.embedding <=> query_embedding
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LIMIT limit_count;
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END;
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$$ LANGUAGE plpgsql;
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-- Insert sample data for testing
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INSERT INTO claude_flow.embeddings (content, metadata) VALUES
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('How to implement authentication in Node.js', '{"category": "tutorial", "language": "javascript"}'),
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('Best practices for REST API design', '{"category": "tutorial", "language": "general"}'),
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('Understanding PostgreSQL indexing strategies', '{"category": "database", "language": "sql"}'),
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('Introduction to vector databases and embeddings', '{"category": "ai", "language": "python"}'),
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('Building scalable microservices architecture', '{"category": "architecture", "language": "general"}')
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ON CONFLICT DO NOTHING;
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-- Log initialization
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DO $$
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BEGIN
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RAISE NOTICE 'RuVector PostgreSQL initialization complete!';
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RAISE NOTICE 'Schema: claude_flow';
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RAISE NOTICE 'Tables: embeddings, patterns, agents';
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RAISE NOTICE 'Indices: 3 HNSW indices created';
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END $$;
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