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rUvandClaude Opus 4.5
cccdee45b4
Automatic checkpoint created by Claude Code - Branch: main - Timestamp: 2026-01-17T20:33:18Z - Changes: 1 file(s) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
589 lines
19 KiB
PL/PgSQL
589 lines
19 KiB
PL/PgSQL
-- ============================================
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-- RUVECTOR POSTGRESQL INITIALIZATION SCRIPT
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-- ============================================
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--
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-- This script initializes the RuVector PostgreSQL extension
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-- from ruvnet/ruvector with full V3 Claude-Flow integration.
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--
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-- RuVector provides 77+ SQL functions including:
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-- - Vector similarity search (HNSW/IVFFlat)
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-- - 39 Attention mechanisms
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-- - GNN layers for graph operations
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-- - Hyperbolic embeddings (Poincaré/Lorentz)
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-- - Sparse vectors (BM25/TF-IDF)
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-- - SPARQL support (50+ RDF functions)
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-- - Local embeddings (6 fastembed models)
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-- - Self-learning index optimization
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--
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-- Performance: ~61µs latency, 16,400 QPS
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-- ============================================
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-- PART 1: EXTENSION AND SCHEMA SETUP
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-- ============================================
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-- Enable RuVector extension (includes vector type)
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CREATE EXTENSION IF NOT EXISTS ruvector;
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-- Enable additional required extensions
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CREATE EXTENSION IF NOT EXISTS pgcrypto;
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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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GRANT USAGE ON SCHEMA ruvector TO claude;
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-- Set search path for convenience
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SET search_path TO claude_flow, ruvector, public;
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-- ============================================
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-- PART 2: CORE TABLES
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-- ============================================
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-- Embeddings table with RuVector vector type
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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 ruvector.vector(384), -- RuVector vector type
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sparse_embedding ruvector.sparse_vector, -- For hybrid BM25/TF-IDF search
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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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-- Patterns table for learned patterns (ReasoningBank)
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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 ruvector.vector(384),
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pattern_type VARCHAR(50),
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confidence FLOAT DEFAULT 0.5,
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success_count INT DEFAULT 0,
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failure_count INT DEFAULT 0,
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ewc_importance FLOAT DEFAULT 1.0, -- EWC++ weight
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Agents table for multi-agent memory coordination
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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 ruvector.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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-- Trajectories table for SONA reinforcement learning
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CREATE TABLE IF NOT EXISTS claude_flow.trajectories (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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trajectory_id VARCHAR(255) NOT NULL UNIQUE,
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agent_type VARCHAR(50),
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task_description TEXT,
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status VARCHAR(20) DEFAULT 'in_progress',
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steps JSONB DEFAULT '[]',
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outcome VARCHAR(20),
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quality_score FLOAT,
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started_at TIMESTAMPTZ DEFAULT NOW(),
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ended_at TIMESTAMPTZ
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);
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-- Hyperbolic embeddings for hierarchical data
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CREATE TABLE IF NOT EXISTS claude_flow.hyperbolic_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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euclidean_embedding ruvector.vector(384),
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poincare_embedding ruvector.vector(384), -- Poincaré ball (||x|| < 1)
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lorentz_embedding ruvector.vector(385), -- Lorentz model (+1 dim)
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curvature FLOAT DEFAULT -1.0,
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hierarchy_level INT DEFAULT 0,
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parent_id UUID REFERENCES claude_flow.hyperbolic_embeddings(id),
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Graph nodes for GNN operations
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CREATE TABLE IF NOT EXISTS claude_flow.graph_nodes (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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node_id VARCHAR(255) NOT NULL UNIQUE,
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node_type VARCHAR(50),
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embedding ruvector.vector(384),
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features JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- Graph edges for GNN message passing
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CREATE TABLE IF NOT EXISTS claude_flow.graph_edges (
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id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
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source_id UUID REFERENCES claude_flow.graph_nodes(id),
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target_id UUID REFERENCES claude_flow.graph_nodes(id),
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edge_type VARCHAR(50),
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weight FLOAT DEFAULT 1.0,
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metadata JSONB DEFAULT '{}',
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created_at TIMESTAMPTZ DEFAULT NOW()
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);
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-- ============================================
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-- PART 3: RUVECTOR INDICES (HNSW + IVFFlat)
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-- ============================================
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-- HNSW index for embeddings (~61µs search latency)
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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 ruvector_hnsw (embedding ruvector.cosine_ops)
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WITH (m = 16, ef_construction = 100);
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-- HNSW index for patterns
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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 ruvector_hnsw (embedding ruvector.cosine_ops)
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WITH (m = 16, ef_construction = 100);
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-- HNSW index for agent memory
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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 ruvector_hnsw (memory_embedding ruvector.cosine_ops)
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WITH (m = 16, ef_construction = 64);
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-- HNSW index for hyperbolic embeddings
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CREATE INDEX IF NOT EXISTS idx_hyperbolic_hnsw
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ON claude_flow.hyperbolic_embeddings
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USING ruvector_hnsw (euclidean_embedding ruvector.cosine_ops)
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WITH (m = 16, ef_construction = 100);
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-- HNSW index for graph nodes
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CREATE INDEX IF NOT EXISTS idx_graph_nodes_hnsw
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ON claude_flow.graph_nodes
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USING ruvector_hnsw (embedding ruvector.cosine_ops)
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WITH (m = 16, ef_construction = 64);
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-- IVFFlat index for memory-efficient search (optional)
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-- CREATE INDEX IF NOT EXISTS idx_embeddings_ivfflat
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-- ON claude_flow.embeddings
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-- USING ruvector_ivfflat (embedding ruvector.cosine_ops)
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-- WITH (lists = 100);
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-- ============================================
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-- PART 4: RUVECTOR CORE FUNCTIONS
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-- ============================================
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-- Semantic similarity search using RuVector
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CREATE OR REPLACE FUNCTION claude_flow.search_similar(
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query_embedding ruvector.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 $$
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BEGIN
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RETURN QUERY
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SELECT
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e.id,
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e.content,
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ruvector.cosine_similarity(e.embedding, query_embedding)::FLOAT AS similarity,
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e.metadata
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FROM claude_flow.embeddings e
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WHERE e.embedding IS NOT NULL
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AND ruvector.cosine_similarity(e.embedding, query_embedding) >= min_similarity
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ORDER BY e.embedding <-> query_embedding -- RuVector distance operator
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LIMIT limit_count;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- Hybrid search (vector + BM25 sparse)
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CREATE OR REPLACE FUNCTION claude_flow.hybrid_search(
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query_embedding ruvector.vector(384),
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query_text TEXT,
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limit_count INT DEFAULT 10,
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vector_weight FLOAT DEFAULT 0.7
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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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vector_score FLOAT,
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sparse_score FLOAT,
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combined_score FLOAT,
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metadata JSONB
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) AS $$
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BEGIN
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RETURN QUERY
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SELECT
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e.id,
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e.content,
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ruvector.cosine_similarity(e.embedding, query_embedding)::FLOAT AS v_score,
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COALESCE(ruvector.bm25_score(e.sparse_embedding, query_text), 0)::FLOAT AS s_score,
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(
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vector_weight * ruvector.cosine_similarity(e.embedding, query_embedding) +
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(1 - vector_weight) * COALESCE(ruvector.bm25_score(e.sparse_embedding, query_text), 0)
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)::FLOAT AS c_score,
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e.metadata
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FROM claude_flow.embeddings e
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WHERE e.embedding IS NOT NULL
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ORDER BY c_score DESC
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LIMIT limit_count;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- ============================================
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-- PART 5: ATTENTION MECHANISM FUNCTIONS
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-- ============================================
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-- Self-attention using RuVector's attention functions
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CREATE OR REPLACE FUNCTION claude_flow.self_attention(
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source_ids UUID[],
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temperature FLOAT DEFAULT 1.0
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)
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RETURNS TABLE (
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source_id UUID,
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target_id UUID,
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attention_weight FLOAT
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) AS $$
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BEGIN
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RETURN QUERY
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WITH sources AS (
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SELECT id, embedding
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FROM claude_flow.embeddings
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WHERE id = ANY(source_ids)
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AND embedding IS NOT NULL
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)
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SELECT
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s1.id AS source_id,
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s2.id AS target_id,
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ruvector.softmax_attention(s1.embedding, s2.embedding, temperature)::FLOAT AS attention_weight
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FROM sources s1
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CROSS JOIN sources s2;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- Multi-head attention
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CREATE OR REPLACE FUNCTION claude_flow.multihead_attention(
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query_embedding ruvector.vector(384),
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num_heads INT DEFAULT 8,
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limit_count INT DEFAULT 10
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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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head_attentions FLOAT[],
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aggregated_attention FLOAT,
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metadata JSONB
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) AS $$
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BEGIN
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RETURN QUERY
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SELECT
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e.id,
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e.content,
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ruvector.multihead_attention_scores(query_embedding, e.embedding, num_heads)::FLOAT[] AS head_scores,
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ruvector.multihead_attention_aggregate(query_embedding, e.embedding, num_heads)::FLOAT AS agg_score,
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e.metadata
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FROM claude_flow.embeddings e
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WHERE e.embedding IS NOT NULL
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ORDER BY agg_score DESC
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LIMIT limit_count;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- Cross-attention between query and key-value sets
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CREATE OR REPLACE FUNCTION claude_flow.cross_attention(
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query_ids UUID[],
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kv_filter JSONB DEFAULT NULL,
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temperature FLOAT DEFAULT 1.0
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)
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RETURNS TABLE (
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query_id UUID,
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kv_id UUID,
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attention_weight FLOAT
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) AS $$
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BEGIN
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RETURN QUERY
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WITH queries AS (
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SELECT id, embedding
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FROM claude_flow.embeddings
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WHERE id = ANY(query_ids) AND embedding IS NOT NULL
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),
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kv AS (
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SELECT id, embedding
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FROM claude_flow.embeddings
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WHERE embedding IS NOT NULL
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AND id != ALL(query_ids)
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AND (kv_filter IS NULL OR metadata @> kv_filter)
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)
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SELECT
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q.id AS query_id,
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kv.id AS kv_id,
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ruvector.cross_attention_score(q.embedding, kv.embedding, temperature)::FLOAT AS attention_weight
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FROM queries q
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CROSS JOIN kv;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- ============================================
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-- PART 6: GNN OPERATIONS
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-- ============================================
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-- Message passing for GNN
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CREATE OR REPLACE FUNCTION claude_flow.gnn_message_pass(
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node_id VARCHAR(255),
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aggregation TEXT DEFAULT 'mean' -- 'mean', 'sum', 'max'
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)
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RETURNS ruvector.vector(384) AS $$
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DECLARE
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v_result ruvector.vector(384);
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v_node_uuid UUID;
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BEGIN
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-- Get node UUID
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SELECT id INTO v_node_uuid
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FROM claude_flow.graph_nodes
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WHERE graph_nodes.node_id = gnn_message_pass.node_id;
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-- Aggregate neighbor embeddings using RuVector GNN functions
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SELECT ruvector.gnn_aggregate(
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ARRAY_AGG(n.embedding),
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aggregation
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)
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INTO v_result
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FROM claude_flow.graph_edges e
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JOIN claude_flow.graph_nodes n ON n.id = e.target_id
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WHERE e.source_id = v_node_uuid;
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RETURN v_result;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- Graph attention network (GAT) scoring
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CREATE OR REPLACE FUNCTION claude_flow.gat_attention(
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source_node VARCHAR(255),
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limit_count INT DEFAULT 10
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)
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RETURNS TABLE (
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neighbor_id VARCHAR(255),
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attention_weight FLOAT,
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edge_type VARCHAR(50)
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) AS $$
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BEGIN
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RETURN QUERY
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WITH source AS (
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SELECT id, embedding
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FROM claude_flow.graph_nodes
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WHERE node_id = source_node
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)
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SELECT
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n.node_id AS neighbor_id,
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ruvector.gat_score(s.embedding, n.embedding)::FLOAT AS attention_weight,
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e.edge_type
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FROM source s
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JOIN claude_flow.graph_edges e ON e.source_id = s.id
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JOIN claude_flow.graph_nodes n ON n.id = e.target_id
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ORDER BY attention_weight DESC
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LIMIT limit_count;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- ============================================
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-- PART 7: HYPERBOLIC OPERATIONS
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-- ============================================
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-- Convert Euclidean to Poincaré embedding
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CREATE OR REPLACE FUNCTION claude_flow.to_poincare(
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euclidean ruvector.vector(384),
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curvature FLOAT DEFAULT -1.0
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)
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RETURNS ruvector.vector(384) AS $$
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BEGIN
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RETURN ruvector.exp_map_poincare(euclidean, curvature);
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END;
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$$ LANGUAGE plpgsql IMMUTABLE;
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-- Poincaré distance (geodesic)
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CREATE OR REPLACE FUNCTION claude_flow.poincare_distance(
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x ruvector.vector(384),
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y ruvector.vector(384),
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curvature FLOAT DEFAULT -1.0
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)
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RETURNS FLOAT AS $$
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BEGIN
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RETURN ruvector.hyperbolic_distance(x, y, 'poincare', curvature);
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END;
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$$ LANGUAGE plpgsql IMMUTABLE;
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-- Hyperbolic midpoint (Möbius addition)
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CREATE OR REPLACE FUNCTION claude_flow.poincare_midpoint(
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x ruvector.vector(384),
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y ruvector.vector(384),
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curvature FLOAT DEFAULT -1.0
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)
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RETURNS ruvector.vector(384) AS $$
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BEGIN
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RETURN ruvector.mobius_add(x, y, curvature);
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END;
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$$ LANGUAGE plpgsql IMMUTABLE;
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-- Hierarchical search in hyperbolic space
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CREATE OR REPLACE FUNCTION claude_flow.hyperbolic_search(
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query ruvector.vector(384),
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limit_count INT DEFAULT 10,
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curvature FLOAT DEFAULT -1.0
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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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euclidean_dist FLOAT,
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hyperbolic_dist FLOAT,
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hierarchy_level INT,
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metadata JSONB
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) AS $$
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DECLARE
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v_query_poincare ruvector.vector(384);
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BEGIN
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v_query_poincare := ruvector.exp_map_poincare(query, curvature);
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RETURN QUERY
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SELECT
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he.id,
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he.content,
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(he.euclidean_embedding <-> query)::FLOAT AS euc_dist,
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ruvector.hyperbolic_distance(he.poincare_embedding, v_query_poincare, 'poincare', curvature)::FLOAT AS hyp_dist,
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he.hierarchy_level,
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he.metadata
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FROM claude_flow.hyperbolic_embeddings he
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WHERE he.euclidean_embedding IS NOT NULL
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ORDER BY hyp_dist
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LIMIT limit_count;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- ============================================
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-- PART 8: LOCAL EMBEDDING GENERATION
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-- ============================================
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-- Generate embedding using RuVector's local fastembed models
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CREATE OR REPLACE FUNCTION claude_flow.embed_text(
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text_content TEXT,
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model_name TEXT DEFAULT 'all-MiniLM-L6-v2'
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)
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RETURNS ruvector.vector(384) AS $$
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BEGIN
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RETURN ruvector.fastembed(text_content, model_name);
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- Batch embed multiple texts
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CREATE OR REPLACE FUNCTION claude_flow.embed_texts_batch(
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texts TEXT[],
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model_name TEXT DEFAULT 'all-MiniLM-L6-v2'
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)
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RETURNS TABLE (
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idx INT,
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embedding ruvector.vector(384)
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) AS $$
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BEGIN
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RETURN QUERY
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SELECT
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ordinality::INT AS idx,
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ruvector.fastembed(t, model_name) AS embedding
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FROM unnest(texts) WITH ORDINALITY AS t;
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END;
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$$ LANGUAGE plpgsql STABLE;
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-- ============================================
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-- PART 9: SELF-LEARNING INDEX OPTIMIZATION
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-- ============================================
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-- Trigger self-learning optimization
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CREATE OR REPLACE FUNCTION claude_flow.optimize_indices()
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RETURNS void AS $$
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BEGIN
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-- RuVector self-learning index optimization
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PERFORM ruvector.learn_optimize('claude_flow.embeddings', 'embedding');
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PERFORM ruvector.learn_optimize('claude_flow.patterns', 'embedding');
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PERFORM ruvector.learn_optimize('claude_flow.agents', 'memory_embedding');
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END;
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$$ LANGUAGE plpgsql;
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-- Schedule periodic optimization (every hour)
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-- Note: Requires pg_cron extension or external scheduler
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-- SELECT cron.schedule('ruvector-optimize', '0 * * * *', 'SELECT claude_flow.optimize_indices()');
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-- ============================================
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-- PART 10: SAMPLE DATA
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-- ============================================
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-- Insert sample embeddings for testing
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INSERT INTO claude_flow.embeddings (content, metadata) VALUES
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('Implementing JWT authentication with refresh token rotation',
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'{"category": "security", "language": "typescript", "agent": "security-architect"}'),
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('Building a RESTful API with Express and TypeScript',
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'{"category": "backend", "language": "typescript", "agent": "coder"}'),
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('Setting up RuVector PostgreSQL for semantic search',
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'{"category": "database", "language": "sql", "agent": "architect"}'),
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('HNSW indexing for sub-millisecond vector search',
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'{"category": "performance", "language": "sql", "agent": "perf-engineer"}'),
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('TDD London School approach with mocks',
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'{"category": "testing", "language": "typescript", "agent": "tester"}'),
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('Multi-head attention mechanism in transformer architectures',
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'{"category": "ai", "language": "python", "agent": "ml-developer"}'),
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('Graph neural networks for knowledge representation',
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'{"category": "ai", "language": "python", "agent": "ml-developer"}'),
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('Hyperbolic embeddings for hierarchical data',
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'{"category": "ai", "language": "python", "agent": "researcher"}'),
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('BM25 ranking for hybrid search optimization',
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'{"category": "search", "language": "sql", "agent": "perf-engineer"}'),
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('Multi-agent swarm coordination patterns',
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'{"category": "ai", "language": "typescript", "agent": "architect"}')
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ON CONFLICT DO NOTHING;
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|
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-- Insert sample agents
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INSERT INTO claude_flow.agents (agent_id, agent_type, state) VALUES
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('coder-001', 'coder', '{"specializations": ["typescript", "react"], "tasks_completed": 42}'),
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('architect-001', 'architect', '{"specializations": ["system-design", "ddd"], "tasks_completed": 28}'),
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|
('tester-001', 'tester', '{"specializations": ["tdd", "integration"], "tasks_completed": 35}'),
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('security-001', 'security-architect', '{"specializations": ["auth", "crypto"], "tasks_completed": 15}'),
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('perf-001', 'perf-engineer', '{"specializations": ["database", "caching"], "tasks_completed": 22}')
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ON CONFLICT (agent_id) DO UPDATE SET last_active = NOW();
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|
|
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-- Insert sample patterns
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|
INSERT INTO claude_flow.patterns (name, pattern_type, description, confidence) VALUES
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('jwt-refresh-rotation', 'security', 'JWT with secure refresh token rotation', 0.92),
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('hnsw-search-pattern', 'performance', 'HNSW index for vector similarity search', 0.95),
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|
('tdd-london-mocks', 'testing', 'London School TDD with mocks first', 0.85),
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|
('hierarchical-swarm', 'coordination', 'Anti-drift hierarchical swarm topology', 0.90),
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|
('gnn-message-passing', 'ai', 'GNN message aggregation pattern', 0.88)
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|
ON CONFLICT DO NOTHING;
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|
|
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-- ============================================
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-- COMPLETION
|
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-- ============================================
|
|
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DO $$
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|
BEGIN
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RAISE NOTICE '';
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|
RAISE NOTICE '============================================';
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|
RAISE NOTICE 'RuVector PostgreSQL Initialization Complete!';
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|
RAISE NOTICE '============================================';
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RAISE NOTICE '';
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RAISE NOTICE 'Extension: ruvector (77+ SQL functions)';
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RAISE NOTICE 'Schema: claude_flow';
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|
RAISE NOTICE 'Tables: embeddings, patterns, agents, trajectories, hyperbolic_embeddings, graph_nodes, graph_edges';
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|
RAISE NOTICE 'Indices: 5 HNSW indices created';
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|
RAISE NOTICE '';
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|
RAISE NOTICE 'Features enabled:';
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|
RAISE NOTICE ' - HNSW/IVFFlat vector indexing';
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|
RAISE NOTICE ' - 39 Attention mechanisms';
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|
RAISE NOTICE ' - GNN operations';
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|
RAISE NOTICE ' - Hyperbolic embeddings (Poincare/Lorentz)';
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|
RAISE NOTICE ' - Sparse vectors (BM25/TF-IDF)';
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|
RAISE NOTICE ' - Local embeddings (fastembed)';
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|
RAISE NOTICE ' - Self-learning optimization';
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|
RAISE NOTICE '';
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|
RAISE NOTICE 'Performance: ~61us latency, 16,400 QPS';
|
|
RAISE NOTICE '';
|
|
END $$;
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