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eb1227047d
Comprehensive implementation of advanced AI capabilities: ## New Modules (23,541 lines of code) ### 1. Self-Learning / ReasoningBank (`src/learning/`) - Trajectory tracking for query optimization - Pattern extraction using K-means clustering - ReasoningBank for pattern storage and matching - Adaptive search parameter optimization ### 2. Attention Mechanisms (`src/attention/`) - Scaled dot-product attention (core) - Multi-head attention with parallel heads - Flash Attention v2 (memory-efficient) - 10 attention types with PostgresEnum support ### 3. GNN Layers (`src/gnn/`) - Message passing framework - GCN (Graph Convolutional Network) - GraphSAGE with mean/max aggregation - Configurable aggregation methods ### 4. Hyperbolic Embeddings (`src/hyperbolic/`) - Poincaré ball model - Lorentz hyperboloid model - Hyperbolic distance metrics - Möbius operations ### 5. Sparse Vectors (`src/sparse/`) - COO format sparse vector type - Efficient sparse-sparse distance functions - BM25/SPLADE compatible - Top-k pruning operations ### 6. Graph Operations & Cypher (`src/graph/`) - Property graph storage (nodes/edges) - BFS, DFS, Dijkstra traversal - Cypher query parser (AST-based) - Query executor with pattern matching ### 7. Tiny Dancer Routing (`src/routing/`) - FastGRNN neural network - Agent registry with capabilities - Multi-objective routing optimization - Cost/latency/quality balancing ## Docker Infrastructure - Dockerfile with pgrx 0.12.6 and PostgreSQL 16 - docker-compose.yml with test runner - Initialization SQL with test tables - Shell scripts for dev/test/benchmark ## Feature Flags - `learning`, `attention`, `gnn`, `hyperbolic` - `sparse`, `graph`, `routing` - `ai-complete` and `graph-complete` bundles 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
78 lines
2.1 KiB
SQL
78 lines
2.1 KiB
SQL
-- RuVector-Postgres Initialization Script
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-- Creates extension and test tables
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-- Create the extension
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CREATE EXTENSION IF NOT EXISTS ruvector;
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-- Create test schema
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CREATE SCHEMA IF NOT EXISTS ruvector_test;
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-- Test table for vectors
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CREATE TABLE ruvector_test.vectors (
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id SERIAL PRIMARY KEY,
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embedding vector(768),
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sparse_embedding sparsevec(30000),
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category TEXT,
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metadata JSONB,
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created_at TIMESTAMP DEFAULT NOW()
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);
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-- Test table for graph nodes
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CREATE TABLE ruvector_test.nodes (
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id SERIAL PRIMARY KEY,
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label TEXT NOT NULL,
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embedding vector(256),
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properties JSONB,
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created_at TIMESTAMP DEFAULT NOW()
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);
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-- Test table for graph edges
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CREATE TABLE ruvector_test.edges (
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id SERIAL PRIMARY KEY,
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src_id INTEGER REFERENCES ruvector_test.nodes(id),
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dst_id INTEGER REFERENCES ruvector_test.nodes(id),
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edge_type TEXT NOT NULL,
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weight FLOAT DEFAULT 1.0,
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properties JSONB,
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created_at TIMESTAMP DEFAULT NOW()
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);
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-- Test table for learning trajectories
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CREATE TABLE ruvector_test.trajectories (
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id SERIAL PRIMARY KEY,
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query_vector vector(768),
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result_ids INTEGER[],
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latency_ms FLOAT,
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recall_score FLOAT,
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created_at TIMESTAMP DEFAULT NOW()
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);
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-- Test table for routing agents
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CREATE TABLE ruvector_test.agents (
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id SERIAL PRIMARY KEY,
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name TEXT UNIQUE NOT NULL,
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agent_type TEXT NOT NULL,
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capabilities TEXT[],
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capability_embedding vector(768),
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cost_per_1k_tokens FLOAT,
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avg_latency_ms FLOAT,
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quality_score FLOAT,
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active BOOLEAN DEFAULT TRUE,
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created_at TIMESTAMP DEFAULT NOW()
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);
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-- Create indexes (will be created after extension functions are available)
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-- These are placeholder comments for test setup
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-- Grant permissions
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GRANT ALL ON SCHEMA ruvector_test TO ruvector;
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GRANT ALL ON ALL TABLES IN SCHEMA ruvector_test TO ruvector;
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GRANT ALL ON ALL SEQUENCES IN SCHEMA ruvector_test TO ruvector;
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-- Log initialization
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DO $$
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BEGIN
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RAISE NOTICE 'RuVector-Postgres initialized successfully';
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RAISE NOTICE 'Extension version: %', (SELECT ruvector_version());
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RAISE NOTICE 'SIMD info: %', (SELECT ruvector_simd_info());
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END $$;
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