mirror of
https://github.com/ruvnet/RuVector.git
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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>
79 lines
2 KiB
YAML
79 lines
2 KiB
YAML
version: '3.8'
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services:
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# Development PostgreSQL with ruvector extension
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postgres:
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build:
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context: ../../..
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dockerfile: crates/ruvector-postgres/docker/Dockerfile
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container_name: ruvector-postgres
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ports:
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- "5432:5432"
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environment:
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POSTGRES_USER: ruvector
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POSTGRES_PASSWORD: ruvector
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POSTGRES_DB: ruvector_test
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# Performance tuning
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POSTGRES_INITDB_ARGS: "--data-checksums"
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volumes:
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- postgres_data:/var/lib/postgresql/data
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- ./init.sql:/docker-entrypoint-initdb.d/01-init.sql
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healthcheck:
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test: ["CMD-SHELL", "pg_isready -U ruvector -d ruvector_test"]
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interval: 5s
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timeout: 5s
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retries: 5
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networks:
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- ruvector-network
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# Test runner container
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test-runner:
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build:
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context: ../../..
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dockerfile: crates/ruvector-postgres/docker/Dockerfile.test
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container_name: ruvector-test-runner
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depends_on:
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postgres:
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condition: service_healthy
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environment:
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DATABASE_URL: postgres://ruvector:ruvector@postgres:5432/ruvector_test
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RUST_LOG: info
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RUST_BACKTRACE: 1
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volumes:
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- ../../..:/app
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- cargo_cache:/usr/local/cargo/registry
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- target_cache:/app/target
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networks:
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- ruvector-network
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command: ["cargo", "test", "--features", "pg_test"]
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# Benchmark runner
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benchmark:
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build:
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context: ../../..
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dockerfile: crates/ruvector-postgres/docker/Dockerfile.test
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container_name: ruvector-benchmark
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depends_on:
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postgres:
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condition: service_healthy
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environment:
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DATABASE_URL: postgres://ruvector:ruvector@postgres:5432/ruvector_test
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RUST_LOG: info
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volumes:
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- ../../..:/app
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- cargo_cache:/usr/local/cargo/registry
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- target_cache:/app/target
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networks:
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- ruvector-network
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command: ["cargo", "bench", "--features", "pg_test"]
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profiles:
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- benchmark
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volumes:
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postgres_data:
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cargo_cache:
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target_cache:
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networks:
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ruvector-network:
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driver: bridge
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