rUv
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eb1227047d
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feat(postgres): Add 7 advanced AI modules to ruvector-postgres
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>
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2025-12-02 20:12:48 +00:00 |
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