mirror of
https://github.com/ruvnet/RuVector.git
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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> * fix(docker): Copy entire workspace for pgrx build 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix(docker): Build standalone crate without workspace 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * docs: Update README to enhance clarity and structure * fix(postgres): Resolve compilation errors and Docker build issues - Fix simsimd Option/Result type mismatch in scaled_dot.rs - Fix f32/f64 type conversions in poincare.rs and lorentz.rs - Fix AVX512 missing wrapper functions by using AVX2 fallback - Fix Vec<Vec<f32>> to JsonB for pgrx pg_extern compatibility - Fix DashMap get() to get_mut() for mutable access - Fix router.rs dereference for best_score comparison - Update Dockerfile to copy pre-written SQL file for pgrx - Simplify init.sql to use correct function names - Add postgres-cli npm package for CLI tooling All changes tested successfully in Docker with: - Extension loads with AVX2 SIMD support (8 floats/op) - Distance functions verified working - PostgreSQL 16 container runs successfully 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat: Add ruvLLM examples and enhanced postgres-cli Added from claude/ruvector-lfm2-llm-01YS5Tc7i64PyYCLecT9L1dN branch: - examples/ruvLLM: Complete LLM inference system with SIMD optimization - Pretraining, benchmarking, and optimization system - Real SIMD-optimized CPU inference engine - Comprehensive SOTA benchmark suite - Attention mechanisms, memory management, router Enhanced postgres-cli with full ruvector-postgres integration: - Sparse vector operations (BM25, top-k, prune, conversions) - Hyperbolic geometry (Poincare, Lorentz, Mobius operations) - Agent routing (Tiny Dancer system) - Vector quantization (binary, scalar, product) - Enhanced graph and learning commands 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * fix(postgres-cli): Use native ruvector type instead of pgvector - Change createVectorTable to use ruvector type (native RuVector extension) - Add dimensions column for metadata since ruvector is variable-length - Update index creation to use simple btree (HNSW/IVFFlat TBD) - Tested against Docker container with ruvector extension 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(postgres): Add 53 SQL function definitions for all advanced modules Enable all advanced PostgreSQL extension functions by adding their SQL definitions to the extension file. This exposes all Rust #[pg_extern] functions to PostgreSQL. ## New SQL Functions (53 total) ### Hyperbolic Geometry (8 functions) - ruvector_poincare_distance, ruvector_lorentz_distance - ruvector_mobius_add, ruvector_exp_map, ruvector_log_map - ruvector_poincare_to_lorentz, ruvector_lorentz_to_poincare - ruvector_minkowski_dot ### Sparse Vectors (14 functions) - ruvector_sparse_create, ruvector_sparse_from_dense - ruvector_sparse_dot, ruvector_sparse_cosine, ruvector_sparse_l2_distance - ruvector_sparse_add, ruvector_sparse_scale, ruvector_sparse_to_dense - ruvector_sparse_nnz, ruvector_sparse_dim - ruvector_bm25_score, ruvector_tf_idf, ruvector_sparse_normalize - ruvector_sparse_topk ### GNN - Graph Neural Networks (5 functions) - ruvector_gnn_gcn_layer, ruvector_gnn_graphsage_layer - ruvector_gnn_gat_layer, ruvector_gnn_message_pass - ruvector_gnn_aggregate ### Routing/Agents - "Tiny Dancer" (11 functions) - ruvector_route_query, ruvector_route_with_context - ruvector_calculate_agent_affinity, ruvector_select_best_agent - ruvector_multi_agent_route, ruvector_create_agent_embedding - ruvector_get_routing_stats, ruvector_register_agent - ruvector_update_agent_performance, ruvector_adaptive_route - ruvector_fastgrnn_forward ### Learning/ReasoningBank (7 functions) - ruvector_record_trajectory, ruvector_get_verdict - ruvector_distill_memory, ruvector_adaptive_search - ruvector_learning_feedback, ruvector_get_learning_patterns - ruvector_optimize_search_params ### Graph/Cypher (8 functions) - ruvector_graph_create_node, ruvector_graph_create_edge - ruvector_graph_get_neighbors, ruvector_graph_shortest_path - ruvector_graph_pagerank, ruvector_cypher_query - ruvector_graph_traverse, ruvector_graph_similarity_search ## CLI Updates - Enabled hyperbolic geometry commands in postgres-cli - Added vector distance and normalize commands - Enhanced client with connection pooling and retry logic 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
269 lines
8.9 KiB
Rust
269 lines
8.9 KiB
Rust
// Integration tests for Tiny Dancer Routing module
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//
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// These tests validate the complete routing functionality including
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// agent registration, FastGRNN neural network, and routing decisions.
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#[cfg(test)]
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mod routing_tests {
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use ruvector_postgres::routing::{
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agents::{Agent, AgentRegistry, AgentType},
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fastgrnn::FastGRNN,
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router::{OptimizationTarget, Router, RoutingConstraints},
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};
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#[test]
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fn test_complete_routing_workflow() {
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// Create registry and router
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let registry = AgentRegistry::new();
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let router = Router::with_registry(std::sync::Arc::new(registry));
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// Register diverse agents
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let agents = vec![
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create_agent("gpt-4", 0.03, 500.0, 0.95, vec!["coding", "reasoning"]),
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create_agent("claude-3", 0.025, 400.0, 0.93, vec!["coding", "writing"]),
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create_agent("gpt-3.5", 0.002, 200.0, 0.75, vec!["general", "fast"]),
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create_agent("llama-2", 0.0, 800.0, 0.70, vec!["local", "private"]),
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];
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for agent in agents {
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router.registry().register(agent).unwrap();
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}
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// Test cost-optimized routing
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let request_emb = vec![0.1; 384];
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let decision = router
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.route(&request_emb, &RoutingConstraints::new(), OptimizationTarget::Cost)
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.unwrap();
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assert_eq!(decision.agent_name, "llama-2"); // Free option
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assert!(decision.confidence > 0.0);
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// Test quality-optimized routing
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let decision = router
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.route(&request_emb, &RoutingConstraints::new(), OptimizationTarget::Quality)
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.unwrap();
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assert_eq!(decision.agent_name, "gpt-4"); // Highest quality
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// Test latency-optimized routing
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let decision = router
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.route(&request_emb, &RoutingConstraints::new(), OptimizationTarget::Latency)
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.unwrap();
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assert_eq!(decision.agent_name, "gpt-3.5"); // Fastest
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}
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#[test]
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fn test_routing_with_constraints() {
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let registry = AgentRegistry::new();
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let router = Router::with_registry(std::sync::Arc::new(registry));
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router.registry().register(
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create_agent("expensive-high-quality", 1.0, 200.0, 0.99, vec!["coding"])
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).unwrap();
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router.registry().register(
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create_agent("cheap-medium-quality", 0.01, 200.0, 0.75, vec!["coding"])
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).unwrap();
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let request_emb = vec![0.1; 384];
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// Constrain by max cost
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let constraints = RoutingConstraints::new()
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.with_max_cost(0.5)
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.with_min_quality(0.7);
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let decision = router
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.route(&request_emb, &constraints, OptimizationTarget::Quality)
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.unwrap();
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// Should pick cheap option due to cost constraint
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assert_eq!(decision.agent_name, "cheap-medium-quality");
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}
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#[test]
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fn test_fastgrnn_routing() {
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let mut router = Router::new();
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router.init_grnn(64);
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router.registry().register(
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create_agent("agent1", 0.05, 200.0, 0.85, vec!["coding"])
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).unwrap();
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let request_emb = vec![0.1; 384];
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let decision = router
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.route(&request_emb, &RoutingConstraints::new(), OptimizationTarget::Balanced)
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.unwrap();
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// Verify neural network enhanced confidence
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assert!(decision.confidence > 0.0);
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assert!(decision.confidence <= 1.0);
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}
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#[test]
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fn test_capability_based_routing() {
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let registry = AgentRegistry::new();
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let router = Router::with_registry(std::sync::Arc::new(registry));
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router.registry().register(
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create_agent("coder", 0.05, 200.0, 0.90, vec!["coding", "debugging"])
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).unwrap();
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router.registry().register(
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create_agent("writer", 0.03, 150.0, 0.85, vec!["writing", "translation"])
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).unwrap();
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router.registry().register(
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create_agent("generalist", 0.02, 300.0, 0.70, vec!["coding", "writing", "general"])
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).unwrap();
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let request_emb = vec![0.1; 384];
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// Require coding capability
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let constraints = RoutingConstraints::new()
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.with_capability("coding".to_string());
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let decision = router
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.route(&request_emb, &constraints, OptimizationTarget::Quality)
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.unwrap();
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// Should pick specialized coder (highest quality with coding)
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assert!(decision.agent_name == "coder" || decision.agent_name == "generalist");
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// Verify writer was not selected
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assert_ne!(decision.agent_name, "writer");
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}
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#[test]
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fn test_agent_metrics_update() {
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let registry = AgentRegistry::new();
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let mut agent = create_agent("test-agent", 0.05, 200.0, 0.80, vec!["test"]);
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// Initial state
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assert_eq!(agent.performance.total_requests, 0);
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assert_eq!(agent.performance.avg_latency_ms, 200.0);
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// Update with better latency
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agent.update_metrics(150.0, true, Some(0.85));
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assert_eq!(agent.performance.total_requests, 1);
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assert_eq!(agent.performance.avg_latency_ms, 150.0);
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assert_eq!(agent.performance.success_rate, 1.0);
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// Update with worse latency
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agent.update_metrics(250.0, true, Some(0.75));
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assert_eq!(agent.performance.total_requests, 2);
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assert_eq!(agent.performance.avg_latency_ms, 200.0); // Average of 150 and 250
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assert_eq!(agent.performance.success_rate, 1.0);
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// Failed request
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agent.update_metrics(300.0, false, None);
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assert_eq!(agent.performance.total_requests, 3);
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assert!(agent.performance.success_rate < 1.0);
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}
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#[test]
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fn test_fastgrnn_sequence_processing() {
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let grnn = FastGRNN::new(10, 5);
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let sequence = vec![
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vec![1.0, 0.0, 0.0, 0.5, -0.5, 0.2, -0.2, 0.8, -0.8, 0.0],
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vec![0.0, 1.0, 0.0, -0.5, 0.5, -0.2, 0.2, -0.8, 0.8, 0.0],
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vec![0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
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];
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let outputs = grnn.forward_sequence(&sequence);
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assert_eq!(outputs.len(), 3);
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assert_eq!(outputs[0].len(), 5);
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// Verify state evolution (later states should be different from first)
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let first_state = &outputs[0];
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let last_state = &outputs[2];
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let diff: f32 = first_state
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.iter()
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.zip(last_state.iter())
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.map(|(a, b)| (a - b).abs())
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.sum();
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assert!(diff > 0.0, "Hidden state should evolve across sequence");
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}
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#[test]
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fn test_routing_alternatives() {
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let registry = AgentRegistry::new();
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let router = Router::with_registry(std::sync::Arc::new(registry));
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// Register multiple similar agents
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for i in 0..5 {
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let quality = 0.7 + (i as f32 * 0.05);
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let cost = 0.01 + (i as f32 * 0.01);
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router.registry().register(
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create_agent(&format!("agent-{}", i), cost, 200.0, quality, vec!["test"])
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).unwrap();
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}
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let request_emb = vec![0.1; 384];
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let decision = router
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.route(&request_emb, &RoutingConstraints::new(), OptimizationTarget::Quality)
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.unwrap();
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// Should have alternatives listed
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assert!(!decision.alternatives.is_empty());
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assert!(decision.alternatives.len() <= 3); // Max 3 alternatives
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// Alternatives should have lower scores
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for alt in &decision.alternatives {
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assert!(alt.score < 1.0);
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assert!(!alt.reason.is_empty());
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}
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}
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#[test]
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fn test_excluded_agents() {
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let registry = AgentRegistry::new();
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let router = Router::with_registry(std::sync::Arc::new(registry));
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router.registry().register(
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create_agent("agent-a", 0.05, 200.0, 0.90, vec!["test"])
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).unwrap();
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router.registry().register(
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create_agent("agent-b", 0.05, 200.0, 0.85, vec!["test"])
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).unwrap();
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let request_emb = vec![0.1; 384];
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// Exclude the best agent
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let constraints = RoutingConstraints::new()
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.with_excluded_agent("agent-a".to_string());
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let decision = router
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.route(&request_emb, &constraints, OptimizationTarget::Quality)
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.unwrap();
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assert_eq!(decision.agent_name, "agent-b");
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}
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// Helper function to create test agents
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fn create_agent(
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name: &str,
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cost: f32,
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latency: f32,
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quality: f32,
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capabilities: Vec<&str>,
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) -> Agent {
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let mut agent = Agent::new(
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name.to_string(),
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AgentType::LLM,
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capabilities.iter().map(|s| s.to_string()).collect(),
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);
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agent.cost_model.per_request = cost;
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agent.performance.avg_latency_ms = latency;
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agent.performance.quality_score = quality;
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agent.embedding = Some(vec![0.1; 384]); // Default embedding
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agent
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}
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}
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