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>
229 lines
6.9 KiB
Rust
229 lines
6.9 KiB
Rust
//! Memory service benchmarks for RuvLLM
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//!
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//! Benchmarks HNSW insertion, search, and graph operations.
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use criterion::{black_box, criterion_group, criterion_main, Criterion, BenchmarkId, Throughput};
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use ruvllm::memory::MemoryService;
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use ruvllm::config::MemoryConfig;
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use ruvllm::types::{MemoryNode, MemoryEdge, NodeType, EdgeType};
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use std::collections::HashMap;
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use tokio::runtime::Runtime;
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use rand::{Rng, SeedableRng};
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fn create_random_node(id: &str, dim: usize, seed: u64) -> MemoryNode {
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let mut rng = rand::rngs::StdRng::seed_from_u64(seed);
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let mut vec: Vec<f32> = (0..dim).map(|_| rng.gen::<f32>() - 0.5).collect();
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let norm: f32 = vec.iter().map(|x| x * x).sum::<f32>().sqrt();
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vec.iter_mut().for_each(|x| *x /= norm);
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MemoryNode {
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id: id.into(),
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vector: vec,
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text: format!("Node {}", id),
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node_type: NodeType::Document,
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source: "bench".into(),
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metadata: HashMap::new(),
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}
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}
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fn benchmark_memory_insert(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = MemoryConfig::default();
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let memory = rt.block_on(MemoryService::new(&config)).unwrap();
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let mut counter = 0u64;
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c.bench_function("memory_insert_single", |b| {
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b.iter(|| {
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counter += 1;
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let node = create_random_node(&format!("bench-{}", counter), 768, counter);
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black_box(memory.insert_node(node).unwrap())
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})
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});
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}
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fn benchmark_memory_insert_batch(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let mut group = c.benchmark_group("memory_insert_batch");
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for batch_size in [10, 50, 100, 500] {
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group.throughput(Throughput::Elements(batch_size as u64));
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let config = MemoryConfig::default();
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let memory = rt.block_on(MemoryService::new(&config)).unwrap();
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let nodes: Vec<MemoryNode> = (0..batch_size)
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.map(|i| create_random_node(&format!("batch-{}", i), 768, i as u64))
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.collect();
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group.bench_with_input(
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BenchmarkId::from_parameter(batch_size),
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&nodes,
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|b, nodes| {
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b.iter(|| {
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for node in nodes.clone() {
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black_box(memory.insert_node(node).unwrap());
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}
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})
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},
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);
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}
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group.finish();
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}
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fn benchmark_memory_search(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = MemoryConfig::default();
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let memory = rt.block_on(MemoryService::new(&config)).unwrap();
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// Pre-populate with nodes
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for i in 0..1000 {
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let node = create_random_node(&format!("search-{}", i), 768, i as u64);
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memory.insert_node(node).unwrap();
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}
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let query = vec![0.1f32; 768];
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c.bench_function("memory_search_k10_1000", |b| {
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b.to_async(&rt).iter(|| async {
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black_box(memory.search_with_graph(&query, 10, 64, 0).await.unwrap())
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})
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});
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}
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fn benchmark_memory_search_varying_k(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = MemoryConfig::default();
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let memory = rt.block_on(MemoryService::new(&config)).unwrap();
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// Pre-populate
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for i in 0..1000 {
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let node = create_random_node(&format!("k-{}", i), 768, i as u64);
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memory.insert_node(node).unwrap();
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}
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let query = vec![0.1f32; 768];
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let mut group = c.benchmark_group("memory_search_k");
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for k in [1, 5, 10, 20, 50, 100] {
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group.bench_with_input(
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BenchmarkId::from_parameter(k),
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&k,
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|b, &k| {
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b.to_async(&rt).iter(|| async {
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black_box(memory.search_with_graph(&query, k, 64, 0).await.unwrap())
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})
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},
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);
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}
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group.finish();
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}
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fn benchmark_memory_search_varying_ef(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = MemoryConfig::default();
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let memory = rt.block_on(MemoryService::new(&config)).unwrap();
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// Pre-populate
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for i in 0..1000 {
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let node = create_random_node(&format!("ef-{}", i), 768, i as u64);
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memory.insert_node(node).unwrap();
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}
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let query = vec![0.1f32; 768];
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let mut group = c.benchmark_group("memory_search_ef");
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for ef in [16, 32, 64, 128, 256] {
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group.bench_with_input(
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BenchmarkId::from_parameter(ef),
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&ef,
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|b, &ef| {
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b.to_async(&rt).iter(|| async {
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black_box(memory.search_with_graph(&query, 10, ef, 0).await.unwrap())
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})
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},
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);
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}
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group.finish();
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}
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fn benchmark_memory_search_with_graph(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let config = MemoryConfig::default();
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let memory = rt.block_on(MemoryService::new(&config)).unwrap();
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// Pre-populate with nodes and edges
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for i in 0..500 {
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let node = create_random_node(&format!("graph-{}", i), 768, i as u64);
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memory.insert_node(node).unwrap();
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}
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for i in 0..499 {
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let edge = MemoryEdge {
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id: format!("edge-{}", i),
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src: format!("graph-{}", i),
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dst: format!("graph-{}", i + 1),
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edge_type: EdgeType::Follows,
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weight: 0.8,
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metadata: HashMap::new(),
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};
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memory.insert_edge(edge).unwrap();
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}
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let query = vec![0.1f32; 768];
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let mut group = c.benchmark_group("memory_search_hops");
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for hops in [0, 1, 2, 3] {
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group.bench_with_input(
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BenchmarkId::from_parameter(hops),
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&hops,
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|b, &hops| {
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b.to_async(&rt).iter(|| async {
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black_box(memory.search_with_graph(&query, 10, 64, hops).await.unwrap())
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})
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},
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);
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}
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group.finish();
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}
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fn benchmark_memory_scaling(c: &mut Criterion) {
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let rt = Runtime::new().unwrap();
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let mut group = c.benchmark_group("memory_scaling");
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for num_nodes in [100, 500, 1000, 5000] {
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let config = MemoryConfig::default();
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let memory = rt.block_on(MemoryService::new(&config)).unwrap();
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// Pre-populate
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for i in 0..num_nodes {
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let node = create_random_node(&format!("scale-{}", i), 768, i as u64);
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memory.insert_node(node).unwrap();
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}
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let query = vec![0.1f32; 768];
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group.bench_with_input(
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BenchmarkId::from_parameter(num_nodes),
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&num_nodes,
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|b, _| {
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b.to_async(&rt).iter(|| async {
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black_box(memory.search_with_graph(&query, 10, 64, 0).await.unwrap())
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})
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},
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);
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}
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group.finish();
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}
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criterion_group!(
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benches,
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benchmark_memory_insert,
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benchmark_memory_insert_batch,
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benchmark_memory_search,
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benchmark_memory_search_varying_k,
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benchmark_memory_search_varying_ef,
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benchmark_memory_search_with_graph,
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benchmark_memory_scaling,
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);
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criterion_main!(benches);
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