ruvector/examples/ruvLLM/benches/memory.rs
rUv 073ce73612
feat(postgres): Add 53 SQL function definitions for all advanced modules (#46)
* 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>
2025-12-02 22:49:29 -05:00

229 lines
6.9 KiB
Rust

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