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ruv cd5943df23 Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

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Advanced Features Guide

This guide covers advanced features of Ruvector including hybrid search, filtered search, MMR, quantization techniques, and performance optimization.

Hybrid Search (Vector + Keyword)
Filtered Search
MMR (Maximal Marginal Relevance)
Product Quantization
Conformal Prediction
Performance Optimization
Collection Management
Additional VectorDB Operations
Server REST API
Advanced Filter Expressions
Graph Database
Metrics & Health Monitoring
RVF Format Capabilities
Additional Crates

Hybrid Search

Combine vector similarity with keyword-based BM25 scoring for best of both worlds.

Rust

use ruvector_core::{HybridSearch, HybridConfig};

fn hybrid_search_example(db: &VectorDB) -> Result<(), Box<dyn std::error::Error>> {
    let config = HybridConfig {
        vector_weight: 0.7,  // 70% vector similarity
        bm25_weight: 0.3,    // 30% keyword relevance
        k1: 1.5,             // BM25 parameter
        b: 0.75,             // BM25 parameter
    };

    let hybrid = HybridSearch::new(db, config)?;

    // Search with both vector and keywords
    let results = hybrid.search(
        &query_vector,
        &["machine", "learning", "embeddings"],
        10
    )?;

    for result in results {
        println!(
            "ID: {}, Vector Score: {:.4}, BM25 Score: {:.4}, Combined: {:.4}",
            result.id, result.vector_score, result.bm25_score, result.combined_score
        );
    }

    Ok(())
}

Node.js

const { HybridSearch } = require('ruvector');

const hybrid = new HybridSearch(db, {
    vectorWeight: 0.7,
    bm25Weight: 0.3,
    k1: 1.5,
    b: 0.75
});

const results = await hybrid.search(
    queryVector,
    ['machine', 'learning', 'embeddings'],
    10
);

results.forEach(result => {
    console.log(`ID: ${result.id}`);
    console.log(`  Vector: ${result.vectorScore.toFixed(4)}`);
    console.log(`  BM25: ${result.bm25Score.toFixed(4)}`);
    console.log(`  Combined: ${result.combinedScore.toFixed(4)}`);
});

Use Cases

Document search: Combine semantic similarity with keyword matching
E-commerce: Vector similarity for visual features + text search for descriptions
Q&A systems: Semantic understanding + exact term matching

Filtered Search

Apply metadata filters before or after vector search.

Pre-filtering

Apply filters before graph traversal (efficient for selective filters).

use ruvector_core::{FilteredSearch, FilterExpression, FilterStrategy};
use serde_json::json;

fn pre_filtering_example(db: &VectorDB) -> Result<(), Box<dyn std::error::Error>> {
    let filter = FilterExpression::And(vec![
        FilterExpression::Eq("category".to_string(), json!("tech")),
        FilterExpression::Gte("timestamp".to_string(), json!(1640000000)),
    ]);

    let filtered = FilteredSearch::new(db, FilterStrategy::PreFilter);

    let results = filtered.search(&query_vector, 10, Some(filter))?;

    Ok(())
}

Post-filtering

Traverse full graph, then apply filters (better for loose constraints).

let filtered = FilteredSearch::new(db, FilterStrategy::PostFilter);

let filter = FilterExpression::In(
    "tags".to_string(),
    vec![json!("ml"), json!("ai")]
);

let results = filtered.search(&query_vector, 10, Some(filter))?;

Filter Expressions

// Equality
FilterExpression::Eq("status".into(), json!("active"))

// Comparison
FilterExpression::Gt("score".into(), json!(0.8))
FilterExpression::Gte("timestamp".into(), json!(start_time))
FilterExpression::Lt("price".into(), json!(100))
FilterExpression::Lte("rating".into(), json!(5))

// Set operations
FilterExpression::In("category".into(), vec![json!("a"), json!("b")])
FilterExpression::Nin("id".into(), vec![json!("exclude1"), json!("exclude2")])

// Logical operators
FilterExpression::And(vec![expr1, expr2])
FilterExpression::Or(vec![expr1, expr2])
FilterExpression::Not(Box::new(expr))

Node.js

const { FilteredSearch } = require('ruvector');

const filtered = new FilteredSearch(db, 'preFilter');

const results = await filtered.search(queryVector, 10, {
    and: [
        { field: 'category', op: 'eq', value: 'tech' },
        { field: 'timestamp', op: 'gte', value: 1640000000 }
    ]
});

MMR (Maximal Marginal Relevance)

Diversify search results to reduce redundancy.

Rust

use ruvector_core::{MMRSearch, MMRConfig};

fn mmr_example(db: &VectorDB) -> Result<(), Box<dyn std::error::Error>> {
    let config = MMRConfig {
        lambda: 0.5,        // Balance relevance (1.0) vs diversity (0.0)
        diversity_weight: 0.3,
    };

    let mmr = MMRSearch::new(db, config)?;

    // Get diverse results
    let results = mmr.search(&query_vector, 20)?;

    println!("Diverse results (λ = 0.5):");
    for (i, result) in results.iter().enumerate() {
        println!("{}. ID: {}, Relevance: {:.4}", i + 1, result.id, result.score);
    }

    Ok(())
}

Lambda Parameter

λ = 1.0: Pure relevance (no diversity)
λ = 0.5: Balanced (recommended)
λ = 0.0: Pure diversity (may sacrifice relevance)

Node.js

const { MMRSearch } = require('ruvector');

const mmr = new MMRSearch(db, {
    lambda: 0.5,
    diversityWeight: 0.3
});

const results = await mmr.search(queryVector, 20);

Use Cases

Recommendation systems: Avoid showing too many similar items
Document retrieval: Diverse perspectives on a topic
Search results: Reduce redundancy in top results

Product Quantization

Achieve 8-16x memory compression with 90-95% recall.

Rust

use ruvector_core::{EnhancedPQ, PQConfig};

fn product_quantization_example() -> Result<(), Box<dyn std::error::Error>> {
    let mut options = DbOptions::default();
    options.dimensions = 128;
    options.quantization = Some(QuantizationConfig::Product {
        subspaces: 16,  // Split into 16 subvectors of 8D each
        k: 256,         // 256 centroids per subspace
    });

    let db = VectorDB::new(options)?;

    // Insert vectors (automatically quantized)
    db.insert_batch(vectors)?;

    // Search uses quantized vectors
    let results = db.search(&query)?;

    Ok(())
}

Configuration

Subspaces	Dimensions per subspace	Compression	Recall
8	16	8x	92-95%
16	8	16x	90-94%
32	4	32x	85-90%

Node.js

const db = new VectorDB({
    dimensions: 128,
    quantization: {
        type: 'product',
        subspaces: 16,
        k: 256
    }
});

Performance Impact

Without PQ: 1M vectors × 128 dims × 4 bytes = 512 MB
With PQ (16 subspaces): 1M vectors × 16 bytes = 16 MB (32x compression)
+ Codebooks: 16 × 256 × 8 × 4 bytes = 128 KB
Total: ~16.1 MB

Conformal Prediction

Get confidence intervals for predictions.

Rust

use ruvector_core::{ConformalPredictor, ConformalConfig, PredictionSet};

fn conformal_prediction_example() -> Result<(), Box<dyn std::error::Error>> {
    let config = ConformalConfig {
        alpha: 0.1,              // 90% confidence
        calibration_size: 1000,  // Calibration set size
    };

    let mut predictor = ConformalPredictor::new(config);

    // Calibrate with known similarities
    let calibration_data: Vec<(Vec<f32>, Vec<f32>, f64)> = get_calibration_data();
    predictor.calibrate(&calibration_data)?;

    // Predict with confidence
    let prediction: PredictionSet = predictor.predict(&query_vector, &db)?;

    println!("Prediction set size: {}", prediction.candidates.len());
    println!("Confidence level: {:.1}%", (1.0 - config.alpha) * 100.0);

    for candidate in prediction.candidates {
        println!(
            "  ID: {}, Distance: {:.4}, Confidence: [{:.4}, {:.4}]",
            candidate.id,
            candidate.distance,
            candidate.confidence_lower,
            candidate.confidence_upper
        );
    }

    Ok(())
}

Node.js

const { ConformalPredictor } = require('ruvector');

const predictor = new ConformalPredictor({
    alpha: 0.1,           // 90% confidence
    calibrationSize: 1000
});

// Calibrate
await predictor.calibrate(calibrationData);

// Predict with confidence
const prediction = await predictor.predict(queryVector, db);

console.log(`Prediction set size: ${prediction.candidates.length}`);
prediction.candidates.forEach(c => {
    console.log(`ID: ${c.id}, Distance: ${c.distance.toFixed(4)}`);
    console.log(`  Confidence: [${c.confidenceLower.toFixed(4)}, ${c.confidenceUpper.toFixed(4)}]`);
});

Use Cases

Adaptive top-k: Dynamically adjust number of results based on confidence
Query routing: Route uncertain queries to expensive rerankers
Trust scores: Provide confidence metrics to users

Performance Optimization

1. SIMD Optimization

# Enable all SIMD instructions for your CPU
RUSTFLAGS="-C target-cpu=native" cargo build --release

# Specific features
RUSTFLAGS="-C target-feature=+avx2,+fma" cargo build --release

# Verify SIMD is enabled
cargo build --release -vv | grep target-cpu

2. Memory-Mapped Vectors

let mut options = DbOptions::default();
// options.mmap_vectors = true;  // Enable memory mapping (if supported by storage backend)

let db = VectorDB::new(options)?;

Benefits:

Instant loading (no deserialization)
Datasets larger than RAM
OS-managed caching

3. Batch Operations

// ❌ Slow: Individual inserts
for entry in entries {
    db.insert(entry)?;  // Many individual operations
}

// ✅ Fast: Batch insert
db.insert_batch(entries)?;  // Single optimized operation

Performance: 10-100x faster for large batches.

4. Parallel Search

use rayon::prelude::*;

let queries: Vec<Vec<f32>> = get_query_vectors();

let results: Vec<Vec<SearchResult>> = queries
    .par_iter()
    .map(|query| {
        db.search(&SearchQuery {
            vector: query.clone(),
            k: 10,
            filter: None,
            include_vectors: false,
        }).unwrap()
    })
    .collect();

5. HNSW Parameter Tuning

// For speed (lower recall)
options.hnsw_config.as_mut().unwrap().ef_search = 50;

// For accuracy (slower)
options.hnsw_config.as_mut().unwrap().ef_search = 500;

// Balanced (recommended)
options.hnsw_config.as_mut().unwrap().ef_search = 100;

6. Quantization

// 4x compression, 97-99% recall
options.quantization = Some(QuantizationConfig::Scalar);

// 16x compression, 90-95% recall
options.quantization = Some(QuantizationConfig::Product {
    subspaces: 16,
    k: 256,
});

7. Distance Metric Selection

// For normalized embeddings (faster)
options.distance_metric = DistanceMetric::DotProduct;

// For unnormalized embeddings
options.distance_metric = DistanceMetric::Cosine;  // Auto-normalizes

// For general similarity
options.distance_metric = DistanceMetric::Euclidean;

Performance Comparison

Configuration	Memory	Latency	Recall
Full precision, ef=50	100%	0.5ms	85%
Full precision, ef=100	100%	1.0ms	95%
Full precision, ef=500	100%	5.0ms	99%
Scalar quant, ef=100	25%	0.8ms	94%
Product quant, ef=100	6%	1.2ms	92%

Complete Advanced Example

use ruvector_core::*;

fn advanced_demo() -> Result<(), Box<dyn std::error::Error>> {
    // Create high-performance database
    let mut options = DbOptions::default();
    options.dimensions = 384;
    options.storage_path = "./advanced_db.db".to_string();
    options.hnsw_config = Some(HnswConfig {
        m: 64,
        ef_construction: 400,
        ef_search: 200,
        max_elements: 10_000_000,
    });
    options.distance_metric = DistanceMetric::Cosine;
    options.quantization = Some(QuantizationConfig::Product {
        subspaces: 16,
        k: 256,
    });

    let db = VectorDB::new(options)?;

    // Hybrid search with filtering
    let hybrid_config = HybridConfig {
        vector_weight: 0.7,
        bm25_weight: 0.3,
        k1: 1.5,
        b: 0.75,
    };
    let hybrid = HybridSearch::new(&db, hybrid_config)?;

    let filter = FilterExpression::And(vec![
        FilterExpression::Eq("category".into(), json!("research")),
        FilterExpression::Gte("year".into(), json!(2020)),
    ]);

    // Search with all features
    let results = hybrid.search_filtered(
        &query_vector,
        &["neural", "networks"],
        20,
        Some(filter)
    )?;

    // Apply MMR for diversity
    let mmr_config = MMRConfig {
        lambda: 0.6,
        diversity_weight: 0.4,
    };
    let diverse_results = MMRSearch::rerank(&results, mmr_config)?;

    // Conformal prediction for confidence
    let mut predictor = ConformalPredictor::new(ConformalConfig {
        alpha: 0.1,
        calibration_size: 1000,
    });
    predictor.calibrate(&calibration_data)?;
    let prediction = predictor.predict_batch(&diverse_results)?;

    // Display results with confidence
    for (i, result) in prediction.candidates.iter().enumerate() {
        println!("{}. ID: {} (confidence: {:.1}%)",
            i + 1,
            result.id,
            result.mean_confidence * 100.0
        );
    }

    Ok(())
}

Collection Management

Organize vectors into named collections with alias support.

Rust

use ruvector_collections::CollectionManager;

let manager = CollectionManager::new("./data")?;

// Create collections
manager.create_collection("products", CollectionConfig {
    dimensions: 384,
    distance_metric: "cosine".into(),
    ..Default::default()
})?;

// List, delete, stats
let names = manager.list_collections()?;
let stats = manager.collection_stats("products")?;
println!("Vectors: {}, Size: {} bytes", stats.vectors_count, stats.disk_size_bytes);

// Aliases — point multiple names at one collection
manager.create_alias("shop", "products")?;
manager.list_aliases()?;  // [("shop", "products")]
manager.delete_alias("shop")?;

Node.js

const { CollectionManager } = require('ruvector');

const mgr = new CollectionManager('./data');

mgr.createCollection('products', { dimensions: 384, distanceMetric: 'Cosine' });
const collections = mgr.listCollections();
const stats = mgr.getStats('products');
console.log(`Vectors: ${stats.vectorsCount}, RAM: ${stats.ramSizeBytes}`);

mgr.createAlias('shop', 'products');
mgr.listAliases();
mgr.deleteAlias('shop');

Additional VectorDB Operations

Beyond insert, search, and insert_batch, the Node.js bindings expose:

const { VectorDB } = require('ruvector');
const db = new VectorDB({ dimensions: 128, storagePath: './db' });

// Retrieve a single vector
const entry = db.get('vec_0001');

// Delete a vector
db.delete('vec_0001');

// Count vectors
const count = db.len();
const empty = db.isEmpty();

Server REST API

ruvector-server exposes an Axum-based HTTP API for remote access.

Endpoints

Method	Path	Description
`POST`	`/collections`	Create a collection
`GET`	`/collections`	List all collections
`GET`	`/collections/:name`	Get collection info
`DELETE`	`/collections/:name`	Delete a collection
`PUT`	`/collections/:name/points`	Upsert vectors
`POST`	`/collections/:name/points/search`	Search (with optional `score_threshold`)
`GET`	`/collections/:name/points/:id`	Retrieve a point by ID
`GET`	`/health`	Health check
`GET`	`/ready`	Readiness probe

Example — cURL

# Create a collection
curl -X POST http://localhost:8080/collections \
  -H 'Content-Type: application/json' \
  -d '{"name": "docs", "dimensions": 384, "distanceMetric": "cosine"}'

# Upsert vectors
curl -X PUT http://localhost:8080/collections/docs/points \
  -H 'Content-Type: application/json' \
  -d '{"points": [{"id": "doc_1", "vector": [0.1, ...], "metadata": {"title": "Hello"}}]}'

# Search
curl -X POST http://localhost:8080/collections/docs/points/search \
  -H 'Content-Type: application/json' \
  -d '{"vector": [0.1, ...], "k": 10, "scoreThreshold": 0.8}'

Advanced Filter Expressions

ruvector-filter supports rich filter expressions beyond simple equality.

Available Operators

Operator	Description	Example
`eq`	Equality	`FilterExpression::eq("status", "active")`
`ne`	Not equal	`FilterExpression::ne("status", "deleted")`
`gt`, `gte`	Greater than (or equal)	`FilterExpression::gte("score", 0.8)`
`lt`, `lte`	Less than (or equal)	`FilterExpression::lt("price", 100)`
`in_values`	Set membership	`FilterExpression::in_values("tag", vec!["a","b"])`
`match_text`	Text search	`FilterExpression::match_text("content", "rust")`
`geo_radius`	Geospatial radius	`FilterExpression::geo_radius("location", 37.7, -122.4, 5000.0)`
`and`, `or`	Boolean combinators	`FilterExpression::and(vec![f1, f2])`
`not`	Negation	`FilterExpression::not(expr)`

Payload Indexing

Create field indices for fast filtered search:

use ruvector_filter::{PayloadIndexManager, IndexType};

let mut index_mgr = PayloadIndexManager::new();
index_mgr.create_index("category", IndexType::Keyword)?;
index_mgr.create_index("price", IndexType::Float)?;
index_mgr.create_index("location", IndexType::Geo)?;
index_mgr.create_index("description", IndexType::Text)?;

// Index a payload
index_mgr.index_payload("doc_1", &json!({
    "category": "electronics",
    "price": 299.99,
    "location": {"lat": 37.7749, "lon": -122.4194},
    "description": "High-performance vector database"
}))?;

Node.js

const results = await db.search({
    vector: queryVec,
    k: 10,
    filter: {
        and: [
            { field: 'category', op: 'eq', value: 'electronics' },
            { field: 'price', op: 'lte', value: 500 }
        ]
    }
});

Graph Database

ruvector-graph provides a full property graph database with Cypher query support.

CLI

# Create a graph database
ruvector graph create --db ./graph.db --dimensions 128

# Run Cypher queries
ruvector graph query --db ./graph.db \
  --query "CREATE (n:Person {name: 'Alice', age: 30})"

ruvector graph query --db ./graph.db \
  --query "MATCH (n:Person) WHERE n.age > 25 RETURN n.name, n.age"

# Interactive shell
ruvector graph shell --db ./graph.db

# Start graph server
ruvector graph serve --db ./graph.db --port 8081

Rust API

use ruvector_graph::{GraphDB, NodeBuilder, EdgeBuilder};

let db = GraphDB::new("./graph.db")?;

// Create nodes
let alice = NodeBuilder::new("Person")
    .property("name", "Alice")
    .property("age", 30)
    .build();
let bob = NodeBuilder::new("Person")
    .property("name", "Bob")
    .build();

let alice_id = db.insert_node(alice)?;
let bob_id = db.insert_node(bob)?;

// Create edges
let edge = EdgeBuilder::new("KNOWS", alice_id, bob_id)
    .property("since", 2024)
    .build();
db.insert_edge(edge)?;

// Cypher queries
let results = db.execute_cypher("MATCH (a:Person)-[:KNOWS]->(b) RETURN a.name, b.name")?;

Hybrid Vector + Graph

use ruvector_graph::HybridIndex;

// Combine vector similarity with graph traversal
let hybrid = HybridIndex::new(&graph_db, &vector_db)?;
let results = hybrid.semantic_search(query_vector, 10)?;

Metrics & Health Monitoring

ruvector-metrics provides Prometheus-compatible metrics.

Exposed Metrics

Metric	Type	Description
`ruvector_search_requests_total`	Counter	Searches by collection/status
`ruvector_search_latency_seconds`	Histogram	Search latency
`ruvector_insert_requests_total`	Counter	Inserts by collection
`ruvector_insert_latency_seconds`	Histogram	Insert latency
`ruvector_vectors_total`	Gauge	Total vectors per collection
`ruvector_collections_total`	Gauge	Number of collections
`ruvector_memory_usage_bytes`	Gauge	Memory utilization
`ruvector_uptime_seconds`	Gauge	Server uptime

Node.js

const metrics = db.getMetrics();   // Prometheus text format
const health = db.getHealth();     // { status, uptime, ... }

RVF Format Capabilities

The RVF binary format (via rvf-runtime) provides capabilities beyond basic vector storage. See examples/rvf/ for complete working examples.

Key Capabilities

Feature	API	Description
Quality envelopes	`query_with_envelope()`	Response quality, HNSW vs safety-net stats, budget tracking
Audited queries	`query_audited()`	Auto-appends witness entry per search for compliance
Membership filters	`MembershipFilter`	Bitmap-based tenant isolation (include/exclude modes)
DoS hardening	`BudgetTokenBucket`, `NegativeCache`, `ProofOfWork`	Three-layer defense
Adversarial detection	`is_degenerate_distribution()`, `centroid_distance_cv()`	Detects uniform attack vectors
WASM embedding	`embed_wasm()` / `extract_wasm()`	Self-bootstrapping query engine
Kernel embedding	`embed_kernel()` / `extract_kernel()`	Linux image with cmdline
eBPF embedding	`embed_ebpf()` / `extract_ebpf()`	Socket filter programs
Dashboard embedding	`embed_dashboard()` / `extract_dashboard()`	HTML/JS bundles
Delete + compact	`delete()` + `compact()`	Soft-delete with space reclamation
Lineage derivation	`derive()`	Parent-child snapshots with depth tracking
COW branching	`freeze()` + `branch()`	Copy-on-write staging environments
AGI containers	`AgiContainerBuilder`	Self-describing agent manifests
Witness chains	`create_witness_chain()`	Cryptographic audit trails (SHAKE256)
Segment directory	`segment_dir()`	Enumerate all segments in an RVF file

Additional Crates

RuVector includes 80+ crates. Key specialized crates include:

Crate	npm Package	Description
`ruvector-gnn`	`@ruvector/gnn`	GNN training with EWC forgetting mitigation, LoRA, curriculum learning
`ruvector-attention`	`@ruvector/attention`	50+ attention mechanisms (flash, sparse, hyperbolic, sheaf, MoE)
`ruvector-mincut`	`@ruvector/mincut`	Subpolynomial-time dynamic graph partitioning
`ruvector-solver`	`@ruvector/solver`	Sparse linear solvers (Neumann, CG, forward/backward push)
`ruvector-graph`	`@ruvector/graph-node`	Property graph DB with Cypher, hybrid vector+graph
`ruvector-graph-transformer`	`@ruvector/graph-transformer`	Transformer-based graph encoding
`ruvllm`	—	Full LLM serving with paged attention, speculative decoding, LoRA
`sona`	—	Self-Optimizing Neural Architecture (continual learning)
`rvlite`	—	Lightweight vector DB for edge devices
`ruvector-verified`	—	Cryptographic proof system for vector correctness
`ruvector-postgres`	—	PostgreSQL extension for vector operations

See the API reference and individual crate READMEs for detailed documentation.

Best Practices

Start simple: Begin with default settings, optimize later
Measure first: Profile before optimizing
Batch operations: Always use batch methods for bulk operations
Choose quantization wisely: Scalar for general use, product for extreme scale
Tune HNSW gradually: Increase parameters only if needed
Use appropriate metrics: Cosine for normalized, Euclidean otherwise
Enable SIMD: Always compile with target-cpu=native
Use collections: Organize vectors by domain for better management
Monitor with metrics: Enable Prometheus metrics in production
Use RVF for self-contained files: When you need portability with embedded segments

Next Steps

AgenticDB Quickstart - AI agent memory features
Performance Tuning - Detailed optimization
API Reference - Complete API documentation
Cypher Reference - Graph query language
RVF Examples - Working RVF format examples
Examples - All working code examples

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Advanced Features Guide

Table of Contents

Hybrid Search

Rust

Node.js

Use Cases

Filtered Search

Pre-filtering

Post-filtering

Filter Expressions

Node.js

MMR (Maximal Marginal Relevance)

Rust

Lambda Parameter

Node.js

Use Cases

Product Quantization

Rust

Configuration

Node.js

Performance Impact

Conformal Prediction

Rust

Node.js

Use Cases

Performance Optimization

1. SIMD Optimization

2. Memory-Mapped Vectors

3. Batch Operations

4. Parallel Search

5. HNSW Parameter Tuning

6. Quantization

7. Distance Metric Selection

Performance Comparison

Complete Advanced Example

Collection Management

Rust

Node.js

Additional VectorDB Operations

Server REST API

Endpoints

Example — cURL

Advanced Filter Expressions

Available Operators

Payload Indexing

Node.js

Graph Database

CLI

Rust API

Hybrid Vector + Graph

Metrics & Health Monitoring

Exposed Metrics

Node.js

RVF Format Capabilities

Key Capabilities

Additional Crates

Best Practices

Next Steps

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