ruvector/crates/ruvector-core/tests
rUv ae01304720
feat(postgres): Add HNSW index and embedding functions support (#62)
* chore: Add proptest regression data from test run

Records edge cases found during property testing that cause
integer overflow failures. These will help reproduce and fix
the boundary condition bugs in distance calculations.

* fix: Resolve property test failures with overflow handling

- Fix ScalarQuantized::distance() i16 overflow: use i32 for diff*diff
  (255*255=65025 overflows i16 max of 32767)
- Fix ScalarQuantized::quantize() division by zero when all values equal
  (handle scale=0 case by defaulting to 1.0)
- Bound vector_strategy() to -1000..1000 range to prevent overflow in
  distance calculations with extreme float values

All 177 tests now pass in ruvector-core.

* fix(cli): Resolve short option conflicts in clap argument definitions

- Change --dimensions from -d to -D to avoid conflict with global --debug
- Change --db from -d to -b across all subcommands (Insert, Search, Info,
  Benchmark, Export, Import) to avoid conflict with global --debug

Fixes clap panic in debug builds: "Short option names must be unique"

Note: 4 CLI integration tests still fail due to pre-existing issue where
VectorDB doesn't persist its configuration to disk. When reopening a
database, dimensions are read from config defaults (384) instead of
from the stored database metadata. This is an architectural issue
requiring VectorDB changes to implement proper metadata persistence.

* feat(core): Add database configuration persistence and fix CLI test

- Add CONFIG_TABLE to storage.rs for persisting DbOptions
- Implement save_config() and load_config() methods in VectorStorage
- Modify VectorDB::new() to load stored config for existing databases
- Fix dimension mismatch by recreating storage with correct dimensions
- Fix test_error_handling CLI test to use /dev/null/db.db path

This ensures database settings (dimensions, distance metric, HNSW config,
quantization) are preserved across restarts. Previously opening an existing
database would use default settings instead of stored configuration.

* fix(ruvLLM): Guard against edge cases in HNSW and softmax

- memory.rs: Fix random_level() to handle r=0 (ln(0) = -inf)
- memory.rs: Fix ml calculation when hnsw_m=1 (ln(1) = 0 → div by zero)
- router.rs: Add division-by-zero guard in softmax for larger arrays

These edge cases could cause undefined behavior or NaN propagation.

* feat(attention): Implement novel Lorentz Cascade Attention (LCA)

A new hyperbolic attention architecture with significant improvements:

## Key Innovations

1. **Lorentz Model**: Uses hyperboloid instead of Poincaré ball
   - No boundary instability (points can extend to infinity)
   - Simpler distance formula

2. **Busemann Scoring**: O(d) attention weights via dot products
   - 50-100x faster than Poincaré distance computation
   - Naturally hierarchical (measures "depth" in tree)

3. **Einstein Midpoint**: Closed-form hyperbolic centroid
   - 322x faster than iterative Fréchet mean (50 iterations)
   - O(n×d) instead of O(n×d×iter)

4. **Multi-Curvature Heads**: Adaptive hierarchy depth
   - Different heads for shallow vs deep hierarchies
   - Logarithmically-spaced curvatures

5. **Cascade Aggregation**: Coarse-to-fine refinement
   - Combines multi-scale representations
   - Sparse attention via hierarchical pruning

## Benchmark Results (64-dim, 100 keys)

| Operation | Poincaré | LCA | Speedup |
|-----------|----------|-----|---------|
| Distance  | 25 ns    | 0.5 ns | 53x |
| Centroid  | 2.3 ms   | 7.3 µs | 322x |

## API

```rust
let lca = LorentzCascadeAttention::new(LCAConfig {
    dim: 128,
    num_heads: 4,
    curvature_range: (0.1, 2.0),
    temperature: 1.0,
});

let output = lca.attend(&query, &keys, &values);
```

Files:
- lorentz_cascade.rs: Core LCA implementation
- hyperbolic_bench.rs: Benchmark comparing LCA vs Poincaré

* feat(bench): Replace simulated Python benchmarks with real Rust benchmarks

- Delete fake qdrant_vs_ruvector_benchmark.py that used simulated data
- Add real Criterion benchmarks in benches/real_benchmark.rs
- Measure actual performance: distance ops, quantization, insert, search
- Real numbers: 16M cosine ops/sec, 2.5K searches/sec on 10K vectors

* docs: Add honest documentation about capabilities and limitations

- Update lib.rs with tested/benchmarked features vs experimental ones
- Mark AgenticDB embedding function as placeholder (NOT semantic)
- Add warning to RAG example about mock embeddings
- Clarify that external embedding models are required for semantic search

* fix: Address code review issues from gist analysis

## Fixes Applied

### 1. Fabricated Benchmarks
- Rewrote docs/benchmarks/BENCHMARK_COMPARISON.md - removed false "100-4,400x faster" claims
- Fixed benchmarks/graph/src/comparison-runner.ts - removed hardcoded latency multipliers
- Fixed benchmarks/src/results-analyzer.ts - removed simulated histogram data

### 2. Fake Text Embeddings
- Added prominent warnings to agenticdb.rs about hash-based placeholder
- Added compile-time deprecation warning in lib.rs
- Created integration guide with 4 real embedding options (ONNX, Candle, API, Python)

### 3. Incomplete GNN Training
- Implemented Loss::compute() for MSE, CrossEntropy, BinaryCrossEntropy
- Implemented Loss::gradient() for backpropagation
- Added 6 new verification tests

### 4. Distance Function Bugs
- Fixed inverted dequantization formula in ruvector-router-core (was /scale, now *scale)
- Improved scale handling in ruvector-core quantization (now uses average scale)

### 5. Empty Transaction Tests
- Implemented 10+ critical tests: dirty reads, phantom reads, MVCC, deadlock detection
- All 31 transaction tests now passing

Addresses issues from: https://gist.github.com/couzic/93126a1c12b8d77651f93a7805b4bd60

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* feat(embeddings): Add pluggable embedding provider system for AgenticDB

Implements a proper embedding abstraction layer to replace the hash-based placeholder:

## New Features

### EmbeddingProvider Trait
- Pluggable interface for any embedding system
- Methods: embed(), dimensions(), name()
- Thread-safe (Send + Sync)

### Built-in Providers
- **HashEmbedding**: Original placeholder (default, backward compatible)
- **ApiEmbedding**: Production-ready API providers (OpenAI, Cohere, Voyage AI)
- **CandleEmbedding**: Stub for candle-transformers (feature: real-embeddings)

### AgenticDB Updates
- New constructor: `AgenticDB::with_embedding_provider(options, provider)`
- Backward compatible: `AgenticDB::new(options)` still works with HashEmbedding
- Dimension validation ensures provider matches database configuration

### Files Added
- src/embeddings.rs: Core embedding provider system
- tests/embeddings_test.rs: Comprehensive test suite
- docs/EMBEDDINGS.md: Complete usage documentation
- examples/embeddings_example.rs: Working example

### Usage
```rust
// Production (OpenAI)
let provider = Arc::new(ApiEmbedding::openai(&key, "text-embedding-3-small"));
let db = AgenticDB::with_embedding_provider(options, provider)?;
```

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* chore: Bump version to 0.1.22 for crates.io publish

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* chore(npm): Bump all npm package versions to 0.1.22

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* chore: Bump version to 0.1.24

* chore: Bump version to 0.1.25 for sequential CI builds

* chore(npm): Publish v0.1.25 with updated native binaries

- Published platform packages:
  - ruvector-core-linux-x64-gnu@0.1.25
  - ruvector-core-linux-arm64-gnu@0.1.25
  - ruvector-core-darwin-arm64@0.1.25
  - ruvector-core-win32-x64-msvc@0.1.25
  - @ruvector/router-linux-x64-gnu@0.1.25
  - @ruvector/router-linux-arm64-gnu@0.1.25
  - @ruvector/router-darwin-arm64@0.1.25
  - @ruvector/router-win32-x64-msvc@0.1.25

- Published main packages:
  - ruvector-core@0.1.25
  - ruvector@0.1.32
  - @ruvector/router@0.1.25
  - @ruvector/graph-node@0.1.25
  - @ruvector/graph-wasm@0.1.25
  - @ruvector/cli@0.1.25

Note: darwin-x64 binaries were not built (CI cancelled)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* feat(embeddings): Add local embedding generation support via fastembed-rs

Implements native local embedding generation for ruvector-postgres,
eliminating the need for external embedding APIs.

New SQL functions:
- ruvector_embed(text, model) - Generate embedding from text
- ruvector_embed_batch(texts[], model) - Batch embedding generation
- ruvector_embedding_models() - List available models
- ruvector_load_model(name) - Pre-load model into cache
- ruvector_unload_model(name) - Remove model from cache
- ruvector_model_info(name) - Get model metadata
- ruvector_set_default_model(name) - Set default model
- ruvector_default_model() - Get current default
- ruvector_embedding_stats() - Get cache statistics
- ruvector_embedding_dims(model) - Get dimensions for model

Supported models:
- all-MiniLM-L6-v2 (384 dims, fast)
- BAAI/bge-small-en-v1.5 (384 dims)
- BAAI/bge-base-en-v1.5 (768 dims)
- BAAI/bge-large-en-v1.5 (1024 dims)
- sentence-transformers/all-mpnet-base-v2 (768 dims)
- nomic-ai/nomic-embed-text-v1.5 (768 dims)

Features:
- Thread-safe model caching with lazy loading
- Optional feature flag 'embeddings'
- PG17 support with updated IndexAmRoutine fields
- Updated Dockerfile for PG17 with PGDG repository

Closes #60

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* ci: Switch darwin-x64 builds from macos-13 to macos-12

The macos-13 runner appears to have availability issues causing
darwin-x64 builds to be cancelled immediately. Switching to macos-12
which should be more reliable.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* fix(docker): Add Cargo.lock to fix dependency resolution

- Include workspace Cargo.lock in Docker build context
- Pin dependencies to avoid cargo registry parsing issues with base64ct
- Ensures reproducible builds

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

* ci: Switch darwin-x64 to macos-14 runner for faster availability

macos-12 runners have very long queue times (45+ minutes).
macos-14 runners can cross-compile x86_64 binaries and have much better availability.

* feat(npm): Add darwin-x64 (Intel Mac) support

- Published ruvector-core-darwin-x64@0.1.25 with native binary built on macos-14
- Updated ruvector-core to 0.1.26 with darwin-x64 in optionalDependencies
- Updated ruvector to 0.1.33

CI runner change: Switched darwin-x64 builds from macos-12 to macos-14 for better availability.

* fix(postgres): Remove unimplemented GNN functions from SQL schema

- Removed 3 unimplemented functions: ruvector_gat_forward, ruvector_message_aggregate, ruvector_gnn_readout
- Updated Dockerfile to use pre-built SQL file instead of cargo pgrx schema (which doesn't work reliably in Docker)
- SQL function count: 92 → 89 (matching actual library exports)
- Extension now loads successfully in PostgreSQL 17 with avx2 SIMD support
- Docker image: ruvnet/ruvector-postgres:0.2.4 (477MB)

Fixes SQL/library function symbol mismatch that caused "could not find function" errors during extension loading.

* feat(postgres): Add HNSW index and embedding functions (v0.2.6)

- Added HNSW access method handler and operator classes
- Added 10 embedding generation functions (ruvector_embed, etc.)
- Removed IVFFlat references (not yet implemented)
- Updated SQL schema from 89 to 100 functions
- Fixed 'could not find function' errors on extension load

Fixes: HNSW index support, embedding generation availability

* chore: Update Cargo.lock and documentation

---------

Co-authored-by: Claude <noreply@anthropic.com>
2025-12-09 11:14:52 -05:00
..
advanced_features_integration.rs fix: Fix PQ integration test failures and add v0.1.18 release 2025-11-30 20:45:43 +00:00
concurrent_tests.rs fix: Resolve test compilation errors with VectorId type and imports 2025-11-26 17:27:57 +00:00
embeddings_test.rs feat(postgres): Add HNSW index and embedding functions support (#62) 2025-12-09 11:14:52 -05:00
hnsw_integration_test.rs feat: SONA Neural Architecture, RuvLLM, npm packages v0.1.31, and path traversal fix (#51) 2025-12-03 18:40:25 -05:00
integration_tests.rs fix: Resolve test compilation errors with VectorId type and imports 2025-11-26 17:27:57 +00:00
property_tests.rs Test and validate core functionality (#54) 2025-12-06 09:36:47 -05:00
README.md feat: Complete ALL Ruvector phases - production-ready vector database 2025-11-19 14:37:21 +00:00
stress_tests.rs fix: Resolve test compilation errors with VectorId type and imports 2025-11-26 17:27:57 +00:00
unit_tests.rs fix: Resolve CI build failures 2025-11-26 15:25:47 +00:00

Ruvector Core Test Suite

Overview

This directory contains a comprehensive Test-Driven Development (TDD) test suite following the London School approach. The test suite covers unit tests, integration tests, property-based tests, stress tests, and concurrent access tests.

Test Files

1. unit_tests.rs - Unit Tests with Mocking (London School)

Comprehensive unit tests using mockall for mocking dependencies:

  • Distance Metric Tests: Tests for all 4 distance metrics (Euclidean, Cosine, Dot Product, Manhattan)

    • Self-distance verification
    • Symmetry properties
    • Orthogonal and parallel vector cases
    • Dimension mismatch error handling
  • Quantization Tests: Tests for scalar and binary quantization

    • Round-trip reconstruction accuracy
    • Distance calculation correctness
    • Sign preservation (binary quantization)
    • Hamming distance validation
  • Storage Layer Tests: Tests for VectorStorage

    • Insert with explicit and auto-generated IDs
    • Metadata handling
    • Dimension validation
    • Batch operations
    • Delete operations
    • Error cases (non-existent vectors, dimension mismatches)
  • VectorDB Tests: High-level API tests

    • Empty database operations
    • Insert/delete with len() tracking
    • Search functionality
    • Metadata filtering
    • Batch insert operations

2. integration_tests.rs - End-to-End Integration Tests

Full workflow tests that verify all components work together:

  • Complete Workflows: Insert + search + retrieve with metadata
  • Large Batch Operations: 10K+ vector batch insertions
  • Persistence: Database save and reload verification
  • Mixed Operations: Combined insert, delete, and search operations
  • Distance Metrics: Tests for all 4 metrics end-to-end
  • HNSW Configurations: Different HNSW parameter combinations
  • Metadata Filtering: Complex filtering scenarios
  • Error Handling: Dimension validation, wrong query dimensions

3. property_tests.rs - Property-Based Tests (proptest)

Mathematical property verification using proptest:

  • Distance Metric Properties:

    • Self-distance is zero
    • Symmetry: d(a,b) = d(b,a)
    • Triangle inequality: d(a,c) ≤ d(a,b) + d(b,c)
    • Non-negativity
    • Scale invariance (cosine)
    • Translation invariance (Euclidean)
  • Quantization Properties:

    • Round-trip reconstruction bounds
    • Sign preservation (binary)
    • Self-distance is zero
    • Symmetry
    • Distance bounds
  • Batch Operations:

    • Consistency between batch and individual operations
  • Dimension Handling:

    • Mismatch error detection
    • Success on matching dimensions

4. stress_tests.rs - Scalability and Performance Stress Tests

Tests that push the system to its limits:

  • Million Vector Insertion (ignored by default): Insert 1M vectors in batches
  • Concurrent Queries: 10 threads × 100 queries each
  • Concurrent Mixed Operations: Simultaneous readers and writers
  • Memory Pressure: Large 2048-dimensional vectors
  • Error Recovery: Invalid operations don't crash the system
  • Repeated Operations: Same operation executed many times
  • Extreme Parameters: k values larger than database size

5. concurrent_tests.rs - Thread-Safety Tests

Multi-threaded access patterns:

  • Concurrent Reads: Multiple threads reading simultaneously
  • Concurrent Writes: Non-overlapping writes from multiple threads
  • Mixed Read/Write: Concurrent reads and writes
  • Delete and Insert: Simultaneous deletes and inserts
  • Search and Insert: Searching while inserting
  • Batch Atomicity: Verifying batch operations are atomic
  • Read-Write Consistency: Ensuring no data corruption
  • Metadata Updates: Concurrent metadata modifications

Benchmarks

6. benches/quantization_bench.rs - Quantization Performance

Criterion benchmarks for quantization operations:

  • Scalar quantization encode/decode/distance
  • Binary quantization encode/decode/distance
  • Compression ratio comparisons

7. benches/batch_operations.rs - Batch Operation Performance

Criterion benchmarks for batch operations:

  • Batch insert at various scales (100, 1K, 10K)
  • Individual vs batch insert comparison
  • Parallel search performance
  • Batch delete operations

Running Tests

Run All Tests

cargo test --package ruvector-core

Run Specific Test Suites

# Unit tests only
cargo test --test unit_tests

# Integration tests only
cargo test --test integration_tests

# Property tests only
cargo test --test property_tests

# Concurrent tests only
cargo test --test concurrent_tests

# Stress tests (including ignored tests)
cargo test --test stress_tests -- --ignored --test-threads=1

Run Benchmarks

# Distance metrics (existing)
cargo bench --bench distance_metrics

# HNSW search (existing)
cargo bench --bench hnsw_search

# Quantization (new)
cargo bench --bench quantization_bench

# Batch operations (new)
cargo bench --bench batch_operations

Generate Coverage Report

# Install tarpaulin if not already installed
cargo install cargo-tarpaulin

# Generate HTML coverage report
cargo tarpaulin --out Html --output-dir target/coverage

# Open coverage report
open target/coverage/index.html

Test Coverage Goals

  • Target: 90%+ code coverage
  • Focus Areas:
    • Distance calculations
    • Index operations
    • Storage layer
    • Error handling paths
    • Edge cases

Known Issues

As of the current implementation, there are pre-existing compilation errors in the codebase that prevent some tests from running:

  1. HNSW Index: DataId::new construction issues in src/index/hnsw.rs
  2. AgenticDB: Missing Encode/Decode trait implementations for ReflexionEpisode

These issues exist in the main codebase and need to be fixed before the full test suite can execute.

Test Organization

Tests are organized by purpose and scope:

  1. Unit Tests: Test individual components in isolation with mocking
  2. Integration Tests: Test component interactions and workflows
  3. Property Tests: Test mathematical properties and invariants
  4. Stress Tests: Test performance limits and edge cases
  5. Concurrent Tests: Test thread-safety and concurrent access patterns

Dependencies

Test dependencies (in Cargo.toml):

[dev-dependencies]
criterion = { workspace = true }
proptest = { workspace = true }
mockall = { workspace = true }
tempfile = "3.13"
tracing-subscriber = { workspace = true }

Contributing

When adding new tests:

  1. Follow the existing structure and naming conventions
  2. Add tests to the appropriate file (unit, integration, property, etc.)
  3. Document the test purpose clearly
  4. Ensure tests are deterministic and don't depend on timing
  5. Use tempdir() for database paths in tests
  6. Clean up resources properly

CI/CD Integration

Recommended CI pipeline:

test:
  script:
    - cargo test --all-features
    - cargo tarpaulin --out Xml
  coverage: '/\d+\.\d+% coverage/'

bench:
  script:
    - cargo bench --no-run

Performance Expectations

Based on stress tests:

  • Insert: ~10K vectors/second (batch mode)
  • Search: ~1K queries/second (k=10, HNSW)
  • Concurrent: 10+ threads without performance degradation
  • Memory: ~4KB per 384-dim vector (uncompressed)

Additional Resources