diff --git a/.kiro/specs/competitive-features-phase3/design.md b/.kiro/specs/competitive-features-phase3/design.md deleted file mode 100644 index 4568c10..0000000 --- a/.kiro/specs/competitive-features-phase3/design.md +++ /dev/null @@ -1,412 +0,0 @@ -# Design Document - -## Overview - -This design implements Phase 3 competitive features for LibraVDB, transforming it from a solid foundation into a production-ready vector database that can compete with Pinecone, Qdrant, and Weaviate. The design focuses on five core areas: vector quantization for memory efficiency, advanced metadata filtering, high-performance batch operations, multiple index algorithms, and comprehensive memory management. - -The architecture maintains LibraVDB's existing strengths (embedded Go library, HNSW performance, persistence) while adding enterprise-grade capabilities. All new features integrate seamlessly with the current layered architecture: `libravdb` (public API) → `internal/index` (algorithms) → `internal/storage` (persistence) → `internal/obs` (observability). - -## Architecture - -### Current Architecture Enhancement - -The design extends the existing architecture with new components: - -``` -libravdb/ -├── collection.go # Enhanced with batch ops, quantization config -├── options.go # New quantization, memory, index options -├── query.go # Enhanced metadata filtering -├── batch.go # NEW: Batch operations API -└── memory.go # NEW: Memory management API - -internal/ -├── quant/ # NEW: Quantization implementations -│ ├── interfaces.go # Quantizer interface -│ ├── product.go # Product Quantization (PQ) -│ ├── scalar.go # Scalar Quantization -│ └── registry.go # Quantizer factory -├── index/ -│ ├── hnsw/ # Existing HNSW (enhanced) -│ ├── ivfpq/ # NEW: IVF-PQ index -│ ├── flat/ # NEW: Flat/brute-force index -│ └── interfaces.go # Enhanced with quantization support -├── memory/ # NEW: Memory management -│ ├── manager.go # Memory limit enforcement -│ ├── cache.go # LRU cache implementation -│ └── monitor.go # Memory usage monitoring -└── filter/ # NEW: Metadata filtering engine - ├── parser.go # Query filter parsing - ├── executor.go # Filter execution - └── optimizer.go # Filter optimization -``` - -### Component Interactions - -```mermaid -graph TB - A[Collection API] --> B[Batch Operations] - A --> C[Query Builder] - A --> D[Index Manager] - - B --> E[Memory Manager] - C --> F[Filter Engine] - D --> G[Quantizer] - D --> H[Index Registry] - - H --> I[HNSW Index] - H --> J[IVF-PQ Index] - H --> K[Flat Index] - - G --> L[Product Quantization] - G --> M[Scalar Quantization] - - E --> N[LRU Cache] - E --> O[Memory Monitor] - - F --> P[Filter Parser] - F --> Q[Filter Executor] -``` - -## Components and Interfaces - -### 1. Vector Quantization System - -#### Quantizer Interface -```go -type Quantizer interface { - // Training and configuration - Train(ctx context.Context, vectors [][]float32) error - Configure(config QuantizationConfig) error - - // Compression and decompression - Compress(vector []float32) ([]byte, error) - Decompress(data []byte) ([]float32, error) - - // Distance computation on compressed vectors - Distance(compressed1, compressed2 []byte) (float32, error) - DistanceToQuery(compressed []byte, query []float32) (float32, error) - - // Metadata - CompressionRatio() float32 - MemoryUsage() int64 -} - -type QuantizationConfig struct { - Type QuantizationType - Codebooks int // For PQ - Bits int // Bits per component - TrainRatio float64 // Training data ratio - CacheSize int // Codebook cache size -} -``` - -#### Product Quantization Implementation -- **Training**: K-means clustering on vector subspaces to build codebooks -- **Compression**: Map each subvector to nearest centroid ID -- **Distance**: Precomputed distance tables for fast similarity computation -- **Memory**: 8x-32x compression ratio depending on configuration - -#### Scalar Quantization Implementation -- **Training**: Compute min/max ranges per dimension -- **Compression**: Linear quantization to fixed-point representation -- **Distance**: Direct computation on quantized values -- **Memory**: 4x-8x compression ratio with minimal accuracy loss - -### 2. Advanced Metadata Filtering - -#### Query Builder Enhancement -```go -type QueryBuilder struct { - ctx context.Context - collection *Collection - vector []float32 - filters []Filter - limit int - offset int -} - -type Filter interface { - Apply(ctx context.Context, entries []*VectorEntry) ([]*VectorEntry, error) - Optimize() Filter - EstimateSelectivity() float64 -} - -// Filter implementations -type EqualityFilter struct { - Field string - Value interface{} -} - -type RangeFilter struct { - Field string - Min interface{} - Max interface{} -} - -type ContainsFilter struct { - Field string - Values []interface{} -} -``` - -#### Filter Execution Engine -- **Pre-filtering**: Apply metadata filters before vector search when selective -- **Post-filtering**: Apply filters after vector search when less selective -- **Index Integration**: Bitmap indices for high-cardinality metadata fields -- **Optimization**: Cost-based query planning for multiple filters - -### 3. Batch Operations System - -#### Batch API Design -```go -type BatchOperation interface { - Execute(ctx context.Context) (*BatchResult, error) - Size() int - EstimateMemory() int64 -} - -type BatchInsert struct { - entries []*VectorEntry - options BatchOptions -} - -type BatchUpdate struct { - updates []*VectorUpdate - options BatchOptions -} - -type BatchDelete struct { - ids []string - filter Filter // Alternative to IDs - options BatchOptions -} - -type BatchResult struct { - Successful int - Failed int - Errors map[int]error // Index -> Error mapping - Duration time.Duration -} -``` - -#### Batch Processing Strategy -- **Chunking**: Process large batches in configurable chunks (default 1000) -- **Parallelization**: Concurrent processing with worker pools -- **Memory Management**: Streaming processing for memory-constrained environments -- **Atomicity**: Transaction-like semantics with rollback on failure -- **Progress Tracking**: Real-time progress reporting for long operations - -### 4. Multiple Index Types - -#### Index Registry Enhancement -```go -type IndexFactory interface { - Create(config IndexConfig) (Index, error) - Supports(indexType IndexType) bool -} - -type IndexConfig struct { - Type IndexType - Dimension int - Metric DistanceMetric - Parameters map[string]interface{} - Quantizer Quantizer // Optional quantization -} -``` - -#### IVF-PQ Index Implementation -- **Structure**: Inverted file with product quantization -- **Training**: K-means clustering for coarse quantization + PQ for fine quantization -- **Search**: Probe multiple clusters, compute distances on PQ codes -- **Memory**: Excellent for large-scale datasets (10M+ vectors) -- **Performance**: Sub-linear search complexity - -#### Flat Index Implementation -- **Structure**: Simple linear array of vectors -- **Search**: Brute-force exact search -- **Use Cases**: Small collections (<10K vectors), exact search requirements -- **Performance**: O(n) search but excellent for small datasets - -### 5. Memory Management System - -#### Memory Manager Design -```go -type MemoryManager interface { - SetLimit(bytes int64) error - GetUsage() MemoryUsage - TriggerGC() error - RegisterCache(cache Cache) error - - // Event-driven memory management - OnMemoryPressure(callback func(usage MemoryUsage)) - OnMemoryRelease(callback func(freed int64)) -} - -type MemoryUsage struct { - Total int64 - Indices int64 - Caches int64 - Quantized int64 - Available int64 -} - -type Cache interface { - Evict(bytes int64) int64 - Size() int64 - Clear() -} -``` - -#### Memory Management Strategies -- **Proactive Monitoring**: Continuous memory usage tracking -- **Tiered Eviction**: LRU eviction for caches, quantization for indices -- **Memory Mapping**: mmap for large indices to reduce RAM pressure -- **Graceful Degradation**: Automatic quantization under memory pressure - -## Data Models - -### Enhanced Collection Configuration -```go -type CollectionConfig struct { - // Existing fields - Dimension int - Metric DistanceMetric - IndexType IndexType - - // NEW: Quantization configuration - Quantization *QuantizationConfig - - // NEW: Memory management - MemoryLimit int64 - CachePolicy CachePolicy - EnableMMapping bool - - // NEW: Batch processing - BatchSize int - MaxConcurrency int - - // NEW: Filtering - MetadataSchema map[string]FieldType - IndexedFields []string -} - -type FieldType int -const ( - StringField FieldType = iota - IntField - FloatField - BoolField - TimeField - StringArrayField -) -``` - -### Batch Operation Models -```go -type VectorUpdate struct { - ID string - Vector []float32 // Optional: update vector - Metadata map[string]interface{} // Optional: update metadata - Upsert bool // Create if not exists -} - -type BatchOptions struct { - ChunkSize int - MaxConcurrency int - FailFast bool - ProgressCallback func(completed, total int) -} -``` - -## Error Handling - -### Quantization Errors -- **Training Failures**: Insufficient training data, convergence issues -- **Compression Errors**: Invalid vector dimensions, quantization overflow -- **Recovery Strategy**: Fallback to uncompressed storage, user notification - -### Memory Management Errors -- **Out of Memory**: Graceful degradation with quantization and eviction -- **Cache Failures**: Continue operation without caching -- **Recovery Strategy**: Automatic memory pressure relief, user alerts - -### Batch Operation Errors -- **Partial Failures**: Detailed error reporting per item -- **Transaction Rollback**: Atomic batch processing with rollback capability -- **Recovery Strategy**: Retry mechanisms, checkpoint-based recovery - -### Filter Execution Errors -- **Invalid Filters**: Schema validation and type checking -- **Performance Degradation**: Automatic filter optimization and warnings -- **Recovery Strategy**: Fallback to post-filtering, query plan adjustment - -## Testing Strategy - -### Unit Testing -- **Quantization Accuracy**: Compression ratio vs. accuracy trade-offs -- **Filter Correctness**: All filter types with edge cases -- **Batch Operations**: Atomicity, error handling, performance -- **Memory Management**: Limit enforcement, eviction policies - -### Integration Testing -- **End-to-End Workflows**: Complete batch insert → quantize → search pipelines -- **Cross-Component**: Quantized indices with metadata filtering -- **Performance Regression**: Benchmark suite for all new features - -### Performance Testing -- **Scalability**: 1M+ vector collections with various configurations -- **Memory Efficiency**: Quantization memory savings validation -- **Batch Throughput**: Concurrent batch operations under load -- **Query Performance**: Complex filtered queries with large result sets - -### Compatibility Testing -- **Backward Compatibility**: Existing LibraVDB applications continue working -- **Migration Testing**: Upgrading existing collections to new features -- **Cross-Platform**: macOS, Linux, Windows compatibility validation - -## Implementation Phases - -### Phase 3A: Foundation (Week 1-2) -1. **Quantization Infrastructure** - - Implement quantizer interfaces and registry - - Product Quantization with basic codebook training - - Integration with existing HNSW index - -2. **Memory Management Core** - - Memory usage monitoring and limits - - Basic LRU cache implementation - - Integration with collection lifecycle - -### Phase 3B: Core Features (Week 3-4) -1. **Advanced Filtering** - - Query builder enhancement with filter support - - Filter execution engine with optimization - - Metadata schema validation - -2. **Batch Operations** - - Batch insert/update/delete APIs - - Chunked processing with concurrency control - - Error handling and progress reporting - -### Phase 3C: Index Expansion (Week 5-6) -1. **IVF-PQ Index** - - Cluster-based coarse quantization - - Integration with product quantization - - Search algorithm implementation - -2. **Flat Index** - - Brute-force exact search implementation - - Memory-efficient storage for small collections - - Integration with quantization system - -### Phase 3D: Polish & Optimization (Week 7-8) -1. **Performance Optimization** - - Query plan optimization for filtered searches - - Memory management fine-tuning - - Batch operation performance improvements - -2. **Production Readiness** - - Comprehensive error handling - - Monitoring and observability enhancements - - Documentation and examples \ No newline at end of file diff --git a/.kiro/specs/competitive-features-phase3/requirements.md b/.kiro/specs/competitive-features-phase3/requirements.md deleted file mode 100644 index d60bd75..0000000 --- a/.kiro/specs/competitive-features-phase3/requirements.md +++ /dev/null @@ -1,98 +0,0 @@ -# Requirements Document - -## Introduction - -This feature implements the core competitive capabilities needed to make LibraVDB competitive with production vector databases like Pinecone, Qdrant, Weaviate, and Milvus. Phase 3 focuses on the highest ROI features: vector quantization for memory efficiency, advanced metadata filtering, batch operations for performance, multiple index types, and comprehensive memory management. These features will position LibraVDB as a viable alternative to existing vector database solutions while maintaining its simplicity as a Go library. - -## Requirements - -### Requirement 1: Vector Quantization Support - -**User Story:** As a developer using LibraVDB, I want vector quantization capabilities so that I can reduce memory usage and improve performance for large-scale vector collections. - -#### Acceptance Criteria - -1. WHEN configuring a collection THEN the system SHALL support Product Quantization (PQ) with configurable codebooks and bits per component -2. WHEN configuring a collection THEN the system SHALL support Scalar Quantization with configurable bit precision -3. WHEN creating a collection with quantization THEN the system SHALL automatically train quantization parameters using a configurable ratio of training data -4. WHEN inserting vectors into a quantized collection THEN the system SHALL compress vectors according to the quantization configuration -5. WHEN searching in a quantized collection THEN the system SHALL maintain search accuracy within acceptable bounds while using compressed representations -6. WHEN quantization is enabled THEN the system SHALL reduce memory usage by at least 50% compared to full-precision vectors - -### Requirement 2: Advanced Metadata Filtering - -**User Story:** As a developer building applications with LibraVDB, I want sophisticated metadata filtering capabilities so that I can perform complex queries combining vector similarity with structured data constraints. - -#### Acceptance Criteria - -1. WHEN building a query THEN the system SHALL support equality filters on string, numeric, and boolean metadata fields -2. WHEN building a query THEN the system SHALL support range filters (greater than, less than, between) on numeric and date fields -3. WHEN building a query THEN the system SHALL support array containment filters for multi-valued metadata fields -4. WHEN building a query THEN the system SHALL support combining multiple filters with AND/OR logic -5. WHEN executing filtered queries THEN the system SHALL apply metadata filters before vector similarity computation for performance -6. WHEN metadata filtering is used THEN the system SHALL maintain sub-100ms query latency for collections up to 1M vectors - -### Requirement 3: Batch Operations - -**User Story:** As a developer ingesting large amounts of data into LibraVDB, I want efficient batch operations so that I can achieve high throughput for bulk data operations. - -#### Acceptance Criteria - -1. WHEN performing bulk inserts THEN the system SHALL support batch insertion of up to 10,000 vectors in a single operation -2. WHEN performing bulk updates THEN the system SHALL support batch updates of vector data and metadata -3. WHEN performing bulk deletes THEN the system SHALL support batch deletion by ID or metadata criteria -4. WHEN batch operations are used THEN the system SHALL achieve at least 10x throughput improvement over individual operations -5. WHEN batch operations fail THEN the system SHALL provide detailed error information for each failed item -6. WHEN batch operations are performed THEN the system SHALL maintain ACID properties and data consistency - -### Requirement 4: Multiple Index Types - -**User Story:** As a developer optimizing LibraVDB for different use cases, I want multiple index algorithms so that I can choose the best performance characteristics for my specific workload. - -#### Acceptance Criteria - -1. WHEN creating a collection THEN the system SHALL support HNSW index configuration (existing) -2. WHEN creating a collection THEN the system SHALL support IVF-PQ (Inverted File with Product Quantization) index for large-scale scenarios -3. WHEN creating a collection THEN the system SHALL support Flat index for exact search and small collections -4. WHEN configuring IVF-PQ THEN the system SHALL allow specification of cluster count and probe parameters -5. WHEN using different index types THEN the system SHALL automatically select optimal search algorithms for each index type -6. WHEN switching index types THEN the system SHALL support index rebuilding without data loss - -### Requirement 5: Memory Management and Limits - -**User Story:** As a developer deploying LibraVDB in production, I want comprehensive memory management controls so that I can prevent out-of-memory conditions and optimize resource usage. - -#### Acceptance Criteria - -1. WHEN configuring LibraVDB THEN the system SHALL support setting global memory limits -2. WHEN memory usage approaches limits THEN the system SHALL implement LRU cache eviction policies -3. WHEN configured THEN the system SHALL support memory mapping for large indices to reduce RAM usage -4. WHEN memory limits are exceeded THEN the system SHALL gracefully handle the condition without crashing -5. WHEN requested THEN the system SHALL provide runtime memory usage statistics and controls -6. WHEN memory pressure occurs THEN the system SHALL trigger garbage collection and cache cleanup automatically - -### Requirement 6: Performance Benchmarking - -**User Story:** As a developer evaluating LibraVDB, I want comprehensive performance benchmarks so that I can compare it against other vector database solutions. - -#### Acceptance Criteria - -1. WHEN running benchmarks THEN the system SHALL demonstrate competitive query latency compared to Pinecone, Qdrant, and Weaviate -2. WHEN running benchmarks THEN the system SHALL demonstrate competitive throughput for batch operations -3. WHEN running benchmarks THEN the system SHALL demonstrate memory efficiency improvements with quantization -4. WHEN running benchmarks THEN the system SHALL maintain accuracy metrics (recall@k) within 5% of full-precision results -5. WHEN benchmarking different index types THEN the system SHALL show appropriate performance characteristics for each use case -6. WHEN under load testing THEN the system SHALL maintain stable performance without memory leaks or degradation - -### Requirement 7: API Compatibility and Ergonomics - -**User Story:** As a Go developer, I want intuitive and idiomatic APIs so that LibraVDB integrates seamlessly into my applications with minimal learning curve. - -#### Acceptance Criteria - -1. WHEN using the API THEN the system SHALL provide fluent query builder interfaces for complex operations -2. WHEN configuring collections THEN the system SHALL use functional options pattern for clean configuration -3. WHEN handling errors THEN the system SHALL provide detailed, actionable error messages -4. WHEN using batch operations THEN the system SHALL provide streaming interfaces for large datasets -5. WHEN integrating with applications THEN the system SHALL support context-based cancellation and timeouts -6. WHEN using the library THEN the system SHALL maintain backward compatibility with existing LibraVDB APIs \ No newline at end of file diff --git a/.kiro/specs/competitive-features-phase3/tasks.md b/.kiro/specs/competitive-features-phase3/tasks.md deleted file mode 100644 index a132ae5..0000000 --- a/.kiro/specs/competitive-features-phase3/tasks.md +++ /dev/null @@ -1,140 +0,0 @@ -# Implementation Plan - -- [x] 1. Set up quantization infrastructure foundation - - Create quantization interfaces and base types in `internal/quant/interfaces.go` - - Implement quantization registry pattern for factory creation - - Add quantization configuration types and validation - - _Requirements: 1.1, 1.2, 1.3_ - -- [x] 2. Implement Product Quantization (PQ) core algorithm - - Create PQ implementation with k-means clustering for codebook training - - Implement vector compression and decompression methods - - Add distance computation on compressed vectors with lookup tables - - Write comprehensive unit tests for PQ accuracy and performance - - _Requirements: 1.1, 1.4, 1.5, 1.6_ - -- [x] 3. Implement Scalar Quantization algorithm - - Create scalar quantization with min/max range computation - - Implement linear quantization to fixed-point representation - - Add direct distance computation on quantized values - - Write unit tests comparing accuracy vs compression ratio - - _Requirements: 1.2, 1.4, 1.5, 1.6_ - -- [x] 4. Integrate quantization with existing HNSW index - - Modify HNSW index to support optional quantization during insertion - - Update search algorithms to work with compressed vectors - - Add quantization training during index building phase - - Write integration tests for quantized HNSW performance - - _Requirements: 1.3, 1.4, 1.5, 1.6_ - -- [x] 5. Create memory management infrastructure - - Implement memory usage monitoring and tracking system - - Create memory limit enforcement with configurable thresholds - - Add LRU cache interface and basic implementation - - Write unit tests for memory limit enforcement - - _Requirements: 5.1, 5.2, 5.5_ - -- [x] 6. Implement memory mapping support for large indices - - Add memory mapping option for HNSW index storage - - Implement automatic mmap activation based on index size - - Create memory pressure detection and response system - - Write tests for mmap functionality and memory usage reduction - - _Requirements: 5.3, 5.4, 5.6_ - -- [x] 7. Build metadata filtering query engine foundation - - Create filter interface hierarchy for different filter types - - Implement equality, range, and containment filter classes - - Add filter parsing and validation logic - - Write unit tests for all filter types with edge cases - - _Requirements: 2.1, 2.2, 2.3_ - -- [x] 8. Enhance QueryBuilder with advanced filtering capabilities - - Extend QueryBuilder to support chained filter operations - - Implement AND/OR logic combination for multiple filters - - Add filter optimization and selectivity estimation - - Write integration tests for complex filter combinations - - _Requirements: 2.4, 2.5, 2.6_ - -- [x] 9. Implement batch operations API and infrastructure - - Create batch operation interfaces for insert/update/delete - - Implement chunked processing with configurable batch sizes - - Add concurrent processing with worker pool management - - Write unit tests for batch operation correctness and atomicity - - _Requirements: 3.1, 3.2, 3.3, 3.4, 3.6_ - -- [x] 10. Add batch operation error handling and progress tracking - - Implement detailed error reporting for failed batch items - - Add progress callback system for long-running operations - - Create rollback mechanisms for failed batch transactions - - Write tests for error scenarios and recovery behavior - - _Requirements: 3.5, 3.6_ - -- [x] 11. Create IVF-PQ index implementation foundation - - Implement inverted file structure with cluster management - - Add k-means clustering for coarse quantization training - - Create cluster assignment and search probe logic - - Write unit tests for cluster creation and assignment accuracy - - _Requirements: 4.2, 4.4, 4.5_ - -- [x] 12. Integrate IVF-PQ with product quantization system - - Combine coarse clustering with fine PQ compression - - Implement multi-probe search across cluster candidates - - Add automatic parameter tuning for cluster count and probes - - Write performance tests comparing IVF-PQ vs HNSW on large datasets - - _Requirements: 4.2, 4.4, 4.5, 4.6_ - -- [x] 13. Implement Flat index for exact search scenarios - - Create simple linear array storage for vectors - - Implement brute-force exact search with all distance metrics - - Add automatic index type selection based on collection size - - Write tests for exact search accuracy and small collection performance - - _Requirements: 4.3, 4.5, 4.6_ - -- [x] 14. Enhance collection configuration with new options - - Add quantization configuration options to CollectionConfig - - Implement memory management settings and validation - - Create metadata schema definition and field type validation - - Write tests for configuration validation and backward compatibility - - _Requirements: 1.1, 1.2, 5.1, 2.1, 7.1, 7.2_ - -- [x] 15. Update collection creation and management APIs - - Modify collection creation to support new configuration options - - Add runtime memory management controls and statistics - - Implement collection optimization and rebuilding capabilities - - Write integration tests for enhanced collection lifecycle - - _Requirements: 4.6, 5.5, 7.3, 7.4_ - -- [ ] 16. Create comprehensive performance benchmarking suite - - Implement benchmarks comparing quantized vs full-precision performance - - Add memory usage benchmarks for different quantization settings - - Create batch operation throughput benchmarks - - Write comparative benchmarks against other vector databases - - _Requirements: 6.1, 6.2, 6.3, 6.4, 6.5, 6.6_ - -- [x] 17. Add streaming interfaces for large dataset processing - - Create streaming batch insert API for memory-efficient ingestion - - Implement backpressure handling for streaming operations - - Add context-based cancellation and timeout support - - Write tests for streaming performance and memory usage - - _Requirements: 7.4, 7.5_ - -- [ ] 18. Implement advanced error handling and recovery - - Add detailed error types for all new failure modes - - Implement graceful degradation under memory pressure - - Create automatic recovery mechanisms for quantization failures - - Write comprehensive error handling tests and documentation - - _Requirements: 7.3, 7.6_ - -- [ ] 19. Create integration tests for end-to-end workflows - - Test complete pipeline: batch insert → quantize → filtered search - - Verify cross-component interactions under various configurations - - Add stress tests for concurrent operations and memory limits - - Write migration tests for upgrading existing collections - - _Requirements: 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 7.6_ - -- [ ] 20. Finalize API ergonomics and backward compatibility - - Ensure all new APIs follow Go idioms and existing patterns - - Verify backward compatibility with existing LibraVDB applications - - Add comprehensive examples and documentation for new features - - Write final integration tests covering all competitive features - - _Requirements: 7.1, 7.2, 7.3, 7.4, 7.5, 7.6_ \ No newline at end of file