Module vector search Roadmap

Vector Search Module Roadmap

Current Status

Production-candidate vector search infrastructure providing approximate nearest neighbor (ANN) search over high-dimensional embeddings. The module integrates multiple indexing algorithms and supports efficient similarity queries for semantic search and retrieval augmented generation (RAG).

Milestone: Phase 4 deliverables complete. Core vector indexing and search implementation (HNSW, IVF algorithms) hardened and ready for production.

• Index data structures and algorithms (HNSW, IVF) (Phase 2) → COMPLETE
• Query execution engine (Phase 2) → COMPLETE
• Distance computation (cosine, L2, inner product) (Phase 2) → COMPLETE
• Indexing and rebuilding operations (Phase 3) → COMPLETE
• Error handling and edge cases (Phase 3) → COMPLETE

Completed Initiatives

Phase 1-4 Delivery (Q2-Q3 2026) - COMPLETE ✓

All vector indexing infrastructure implemented and validated. Module ready for production deployment.

Implementation Phases (Completed 2026-08-10)

Phase 1: Design & API Contract ✓ COMPLETE

Objective: Define vector index abstraction, query interface, and similarity semantics.

Deliverables:

• include/vector_search/vector_index.h – Index creation and query interface
• include/vector_search/similarity_search.h – Similarity query API
• include/vector_search/distance_metric.h – Distance function definitions
• Error taxonomy (vector search errors: E5400–E5499)

Index Contracts:

• Vector Index — Core abstraction for similarity search

  • add(vector, document_id) → Result<>
  • search(query_vector, k) → Result<KNearestNeighbors>
  • delete(document_id) → Result<>

• Distance Metrics — Supported similarity functions

  • Cosine distance (normalized embeddings)
  • L2 (Euclidean) distance
  • Inner product (dot product for cosine similarity)

Status: ✓ COMPLETE

Phase 2: Core Implementation ✓ COMPLETE

Objective: Implement vector indexing algorithms (HNSW, IVF) with efficient search.

Deliverables:

• vector_index.cpp – Index base implementation and lifecycle

  • Vector validation (dimension, range checks)
  • Index persistence and loading
  • Metadata management (document IDs, timestamps)

• HNSW (Hierarchical Navigable Small World) algorithm

  • Multi-layer graph structure for fast search
  • Configurable layer decay probability (default: 1/ln(2))
  • Insert, search, and delete operations

• IVF (Inverted File) algorithm

  • Coarse quantization with k-means centroids
  • Fine-grained search within selected clusters
  • Fast approximate search for large-scale indices

• Distance computation kernels

  • Optimized cosine similarity (SIMD where available)
  • L2 distance (batch computation)
  • Inner product (for normalized vectors)

Performance Targets:

• Index insertion: < 100 µs per vector
• Search latency (k=10): < 10 ms P99
• Search throughput: 100+ queries/sec
• Memory overhead: ~30% vs. raw vector storage

Status: ✓ COMPLETE

Phase 3: Error Handling & Edge Cases ✓ COMPLETE

Objective: Handle invalid queries, empty indices, and resource constraints.

Deliverables:

• Dimension mismatch detection and recovery
• Invalid vector handling (NaN, inf values)
• Empty index and no-results handling
• Index rebuilding and rebalancing
• Out-of-memory graceful degradation

Error Scenarios:

• E5400: Invalid vector dimension
• E5401: Vector contains NaN or inf
• E5402: Index is empty
• E5403: Search returned no results
• E5404: Index corruption detected

Status: ✓ COMPLETE

Phase 4: Tests ✓ COMPLETE

Objective: Comprehensive testing of indexing and search correctness.

Test Suite:

• Unit tests for distance computations
• HNSW insertion, search, and delete operations
• IVF clustering and search accuracy
• Correctness validation (nearest neighbors vs. brute force)
• Stress tests with large indices (1M+ vectors)

Test Coverage:

• src/vector_search coverage via focused test suites
• End-to-end indexing and retrieval workflows
• Performance benchmarks for latency and throughput

Status: ✓ COMPLETE

Phase 5: Performance & Hardening ✓ IN PROGRESS

Objective: Optimize search paths and validate production scaling.

Deliverables (In Progress):

• SIMD optimization for distance computation
• Memory-mapped index files for large-scale indices
• Query result caching for frequent searches
• Index tuning heuristics (HNSW M and ef parameters)
• Concurrent search scaling validation

Performance Gates:

• Search latency P99: < 10 ms (k=10)
• Insertion throughput: > 1000 vectors/sec
• Memory efficiency: < 40% overhead
• Concurrent queries: ≥ 100 with < 5% overhead

Status: IN PROGRESS

Phase 6: Documentation & Acceptance - PLANNED

Objective: Complete API documentation and operational guides.

Deliverables (Planned):

• Doxygen comments for all public APIs
• Algorithm selection guide (when to use HNSW vs. IVF)
• Index tuning parameter reference
• Query optimization best practices
• Troubleshooting runbook
• Acceptance checklist

Status: PLANNED

Production Readiness Checklist

• Phase 1 API contracts frozen
• Phase 2 core implementation complete
• Phase 3 error handling comprehensive
• Phase 4 test suite complete
• [~] Phase 5 performance hardening (in progress)
• Phase 6 documentation complete
• [~] Security review (in progress)
• Performance validation on production hardware
• Large-scale index loading and scaling tests
• Operational runbook completion

Known Issues & Limitations

1. No Incremental Index Updates — Full rebuild required for algorithm parameter changes
2. Fixed Dimension Vectors — Cannot mix different embedding dimensions
3. In-Memory Indices — No out-of-core support for very large indices (> available RAM)
4. No Distributed Indexing — Single-machine indices only

Breaking Changes

None expected. APIs designed for forward compatibility.

Module Statistics

• Total LOC (Source): ~800 LOC across implementation files
  • vector_index.cpp: ~200 LOC
  • hnsw_index.cpp: ~350 LOC
  • ivf_index.cpp: ~250 LOC
• Public Headers: 3 (vector_index.h, similarity_search.h, distance_metric.h)
• Distance Metrics: 3 (cosine, L2, inner product)
• Index Algorithms: 2 (HNSW, IVF)
• Error Codes: E5400–E5499 (reserved)

Program Execution Model — Wave Context

This module is a contributing module in the program-level Wave A → B → C → D execution model. It must remain release_critical-green throughout all waves.

See [[../../ROADMAP.md|ROADMAP]] for the full wave model and exit criteria.