feat(rag): Pure-Rust Vector Store & RAG Pipeline (DIR-24)

[bkataru]

feat(rag): Pure-Rust Vector Store & RAG Pipeline (DIR-24)

Summary

Implements a complete RAG (Retrieval Augmented Generation) system with a pure-Rust vector database, eliminating external service dependencies. This release brings A.R.E.S to v0.3.0.

New Crate: ares-vector

• Pure-Rust embedded vector database with HNSW indexing
• Memory-mapped persistence via memmap2
• Multiple distance metrics (Cosine, Euclidean, Dot Product)
• No external dependencies (Qdrant/Milvus not required)
• Published to crates.io as ares-vector@0.1.1

RAG Pipeline

• Embeddings: Multi-model support (BGE, MiniLM, Nomic, Qwen3, GTE) via FastEmbed/Candle
• Chunking: Word, character, and semantic strategies with configurable overlap
• Search: Semantic, BM25, fuzzy, and hybrid strategies
• Reranking: Cross-encoder reranking (MiniLM-L6-v2, BGE)

New API Endpoints

Endpoint	Method	Description
/api/rag/ingest	POST	Document ingestion with chunking
/api/rag/search	POST	Multi-strategy search
/api/rag/collections	GET	List collections
/api/rag/collections/{name}	DELETE	Delete collection

Configuration

New [rag] section in ares.toml with full configuration options.

Breaking Changes

None - all new functionality is additive behind the ares-vector feature flag.

Documentation

• Updated README with RAG section and feature flags
• Added docs/DIR-24_RAG_IMPLEMENTATION_PLAN.md
• Added docs/FUTURE_ENHANCEMENTS.md for deferred features
• Updated PROJECT_STATUS.md with Iteration 5

Test Coverage

• 130 library tests passing
• CI updated to test ares-vector on all platforms (Ubuntu, Windows, macOS)

What's Deferred

• AI-native protocols (ACP, AG-UI, ANP, A2A) - pending standardization
• GPU acceleration for embeddings
• Embedding cache

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Closes DIR-24

[Copilot]

Pull request overview

This PR implements a comprehensive RAG (Retrieval Augmented Generation) system with a pure-Rust vector database, bringing A.R.E.S to v0.3.0. The implementation adds local-first vector storage with HNSW indexing, multi-model embedding support, multiple search strategies, and reranking capabilities—all without requiring external service dependencies.

Changes:

• Pure-Rust ares-vector crate with HNSW indexing and memory-mapped persistence
• Multi-strategy search engine (semantic, BM25, fuzzy, hybrid with RRF fusion)
• Comprehensive embedding service supporting 38+ models via FastEmbed/Candle
• Document chunking with word/semantic/character strategies
• Cross-encoder reranking for improved relevance
• New RAG API endpoints for ingestion, search, and collection management
• 130+ new tests including live integration tests with real models

Reviewed changes

Copilot reviewed 33 out of 34 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
tests/rag_live_tests.rs	Live integration tests with real embedding models (ignored by default)
src/utils/toml_config.rs	Expanded RAG configuration with hybrid weights and reranking options
src/types/mod.rs	New RAG API request/response types with OpenAPI schemas
src/rag/search.rs	Multi-strategy search engine (BM25, fuzzy, hybrid with RRF)
src/rag/reranker.rs	Cross-encoder reranking service with 4 model options
src/rag/embeddings.rs	Comprehensive embedding service with 38+ models
src/rag/chunker.rs	Text chunking with semantic boundary awareness
src/db/vectorstore.rs	Abstract vector store trait with multi-provider support
src/db/lancedb.rs	LanceDB vector store implementation (888 lines)
src/db/ares_vector.rs	Pure-Rust vector store using ares-vector crate
src/api/routes.rs	New RAG API routes for ingestion and search
src/api/handlers/rag.rs	RAG endpoint handlers with global service initialization
docs/PROJECT_STATUS.md	Updated with Iteration 5 completion summary
docs/FUTURE_ENHANCEMENTS.md	Documented deferred features (GPU, caching, protocols)
docs/DIR-24_RAG_IMPLEMENTATION_PLAN.md	Comprehensive implementation plan with research findings
crates/ares-vector/*	Pure-Rust vector database crate (separate package)

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