feat: add RAG (Retrieval-Augmented Generation) module #2
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- Add complete RAG module with vector and keyword search - Implement document indexing with configurable chunking strategies (paragraph, sentence, fixed, semantic) - Add embedding providers: local (transformers.js), OpenAI, Voyage, Cohere, custom - Implement hybrid search combining vector (70%) and keyword (30%) - Add SQLite storage with FTS5 for full-text search - Add 8 MCP tools: rag_index_document, rag_index_project, rag_search, rag_query_context, rag_list_documents, rag_delete_document, rag_get_stats, rag_configure - Add HTTP endpoints for all RAG operations - Add comprehensive test suite (chunking, embeddings, RAG) - Add RAG benchmarks for performance testing - Update README and API documentation
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🚀 Benchmark ResultsCortexFlow Benchmark Results
Last Run: 2025-12-28T17:01:18.330Z Summary
Performance ResultsStorage
Intelligent
Token Efficiency
Context Handoff
RAG Chunking
RAG Token Estimation
RAG Chunk Processing
Token Efficiency
Memory Usage
|
Contributor
🚀 Benchmark ResultsCortexFlow Benchmark Results
Last Run: 2025-12-28T17:01:42.863Z Summary
Performance ResultsStorage
Intelligent
Token Efficiency
Context Handoff
RAG Chunking
RAG Token Estimation
RAG Chunk Processing
Token Efficiency
Memory Usage
|
Contributor
🚀 Benchmark ResultsCortexFlow Benchmark Results
Last Run: 2025-12-28T17:06:01.885Z Summary
Performance ResultsStorage
Intelligent
Token Efficiency
Context Handoff
RAG Chunking
RAG Token Estimation
RAG Chunk Processing
Token Efficiency
Memory Usage
|
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