BobAi
4166d0a362
🎯 Complete transformation from 5.9GB bloated system to 70MB optimized solution ✨ Key Features: - Hybrid embedding system (Ollama + ML fallback + hash backup) - Intelligent chunking with language-aware parsing - Semantic + BM25 hybrid search with rich context - Zero-config portable design with graceful degradation - Beautiful TUI for beginners + powerful CLI for experts - Comprehensive documentation with 8+ Mermaid diagrams - Professional animated demo (183KB optimized GIF) 🏗️ Architecture Highlights: - LanceDB vector storage with streaming indexing - Smart file tracking (size/mtime) to avoid expensive rehashing - Progressive chunking: Markdown headers → Python functions → fixed-size - Quality filtering: 200+ chars, 20+ words, 30% alphanumeric content - Concurrent batch processing with error recovery 📦 Package Contents: - Core engine: claude_rag/ (11 modules, 2,847 lines) - Entry points: rag-mini (unified), rag-tui (beginner interface) - Documentation: README + 6 guides with visual diagrams - Assets: 3D icon, optimized demo GIF, recording tools - Tests: 8 comprehensive integration and validation tests - Examples: Usage patterns, config templates, dependency analysis 🎥 Demo System: - Scripted demonstration showing 12 files → 58 chunks indexing - Semantic search with multi-line result previews - Complete workflow from TUI startup to CLI mastery - Professional recording pipeline with asciinema + GIF conversion 🛡️ Security & Quality: - Complete .gitignore with personal data protection - Dependency optimization (removed python-dotenv) - Code quality validation and educational test suite - Agent-reviewed architecture and documentation Ready for production use - copy folder, run ./rag-mini, start searching\!
22 lines
575 B
Python
22 lines
575 B
Python
"""
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FSS-Mini-RAG - Lightweight, portable semantic code search.
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A hybrid RAG system with Ollama-first embeddings, ML fallback, and streaming indexing.
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Designed for portability, efficiency, and simplicity across projects and computers.
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"""
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__version__ = "2.1.0"
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from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
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from .chunker import CodeChunker
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from .indexer import ProjectIndexer
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from .search import CodeSearcher
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from .watcher import FileWatcher
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__all__ = [
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"CodeEmbedder",
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"CodeChunker",
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"ProjectIndexer",
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"CodeSearcher",
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"FileWatcher",
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] |