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\!
62 lines
1.9 KiB
Markdown
62 lines
1.9 KiB
Markdown
# RAG System - Hybrid Mode Setup
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This RAG system can operate in three modes:
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## 🚀 **Mode 1: Ollama Only (Recommended - Lightweight)**
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```bash
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pip install -r requirements-light.txt
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# Requires: ollama serve running with nomic-embed-text model
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```
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- **Size**: ~426MB total
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- **Performance**: Fastest (leverages Ollama)
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- **Network**: Uses local Ollama server
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## 🔄 **Mode 2: Hybrid (Best of Both Worlds)**
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```bash
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pip install -r requirements-full.txt
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# Works with OR without Ollama
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```
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- **Size**: ~3GB total (includes ML fallback)
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- **Resilience**: Automatic fallback if Ollama unavailable
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- **Performance**: Ollama speed when available, ML fallback when needed
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## 🛡️ **Mode 3: ML Only (Maximum Compatibility)**
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```bash
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pip install -r requirements-full.txt
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# Disable Ollama fallback in config
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```
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- **Size**: ~3GB total
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- **Compatibility**: Works anywhere, no external dependencies
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- **Use case**: Offline environments, embedded systems
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## 🔧 **Configuration**
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Edit `.claude-rag/config.json` in your project:
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```json
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{
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"embedding": {
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"provider": "hybrid", // "hybrid", "ollama", "fallback"
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"model": "nomic-embed-text:latest",
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"base_url": "http://localhost:11434",
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"enable_fallback": true // Set to false to disable ML fallback
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}
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}
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```
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## 📊 **Status Check**
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```python
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from claude_rag.ollama_embeddings import OllamaEmbedder
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embedder = OllamaEmbedder()
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status = embedder.get_status()
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print(f"Mode: {status['mode']}")
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print(f"Ollama: {'✅' if status['ollama_available'] else '❌'}")
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print(f"ML Fallback: {'✅' if status['fallback_available'] else '❌'}")
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```
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## 🎯 **Automatic Behavior**
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1. **Try Ollama first** - fastest and most efficient
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2. **Fall back to ML** - if Ollama unavailable and ML dependencies installed
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3. **Use hash fallback** - deterministic embeddings as last resort
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The system automatically detects what's available and uses the best option! |