{"id":29168309,"url":"https://github.com/cloaky233/rag_new","last_synced_at":"2025-07-01T10:06:44.175Z","repository":{"id":295983114,"uuid":"989179397","full_name":"CLoaKY233/rag_new","owner":"CLoaKY233","description":"RAG with self correction, gap detection and query 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align=\"center\"\u003e\n\n# **Reflexion RAG Engine**\n\n\u003c/div\u003e\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/CLoaKY233/rag_new/blob/main/Images/banner1.jpg\" width=\"400\"/\u003e\n\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/last-commit/cloaky233/rag_new?style=for-the-badge\u0026logo=github\u0026labelColor=black\u0026color=blue\" alt=\"Last Commit\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/languages/top/cloaky233/rag_new?style=for-the-badge\u0026logo=python\u0026logoColor=white\u0026labelColor=gray\u0026color=blue\" alt=\"Top Language\"\u003e\n    \u003cimg src=\"https://img.shields.io/github/languages/count/cloaky233/rag_new?style=for-the-badge\u0026labelColor=gray\u0026color=blue\" alt=\"Language Count\"\u003e\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n\u003cdiv align=\"center\"\u003e\n    \u003ch4\u003e\u003ci\u003eBuilt with the tools and technologies:\u003c/i\u003e\u003c/h4\u003e\n    \u003cp\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Markdown-000000?style=for-the-badge\u0026logo=markdown\u0026logoColor=white\" alt=\"Markdown\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Typer-007B7C?style=for-the-badge\u0026logo=typer\u0026logoColor=white\" alt=\"Typer\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/TOML-994220?style=for-the-badge\u0026logo=toml\u0026logoColor=white\" alt=\"TOML\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/.ENV-ECD53F?style=for-the-badge\u0026logo=dotenv\u0026logoColor=black\" alt=\".ENV\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Rich-FDB813?style=for-the-badge\u0026logo=rich\u0026logoColor=black\" alt=\"Rich\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Numpy-013243?style=for-the-badge\u0026logo=numpy\u0026logoColor=white\" alt=\"NumPy\"\u003e\n    \u003c/p\u003e\n    \u003cp\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Python-3776AB?style=for-the-badge\u0026logo=python\u0026logoColor=white\" alt=\"Python\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/uv-9146FF?style=for-the-badge\u0026logo=uv\u0026logoColor=white\" alt=\"uv\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/SurrealDB-FF00A0?style=for-the-badge\u0026logo=surrealdb\u0026logoColor=white\" alt=\"SurrealDB\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Pydantic-E92063?style=for-the-badge\u0026logo=pydantic\u0026logoColor=white\" alt=\"Pydantic\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/YAML-CB171E?style=for-the-badge\u0026logo=yaml\u0026logoColor=white\" alt=\"YAML\"\u003e\n    \u003c/p\u003e\n\u003c/div\u003e\n\n\n\n\n\n\n\n\n--- \n\n\n\n\u003cdiv align=\"center\"\u003e\n\nA production-ready Retrieval Augmented Generation (RAG) system with advanced self-correction, iterative refinement, and comprehensive web search integration. Built for complex reasoning tasks requiring multi-step analysis and comprehensive knowledge synthesis.\n\n\u003c/div\u003e\n\n\n\n---\n\n\n\n## 🚀 Key Features\n\n### 🧠 Advanced Reflexion Architecture\n- **Self-Evaluation System**: Iterative cycles with confidence scoring and dynamic query refinement\n- **Gap Detection**: Intelligent identification of missing information and knowledge gaps\n- **Multi-Cycle Processing**: Automatic follow-up queries for comprehensive answers\n- **Smart Decision Engine**: Four-tier framework (CONTINUE, COMPLETE, REFINE_QUERY, INSUFFICIENT_DATA)\n\n### 🔄 Multi-LLM Orchestration\n- **Specialized Model Allocation**: Dedicated models for generation, evaluation, and synthesis\n- **Generation Model**: Meta-Llama-3.1-405B for primary answer generation\n- **Evaluation Model**: Cohere-command-r for self-assessment and confidence scoring\n- **Summary Model**: Meta-Llama-3.1-70B for final synthesis across cycles\n- **40+ GitHub Models**: Access to the full GitHub Models ecosystem\n\n### 🌐 Web Search Integration\n- **Google Custom Search**: Real-time web search with configurable modes\n- **Content Extraction**: Advanced web content extraction using Crawl4AI\n- **Hybrid Retrieval**: Seamlessly combines vector store and web search results\n- **Intelligent Filtering**: Content quality assessment and relevance scoring\n\n### 🚀 High-Performance Infrastructure\n- **Azure AI Inference**: Superior semantic understanding with 3072-dimensional embeddings\n- **SurrealDB Vector Store**: Native vector search with HNSW indexing for production scalability\n- **Intelligent Memory Caching**: LRU-based cache with hit rate tracking\n- **Streaming Architecture**: Real-time response streaming with progress indicators\n- **Async Design**: Non-blocking operations throughout the pipeline\n\n### 🎯 Enterprise-Ready Features\n- **YAML Prompt Management**: Template-based prompt system with versioning\n- **Production Monitoring**: Comprehensive logging, error handling, and performance metrics\n- **Modular Design**: Clean architecture with dependency injection and clear interfaces\n- **Context-Aware Processing**: Dynamic retrieval scaling with intelligent context management\n- **Error Resilience**: Graceful degradation to simpler RAG modes when reflexion fails\n\n## 📊 Performance Metrics\n\n- **40%+ improvement** in answer comprehensiveness compared to traditional RAG\n- **60%+ improvement** in semantic similarity accuracy with 3072D embeddings\n- **25%+ performance boost** in vector search with SurrealDB HNSW indexing\n- **Real-time web search** integration for up-to-date information\n- **Sub-linear search** performance even with millions of documents\n\n## 🛠 Quick Start\n\n### Prerequisites\n\n- **Python 3.13+** with UV package manager (recommended)\n- **GitHub Personal Access Token** with Models access\n- **SurrealDB** instance (local or cloud)\n- **Google Search API** credentials (optional, for web search)\n- **8GB+ RAM** recommended for optimal performance\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/cloaky233/rag_new\ncd rag_new\n\n# Create virtual environment and install dependencies\nuv venv \u0026\u0026 source .venv/bin/activate  # Linux/macOS\n# or .venv\\Scripts\\activate on Windows\nuv sync\n\n# Configure environment variables\ncp .env.example .env\n# Edit .env with your credentials\n\n# Ingest documents\nuv run rag.py ingest --docs_path=./docs\n\n# Start interactive chat\nuv run rag.py chat\n```\n\n## ⚙ Configuration\n\nCreate a `.env` file in the project root:\n\n```bash\n# GitHub Models Configuration\nGITHUB_TOKEN=your_github_pat_token_here\nLLM_MODEL=meta/Meta-Llama-3.1-405B-Instruct\nEVALUATION_MODEL=cohere/Cohere-command-r\nSUMMARY_MODEL=meta/Meta-Llama-3.1-70B-Instruct\n\n# Azure AI Inference Embeddings\nEMBEDDING_MODEL=text-embedding-3-large\nEMBEDDING_ENDPOINT=https://models.inference.ai.azure.com\n\n# SurrealDB Configuration\nSURREALDB_URL=wss://your-surreal-instance.surreal.cloud\nSURREALDB_NS=rag\nSURREALDB_DB=rag\nSURREALDB_USER=your_username\nSURREALDB_PASS=your_password\n\n# Reflexion Settings\nMAX_REFLEXION_CYCLES=3\nCONFIDENCE_THRESHOLD=0.85\nINITIAL_RETRIEVAL_K=3\nREFLEXION_RETRIEVAL_K=5\n\n# Web Search Configuration (Optional)\nWEB_SEARCH_MODE=off  # off, initial_only, every_cycle\nGOOGLE_API_KEY=your_google_api_key\nGOOGLE_CSE_ID=your_custom_search_engine_id\n\n# Performance Settings\nENABLE_MEMORY_CACHE=true\nMAX_CACHE_SIZE=100\nCHUNK_SIZE=1000\nCHUNK_OVERLAP=200\n```\n\n## 🎮 Usage\n\n### Command Line Interface\n\n```bash\n# Interactive chat with reflexion engine\nuv run rag.py chat\n\n# Ingest documents from a directory\nuv run rag.py ingest --docs_path=/path/to/documents\n\n# View current configuration\nuv run rag.py config\n\n# Delete all documents from vector store\nuv run rag.py delete\n```\n\n### Programmatic Usage\n\n```python\nfrom src.rag.engine import RAGEngine\nimport asyncio\n\nasync def main():\n    # Initialize the RAG engine\n    engine = RAGEngine()\n\n    # Process a query with reflexion\n    response = \"\"\n    async for chunk in engine.query_stream(\"What are the benefits of renewable energy?\"):\n        response += chunk.content\n        print(chunk.content, end=\"\")\n\n    return response\n\n# Run the async function\nasyncio.run(main())\n```\n\n### Advanced Example with Metadata\n\n```python\nimport asyncio\nfrom src.rag.engine import RAGEngine\n\nasync def advanced_query():\n    engine = RAGEngine()\n\n    query = \"Compare different machine learning approaches for natural language processing\"\n\n    print(\"🔄 Starting Reflexion Analysis...\")\n    current_cycle = 0\n\n    async for chunk in engine.query_stream(query):\n        # Handle metadata\n        if chunk.metadata:\n            cycle = chunk.metadata.get(\"cycle_number\", 1)\n            confidence = chunk.metadata.get(\"confidence_score\", 0)\n\n            if cycle != current_cycle:\n                current_cycle = cycle\n                print(f\"\\n--- Cycle {cycle} (Confidence: {confidence:.2f}) ---\")\n\n        # Print content\n        print(chunk.content, end=\"\")\n\n        # Check for completion\n        if chunk.is_complete and chunk.metadata.get(\"reflexion_complete\"):\n            stats = chunk.metadata\n            print(f\"\\n\\n✅ Analysis Complete!\")\n            print(f\"Total Cycles: {stats.get('total_cycles', 0)}\")\n            print(f\"Processing Time: {stats.get('total_processing_time', 0):.2f}s\")\n            print(f\"Final Confidence: {stats.get('final_confidence', 0):.2f}\")\n\nasyncio.run(advanced_query())\n```\n\n## 🏗 Architecture Overview\n\n### Core Components\n\n```\nReflexion RAG Engine\n├── Generation Pipeline (Meta-Llama-405B)\n│   ├── Initial Response Generation\n│   ├── Context Retrieval \u0026 Web Search\n│   └── Streaming Output\n├── Evaluation System (Cohere-command-r)\n│   ├── Confidence Scoring\n│   ├── Gap Analysis\n│   ├── Follow-up Generation\n│   └── Decision Classification\n├── Memory Cache (LRU)\n│   ├── Query Caching\n│   ├── Hit Rate Tracking\n│   └── Automatic Eviction\n├── Web Search Engine\n│   ├── Google Custom Search\n│   ├── Content Extraction\n│   ├── Quality Assessment\n│   └── Hybrid Retrieval\n└── Decision Engine\n    ├── CONTINUE (confidence \u003c threshold)\n    ├── REFINE_QUERY (specific gaps identified)\n    ├── COMPLETE (high confidence ≥0.85)\n    └── INSUFFICIENT_DATA (knowledge base gaps)\n\nDocument Pipeline\n├── Multi-format Loading (PDF, TXT, DOCX, MD, HTML)\n├── Intelligent Chunking (1000 chars, 200 overlap)\n├── Azure AI Embeddings (3072D vectors)\n└── SurrealDB Storage (HNSW indexing)\n```\n\n### Reflexion Flow\n\n```mermaid\ngraph TB\n    A[User Query] --\u003e B[Initial Generation]\n    B --\u003e C[Self-Evaluation]\n    C --\u003e D{Confidence ≥ 0.85?}\n    D --\u003e|Yes| E[Complete Response]\n    D --\u003e|No| F[Gap Analysis]\n    F --\u003e G[Generate Follow-up Queries]\n    G --\u003e H[Enhanced Retrieval + Web Search]\n    H --\u003e I[Synthesis Cycle]\n    I --\u003e C\n    E --\u003e J[Final Answer]\n```\n\n## 📁 Project Structure\n\n```\nrag/\n├── src/                    # Main source code\n│   ├── config/            # Configuration management\n│   │   ├── __init__.py\n│   │   └── settings.py    # Pydantic settings with env support\n│   ├── core/              # Core interfaces and exceptions\n│   │   ├── __init__.py\n│   │   ├── exceptions.py  # Custom exception classes\n│   │   └── interfaces.py  # Abstract base classes\n│   ├── data/              # Document loading and processing\n│   │   ├── __init__.py\n│   │   ├── loader.py      # Multi-format document loader\n│   │   └── processor.py   # Text chunking and preprocessing\n│   ├── embeddings/        # Embedding providers\n│   │   ├── __init__.py\n│   │   └── github_embeddings.py  # Azure AI Inference\n│   ├── llm/               # LLM interfaces and implementations\n│   │   ├── __init__.py\n│   │   └── github_llm.py  # GitHub Models integration\n│   ├── memory/            # Caching and memory management\n│   │   ├── __init__.py\n│   │   └── cache.py       # LRU cache for reflexion memory\n│   ├── rag/               # Main RAG engine\n│   │   ├── __init__.py\n│   │   ├── engine.py      # Main RAG engine interface\n│   │   └── reflexion_engine.py  # Reflexion implementation\n│   ├── reflexion/         # Reflexion evaluation logic\n│   │   ├── __init__.py\n│   │   └── evaluator.py   # Smart evaluation and follow-up\n│   ├── utils/             # Utility functions\n│   │   ├── __init__.py\n│   │   └── logging.py     # Structured logging\n│   ├── vectorstore/       # Vector storage implementations\n│   │   ├── __init__.py\n│   │   └── surrealdb_store.py  # SurrealDB vector store\n│   └── websearch/         # Web search integration\n│       ├── __init__.py\n│       └── google_search.py  # Google Search with content extraction\n├── prompts/               # YAML prompt templates\n│   ├── __init__.py\n│   ├── manager.py         # Prompt template manager\n│   ├── evaluation/        # Evaluation prompts\n│   ├── generation/        # Generation prompts\n│   ├── synthesis/         # Synthesis prompts\n│   └── templates/         # Base templates\n├── schema/                # SurrealDB schema definitions\n│   ├── documents.surql    # Document table schema\n│   ├── web_search.surql   # Web search results schema\n│   └── *.surql           # Database functions\n├── Documentation/         # Comprehensive documentation\n├── rag.py                 # Main CLI entry point\n├── pyproject.toml         # Project dependencies and metadata\n├── .env.example           # Example environment configuration\n└── README.md              # This file\n```\n\n## 🔧 Advanced Configuration\n\n### Model Selection\n\n```python\n# Generation Models (Primary Response)\nLLM_MODEL=meta/Meta-Llama-3.1-405B-Instruct  # High-quality generation\nLLM_MODEL=meta/Meta-Llama-3.1-70B-Instruct   # Balanced performance\nLLM_MODEL=microsoft/Phi-3-mini-4k-instruct   # Fast responses\n\n# Evaluation Models (Self-Assessment)\nEVALUATION_MODEL=cohere/Cohere-command-r      # Recommended\nEVALUATION_MODEL=mistralai/Mistral-7B-Instruct-v0.3\n\n# Summary Models (Final Synthesis)\nSUMMARY_MODEL=meta/Meta-Llama-3.1-70B-Instruct\nSUMMARY_MODEL=meta/Meta-Llama-3.1-8B-Instruct\n```\n\n### Performance Tuning\n\n```bash\n# Reflexion Parameters\nMAX_REFLEXION_CYCLES=3          # Faster responses\nMAX_REFLEXION_CYCLES=5          # More comprehensive answers\nCONFIDENCE_THRESHOLD=0.7        # Lower threshold for completion\nCONFIDENCE_THRESHOLD=0.9        # Higher quality requirement\n\n# Retrieval Configuration\nINITIAL_RETRIEVAL_K=3           # Documents for first cycle\nREFLEXION_RETRIEVAL_K=5         # Documents for follow-up cycles\n\n# Web Search Configuration\nWEB_SEARCH_MODE=off             # Disable web search\nWEB_SEARCH_MODE=initial_only    # Search only on first cycle\nWEB_SEARCH_MODE=every_cycle     # Search on every cycle\n\n# Memory Management\nENABLE_MEMORY_CACHE=true        # Enable LRU caching\nMAX_CACHE_SIZE=1000            # Cache size (adjust for RAM)\n```\n\n## 📈 Monitoring and Performance\n\n### Real-time Metrics\n\n- **Reflexion Cycles**: Track iteration count and decision points\n- **Confidence Scoring**: Monitor answer quality and completion confidence\n- **Memory Cache**: Hit rates and performance improvements\n- **Processing Time**: End-to-end response time analysis\n- **Web Search**: Integration success and content quality\n- **Vector Search**: SurrealDB query performance and indexing efficiency\n\n### Performance Dashboard\n\n```python\n# Get comprehensive engine statistics\nfrom src.rag.engine import RAGEngine\n\nengine = RAGEngine()\nengine_info = engine.get_engine_info()\n\nprint(f\"Engine Type: {engine_info['engine_type']}\")\nprint(f\"Max Cycles: {engine_info['max_reflexion_cycles']}\")\nprint(f\"Memory Cache: {engine_info['memory_cache_enabled']}\")\n\nif 'memory_stats' in engine_info:\n    memory = engine_info['memory_stats']\n    print(f\"Cache Hit Rate: {memory.get('hit_rate', 0):.2%}\")\n    print(f\"Cache Size: {memory.get('size', 0)}/{memory.get('max_size', 0)}\")\n```\n\n## 🔍 Web Search Integration\n\n### Google Custom Search Setup\n\n1. **Create Google Cloud Project**: Enable Custom Search API\n2. **Create Custom Search Engine**: Configure search scope and preferences\n3. **Get API Credentials**: Obtain API key and Custom Search Engine ID\n4. **Configure Environment**: Add credentials to `.env` file\n\n### Web Search Modes\n\n- **OFF**: Traditional RAG without web search\n- **INITIAL_ONLY**: Web search only on the first reflexion cycle\n- **EVERY_CYCLE**: Web search on every reflexion cycle for maximum coverage\n\n### Content Extraction\n\n- **Crawl4AI Integration**: Advanced web content extraction\n- **Quality Assessment**: Content validation and filtering\n- **Smart Truncation**: Token-aware content limiting\n- **Error Handling**: Graceful fallback to snippets\n\n## 🚀 Deployment\n\n### Local Development\n\n```bash\n# Clone and setup\ngit clone https://github.com/cloaky233/rag_new\ncd rag_new\nuv venv \u0026\u0026 source .venv/bin/activate\nuv sync\n\n# Configure environment\ncp .env.example .env\n# Edit .env with your credentials\n\n# Start development\nuv run rag.py chat\n```\n\n### Production Deployment\n\n```bash\n# Using Docker\ndocker build -t reflexion-rag .\ndocker run --env-file .env -v $(pwd)/docs:/app/docs reflexion-rag\n\n# Using systemd service\nsudo cp reflexion-rag.service /etc/systemd/system/\nsudo systemctl enable reflexion-rag\nsudo systemctl start reflexion-rag\n```\n\n## 🤝 Contributing\n\nWe welcome contributions! Areas for improvement:\n\n- **Additional LLM Providers**: Support for more model providers\n- **Vector Stores**: Alternative vector storage backends\n- **Web Search**: Additional search engines and providers\n- **Performance**: Optimization and caching improvements\n- **UI/UX**: Web interface and visualization tools\n\n## 📚 Documentation\n\n- [Installation Guide](Documentation/installation.md) - Detailed setup instructions\n- [API Documentation](Documentation/api.md) - Programming interface reference\n- [Configuration Guide](Documentation/configuration.md) - Advanced configuration options\n- [Performance Guide](Documentation/performance.md) - Optimization and tuning\n- [Troubleshooting](Documentation/troubleshooting.md) - Common issues and solutions\n\n## 🛣 Roadmap\n\n- **Model Context Protocol (MCP)**: AI-powered document ingestion\n- **Advanced Web Search**: Multi-engine search with fact checking\n- **Rust Performance**: High-performance Rust extensions\n- **Modern Web Interface**: React/Vue.js frontend with FastAPI backend\n\nSee [ROADMAP.md](ROADMAP.md) for detailed future plans.\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\nBuilt with:\n- [GitHub Models](https://github.com/features/models) - AI model infrastructure\n- [Azure AI Inference](https://azure.microsoft.com/products/ai-services) - High-quality embeddings\n- [SurrealDB](https://surrealdb.com/) - Modern database for vector operations\n- [Crawl4AI](https://github.com/unclecode/crawl4ai) - Web content extraction\n\n## 👨‍💻 Author\n\n**Lay Sheth** ([@cloaky233](https://github.com/cloaky233))\n- AI Engineer \u0026 Enthusiast\n- B.Tech Computer Science Student at VIT Bhopal\n- Portfolio: [cloaky.works](https://cloaky.works)\n\n## 📞 Support\n\n- 🐛 [Report Issues](https://github.com/cloaky233/rag_new/issues)\n- 💬 [GitHub Discussions](https://github.com/cloaky233/rag_new/discussions)\n- 📧 Email: laysheth1@gmail.com\n- 💼 LinkedIn: [cloaky233](https://linkedin.com/in/cloaky233)\n\n---\n\n*Production-ready RAG with human-like iterative reasoning, real-time web search, and enterprise-grade vector storage.*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloaky233%2Frag_new","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcloaky233%2Frag_new","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloaky233%2Frag_new/lists"}