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Welcome to Hands-on RAG, a comprehensive guide and repository for building, understanding, and implementing Retrieval-Augmented Generation systems. This project explores everything from simple chatbots with memory to advanced RAG implementations using vector databases and Model Context Protocol (MCP).
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- Website: hereandnowai.com
- Organization: github.com/hereandnowai
- Simple Chatbot: Basic LLM interaction.
- Memory Systems: Chatbots that remember context and previous interactions.
- System Prompt RAG: Implementing RAG logic directly within system instructions.
- Raw Document Processing: Extracting knowledge directly from PDFs.
- Vector Database Integration: Using FAISS for efficient similarity search and retrieval.
- MCP Compatibility: Designed to work within the Model Context Protocol ecosystem.
1-simple_chatbot.py: Basic chatbot implementation.2-chatbot-with-memory copy.py: Chatbot with conversation history.3-rag-from-system-prompt.py: RAG logic via system prompts.4-rag-from-raw-pdf copy.py: PDF knowledge extraction.5-rag-from-vectordb.py: Advanced RAG with FAISS vector database.faiss_mcp_index/: Local FAISS index files.
- Python 3.8+
- API keys for your preferred LLM provider.
git clone https://github.com/hereandnowai/hands-on-rag.git
cd hands-on-rag
pip install -r requirements.txtThis project is licensed under the MIT License. See the LICENSE file for details.
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- Email: info@hereandnowai.com
- Phone: +91 996 296 1000
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