{"id":22059791,"url":"https://github.com/armandfs/langchain_pdf_rag","last_synced_at":"2026-05-04T01:34:13.324Z","repository":{"id":264278903,"uuid":"891880232","full_name":"ArmandFS/langchain_pdf_rag","owner":"ArmandFS","description":"This project is development of a Large Language Model using Python, Streamlit, and the O-Llama LLM open source tool for the built in model. 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For experimentation purposes, this project is tested in a Jupyter Notebook Environment. \n\n## ✨ Features ✨\n\n- 🔒 Fully local processing - no data leaves your machine\n- 📄 PDF processing with intelligent chunking\n- 🧠 Multi-query retrieval for better context understanding\n- 🎯 Advanced RAG implementation using LangChain\n- 📓 Jupyter notebook for personal experimentation\n\n\n## 🚀 Implementation 🚀\nIf you want to use this on your own local setup, you can either watch Tony's video, or follow these simple steps below:\n\n1. You will need to create a virtual environment first on so python dependencies and libraries don't clash\n\n```\n# On Windows\npython -m venv venv\nvenv\\Scripts\\activate\n\n# On macOS/Linux\npython3 -m venv venv\nsource venv/bin/activate\n```\n\n2. Install the requirementss\n```\npip install -r requirements.txt\n```\n\n3. Ensure you have the Ollama package installed\n```\npip install ollama\n```\n\n4. Update to Ollama 0.4.1 to prevent TypeError issues\n```\npip install --upgrade ollama==0.4.1\n```\n\nNote: The application will run slower on CPU-only systems, but it will still work effectively.\n\n## 📝 License\n\nThis project is open source and available under the MIT License.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farmandfs%2Flangchain_pdf_rag","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farmandfs%2Flangchain_pdf_rag","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farmandfs%2Flangchain_pdf_rag/lists"}