Table of Contents
Project Overview
In this project, I developed a Retrieval-Augmented Generation (RAG) system designed to enhance natural language processing capabilities by integrating multiple external data sources. The system utilizes a vector database (FIASS) for efficient data retrieval and incorporates medical books in PDF format, PubMed web pages, and Wikipedia as key information sources.
Features
🎯Efficient Data Retrieval: The system quickly retrieves relevant information from large datasets, ensuring users get accurate and useful responses.
🎯Diverse Data Sources: By integrating multiple external data sources, the system provides comprehensive and authoritative information.
🎯 Interactive Frontend: The Streamlit application offers a clean and easy-to-use interface for users to interact with the RAG system.
🎯Scalable Architecture: The use of FastAPI and LangChain allows for easy scaling and integration of additional data sources and functionalities.
Technical Stack
- Python: Core programming language for developing the retriever tool, backend, and integrating LangChain.
- FIASS: Vector database used for storing and retrieving data efficiently.
- LangChain: Framework for managing prompts and facilitating interaction with the language model.
- FastAPI: Backend framework for handling API requests and managing routes.
- Streamlit: Frontend framework for creating an interactive user interface.
- External Data Sources: Medical books (PDF), PubMed, Wikipedia.
- OpenAI LLM: The API exposed by open AI is used
Output Snapshots
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Demo Video
I have laid out detailed content on each of the frameworks used. Please review here
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Conclusion
This project showcases the potential of combining advanced data retrieval techniques with natural language processing to create a powerful and versatile RAG system. By leveraging a vector database and integrating diverse data sources, the system can provide accurate and contextually relevant information to users in real time. Modern frameworks like LangChain, FastAPI, and Streamlit ensure the system is scalable, efficient, and user-friendl
