Advanced RAG Q&A Chatbot

• In this project I have built an advanced RAG Q&A chatbot with chain and retrievers using Langchain

Description

• Retrievers: A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.
• A retriever does not need to be able to store documents, only to return (or retrieve) them. Vector stores can be used as the backbone of a retriever, but there are other types of retrievers as well.
• Retrieval chain:This chain takes in a user inquiry, which is then passed to the retriever to fetch relevant documents. Those documents (and original inputs) are then passed to an LLM to generate a response

Libraries Used

• langchain==0.1.20
• langchain-community==0.0.38
• langchain-google-genai==1.0.3
• faiss-cpu==1.8.0
• pypdf==4.2.0

Installation

1. Prerequisites
   • Git
   • Command line familiarity
2. Clone the Repository: git clone https://github.com/NebeyouMusie/Advanced-RAG-QA-Chatbot.git
3. Create and Activate Virtual Environment (Recommended)
   • python -m venv venv
   • source venv/bin/activate
4. Navigate to the projects directory cd ./Advanced-RAG-QA-Chatbot using your terminal
5. Install Libraries: pip install -r requirements.txt
6. Open and run all cells in the retriever_chain.ipynb notebook then enter your google api key
7. Or you can download the PDF in the data directory and the retriever_chain.ipynb Notebook from the notebook directory in the repository, upload those files and notebook to Google Collab then run all the cells in the retriever_chain.ipynb Notebook then enter your google api key

Collaboration

• Collaborations are welcomed ❤️

Acknowledgments

• I would like to thank Krish Naik

Contact

• LinkedIn: Nebeyou Musie
• Gmail: [email protected]
• Telegram: Nebeyou Musie