Conversational RAG with PDF Uploads and Chat History Overview This project implements a Retrieval-Augmented Generation (RAG) model using Streamlit to create an interactive question-answering assistant. Users can upload PDF documents, which are processed to enable the assistant to answer questions based on the content of the uploaded files.

Features PDF Uploading: Upload one or more PDF files. Interactive Chat Interface: Ask questions related to the content of the uploaded PDFs. Contextual Understanding: The assistant uses chat history to rephrase questions, ensuring accurate responses. Integration with Language Models: Leverages advanced language models (e.g., ChatGroq) for generating responses based on the retrieved context. Technologies Used Streamlit: For creating the web application interface. LangChain: For building the RAG model and managing document retrieval. Hugging Face: For embedding generation and language model integration. Chroma: For managing the vector store and efficient document retrieval. Installation To run this project locally, clone the repository and install the required packages: git clone https://github.com/Alaaibrahim2/a.git cd a pip install -r requirements.txt

Usage Run the Streamlit application: bash Copy code streamlit run app.py Upload PDF Files: Use the upload feature to add your PDF documents. Ask Questions: Start interacting with the assistant by typing your questions related to the content of the uploaded PDFs

Example Interaction User: What is the main topic of the uploaded document? Assistant: The main topic of the document is about advancements in artificial intelligence.