This is a Streamlit-based web application that allows users to upload PDF documents and interact with them via a Conversational Retrieval Augmented Generation (RAG) model. The app uses vector embeddings for document retrieval and a language model (LLM) for answering user queries based on the uploaded documents.
- Upload PDF Documents: Upload a PDF file to create a vector database.
- Chat Interface: Engage in a conversation where the app reads from the PDF and answers questions.
- Real-time VectorDB Construction: Dynamically builds a vector database from the uploaded document for fast retrieval.
- Persistent Chat History: The app keeps track of the conversation history.
Before running the application, ensure you have the following installed:
- Python 3.8+
- Streamlit
- Langchain
- Huggigface
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Clone the repository:
git clone https://github.com/yourusername/conversational-pdf-rag.git cd conversational-pdf-rag