Skip to content

Latest commit

 

History

History
71 lines (45 loc) · 2.55 KB

README.md

File metadata and controls

71 lines (45 loc) · 2.55 KB
Raw

RAG-based Chat with PDF

This project enables conversational interaction with PDF documents using the Retrieval-Augmented Generation (RAG) model. Users can ask questions related to the content of uploaded PDF files, and the system will provide detailed responses using RAG-based conversation generation techniques.

Features

  • PDF Upload: Users can upload one or multiple PDF files containing the information they want to inquire about.

  • Text Extraction: Extracts text content from uploaded PDF files for processing and analysis.

  • Text Chunking: Splits the extracted text into smaller chunks for efficient processing and retrieval.

  • Vector Store Creation: Utilizes FAISS to create a vector store from the text chunks, enabling fast and accurate retrieval of relevant information.

  • Conversational Interface: Utilizes the RAG model to generate responses to user queries in a conversational manner.

Tech Stack

  • Python: Programming language used for development.
  • Streamlit: Web application framework for building interactive web applications.
  • PyPDF2: Python library for reading PDF files.
  • Langchain: Framework for developing RAG model using LLM.
  • FAISS: Library for efficient similarity search and clustering of dense vectors.
  • Anthropic: API for claude-3-sonnet-20240229 LLM.

Installation

To run this project locally, follow these steps:

  1. Clone the repository:
git clone https://github.com/sagnik-datta-02/ChatwithPDF.git
  1. Install the required dependencies:
pip install -r requirements.txt

  1. Set up environment variables:

    • Ensure you have an Anthropic API key and set it in your environment variables as ANTHROPIC_API_KEY.

    • Make sure to have a .env file containing your environment variables, including the Anthropic API key.

  2. Run the Streamlit app:

streamlit run multipdfragapp.py

Usage

  1. Upload PDF files containing the information you want to inquire about.
  2. Click on "Submit & Process" to process the uploaded PDF files.
  3. Ask a question related to the content of the uploaded PDF files in the text input field and receive a response to your question.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements