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.
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PDF Upload: Users can upload one or multiple PDF files containing the information they want to inquire about.
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Text Extraction: Extracts text content from uploaded PDF files for processing and analysis.
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Text Chunking: Splits the extracted text into smaller chunks for efficient processing and retrieval.
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Vector Store Creation: Utilizes FAISS to create a vector store from the text chunks, enabling fast and accurate retrieval of relevant information.
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Conversational Interface: Utilizes the RAG model to generate responses to user queries in a conversational manner.
- 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.
To run this project locally, follow these steps:
- Clone the repository:
git clone https://github.com/sagnik-datta-02/ChatwithPDF.git- Install the required dependencies:
pip install -r requirements.txt-
Set up environment variables:
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Ensure you have an Anthropic API key and set it in your environment variables as
ANTHROPIC_API_KEY. -
Make sure to have a
.envfile containing your environment variables, including the Anthropic API key.
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Run the Streamlit app:
streamlit run multipdfragapp.py- Upload PDF files containing the information you want to inquire about.
- Click on "Submit & Process" to process the uploaded PDF files.
- Ask a question related to the content of the uploaded PDF files in the text input field and receive a response to your question.
This project is licensed under the MIT License - see the LICENSE file for details.