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Chat With PDFs

Chat with your PDF files for free, using Langchain, Groq, Chroma vector store, and Jina AI embeddings. This repository contains a simple Python implementation of the RAG (Retrieval-Augmented-Generation) system. The RAG model is used to retrieve relevant chunks of the user PDF file based on user queries and provide informative responses.

Installation

Follow these steps:

  1. Clone the repository 
    git clone https://github.com/S4mpl3r/chat-with-pdf.git
  2. Create a virtual environment and activate it (optional, but highly recommended). 
    python -m venv .venv
Windows: .venv\Scripts\activate
Linux: source .venv/bin/activate
  3. Install required packages: 
    python -m pip install -r requirements.txt
  4. Create a .env file in the root of the project and populate it with the following keys. You'll need to obtain your api keys: 
    JINA_API_KEY=<YOUR KEY>
GROQ_API_KEY=<YOUR KEY>
HF_TOKEN=<YOUR TOKEN>
HF_HOME=<PATH TO STORE HUGGINGFACE MODEL>
  5. Run the program: 
    python main.py

Configuration

You can customize the behavior of the system by modifying the constants and parameters in the main.py file:

  • EMBED_MODEL_NAME: Specify the name of the Jina embedding model to be used.
  • LLM_NAME: Specify the name of the language model (Refer to Groq for the list of available models).
  • LLM_TEMPERATURE: Set the temperature parameter for the language model.
  • CHUNK_SIZE: Specify the maximum chunk size allowed by the embedding model.
  • DOCUMENT_DIR: Specify the directory where PDF documents are stored.
  • VECTOR_STORE_DIR: Specify the directory where vector embeddings are stored.
  • COLLECTION_NAME: Specify the name of the collection for the chroma vector store.

Resources

Kudos to the amazing libraries and services listed below:

License

MIT