Simple RAG with DSPy & ChromaDB

This project demonstrates a simple Retrieval-Augmented Generation (RAG) pipeline using DSPy and ChromaDB. The pipeline processes a PDF document, stores the text in a ChromaDB collection, and uses a language model to answer questions based on the retrieved context.

Project Structure

• pipeline.ipynb: Jupyter notebook containing the code for the RAG pipeline.
• data/tesla10K.pdf: Sample PDF file used for text extraction and processing.

Setup

1. Clone the repository:

   git clone https://github.com/marioyordanoff/dspy

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Create a .env file:

   touch .env

5. Add your environment variables to the .env file. For example:

   OPENAI_API_KEY=your_openai_api_key

License

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

Acknowledgements

• DSPy
• ChromaDB
• OpenAI