Development Exercise for Mistral-based RAG

This repository is an exercise in developing Retrieval-Augmented Generation (RAG) using the Mistral 7B LLM and Milvus database. The application is designed to run locally within Docker containers.

Dataset

The dataset used for this project: Wikipedia Movie Plots - Kaggle

Getting Started

To set up the environment and get started with the project, follow these steps:

1. Clone the repository and navigate to its directory.

2. Initialize and activate the environment:

   pipenv --python 3.11
   pipenv install
   pipenv run python -m ipykernel install --user --name="da_$(basename $(pwd))" --display-name="da_$(basename $(pwd))"

3. Restart your code editor to recognize the new environment.

4. Open the main.ipynb notebook and select the appropriate kernel and interpreter.

5. Run the notebook to start interacting with the RAG setup.

For additional notes and development progress, refer to devjournal.ipynb.

Project Structure

• main.ipynb - The primary notebook for running RAG processes.
• devjournal.ipynb - The development work log and notes.