Retrieval Augmented Generation, or RAG, is an architectural approach that can improve the effiacy of Large Language Model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM.
In order to launch the local dev setup seamlessly, you'll need to do the following:
- Clone the repo, go to the root, and install dependencies with
poetry install - Go to
./clientand runnpm install - Create a
.envfile at the root of the repository. It should contain at least a validOPENAI_API_KEYfor the backend to run properly.
Warning: this will only work if you have Docker and its plugins installed.
Open a new terminal tab, and execute the following commands in this order:
- Go to
./chromadband run./launch.sh
Starts the chroma server in a Docker container and redirects logs to the centralized logs folder.
- Go to
./apiand runpython main.py
Starts the backend server.
- Go to
./clientand runnpm start
Starts the frontend npm dev server.
That's it! Access the project at http://localhost:3000.