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RAG

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.

Pre-setup

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 ./client and run npm install
  • Create a .env file at the root of the repository. It should contain at least a valid OPENAI_API_KEY for the backend to run properly.

🚧 Setup 🚧

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 ./chromadb and run ./launch.sh

Starts the chroma server in a Docker container and redirects logs to the centralized logs folder.

  • Go to ./api and run python main.py

Starts the backend server.

  • Go to ./client and run npm start

Starts the frontend npm dev server.

That's it! Access the project at http://localhost:3000.