/ragbacker

Primary LanguagePython

This is a LlamaIndex project using FastAPI bootstrapped with create-llama.

Getting Started

First, setup the environment with poetry:

Note: This step is not needed if you are using the dev-container.

poetry install
poetry shell

By default, we use the OpenAI LLM (though you can customize, see app/settings.py). As a result, you need to specify an OPENAI_API_KEY in an .env file in this directory.

Example .env file:

OPENAI_API_KEY=<openai_api_key>

If you are using any tools or data sources, you can update their config files in the config folder.

Second, generate the embeddings of the documents in the ./data directory (if this folder exists - otherwise, skip this step):

python app/engine/generate.py

Third, run the development server:

python main.py

The example provides two different API endpoints:

  1. /api/chat - a streaming chat endpoint
  2. /api/chat/request - a non-streaming chat endpoint

You can test the streaming endpoint with the following curl request:

curl --location 'localhost:8000/api/chat' \
--header 'Content-Type: application/json' \
--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'

And for the non-streaming endpoint run:

curl --location 'localhost:8000/api/chat/request' \
--header 'Content-Type: application/json' \
--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'

You can start editing the API endpoints by modifying app/api/routers/chat.py. The endpoints auto-update as you save the file. You can delete the endpoint you're not using.

Open http://localhost:8000/docs with your browser to see the Swagger UI of the API.

The API allows CORS for all origins to simplify development. You can change this behavior by setting the ENVIRONMENT environment variable to prod:

ENVIRONMENT=prod python main.py

Using Docker

  1. Build an image for the FastAPI app:
docker build -t <your_backend_image_name> .
  1. Generate embeddings:

Parse the data and generate the vector embeddings if the ./data folder exists - otherwise, skip this step:

docker run \
  --rm \
  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
  -v $(pwd)/config:/app/config \
  -v $(pwd)/data:/app/data \ # Use your local folder to read the data
  -v $(pwd)/storage:/app/storage \ # Use your file system to store the vector database
  <your_backend_image_name> \
  python app/engine/generate.py
  1. Start the API:
docker run \
  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
  -v $(pwd)/config:/app/config \
  -v $(pwd)/storage:/app/storage \ # Use your file system to store gea vector database
  -p 8000:8000 \
  <your_backend_image_name>

Learn More

To learn more about LlamaIndex, take a look at the following resources:

You can check out the LlamaIndex GitHub repository - your feedback and contributions are welcome!