LLM Gateway

🤔 What is this?

llm-gateway is a gateway for third party LLM providers such as OpenAI, Cohere, etc. It tracks data sent and received from these providers in a postgres database and runs PII scrubbing heuristics prior to sending.

Per OpenAI's non-API consumer products data usage policy, they "may use content such as prompts, responses, uploaded images, and generated images to improve our services" to improve products like ChatGPT and DALL-E.

Use llm-gateway to interact with OpenAI in a safe manner. The gateway also recreates the ChatGPT frontend using OpenAI's /ChatCompletion endpoint to keep all communication within the API.

⚒️ Usage

The provider's API key needs to be saved as an environment variable (see setup further down). If you are communicating with OpenAI, set OPENAI_API_KEY.

API Usage

[OpenAI] Example cURL to /completion endpoint:

curl -X 'POST' \
  'http://<host>/api/openai/completion' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "temperature": 0,
  "prompt": "Tell me what is the meaning of life",
  "max_tokens": 50,
  "model": "text-davinci-003"
}'

[OpenAI] When using the /chat_completion endpoint, formulate as conversation between user and assistant.

curl -X 'POST' \
  'http://<host>/api/openai/chat_completion' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "messages": [
    {"role": "assistant", "content": "You are an intelligent assistant."},
    {"role": "user", "content": "create a healthy recipe"}
  ],
  "model": "gpt-3.5-turbo",
  "temperature": 0
}'

Python Usage

from llm_gateway.providers.openai import OpenAIWrapper

wrapper = OpenAIWrapper()
wrapper.send_openai_request(
    "Completion",
    "create",
    max_tokens=100,
    prompt="What is the meaning of life?",
    temperature=0,
    model="text-davinci-003",
)

🚀 Quick Start for Developers

This project uses Poetry, Pyenv for dependency and environment management. Check out the official installation documentation for Poetry and Pyenv to get started. For front-end portion, this project use npm and yarn for dependency management. The most up-to-date node version required for this project is declared in .node-version.

Backend Dependencies

If using Docker, steps 1-3 are optional. We recommend installing pre-commit hooks to speed up the development cycle.

Install Poetry and Pyenv
Install pyenv install 3.11.3
Install project requirments

brew install gitleaks
poetry install
poetry run pre-commit install

Run cp .envrc.example .envrc and update with API secrets

🐳 Docker Development Loop (backend & frontend)

To run in Docker:

# build docker image
make docker-build

# spin up docker-compose
make docker-run

# open frontend in browser
make browse

# open FastAPI Swagger API
make browse-api

# delete docker-compose setup
make clean

interviewstreet / llm-gateway