Webhook LLM Service

A REST API service that processes incoming webhook requests using LLM (Large Language Model) and sends generated responses to specified endpoints.

Features

• Asynchronous webhook processing with Redis message broker
• OpenAI GPT integration for text generation
• Conversation history tracking with PostgreSQL
• Rate limiting for LLM API calls
• Docker containerization
• OpenAPI documentation
• Comprehensive test coverage

Tech Stack

• Python 3.12
• Litestar (FastAPI-like web framework)
• PostgreSQL (conversation storage)
• Redis (message broker)
• OpenAI API (LLM provider)
• Docker & Docker Compose
• Poetry (dependency management)
• Alembic (database migrations)
• Pytest (testing)

Prerequisites

• Docker and Docker Compose
• Python 3.12+
• Poetry
• OpenAI API key

Installation

1. Clone the repository:

git clone https://github.com/ACK1D/webhook-llm.git
cd webhook-llm

2. Install dependencies:

poetry install

3. Create a .env file with the following variables:

OPENAI_API_KEY=<your_openai_api_key>
DATABASE_URL=<your_database_url>
REDIS_URL=<your_redis_url>
RPM_LIMIT=<your_rpm_limit>

or use the .env.example file as a template and fill in the missing values.

cp .env.example .env

4. Start the services:

docker compose up -d

API Endpoints

Health Check

GET / Response:

{"status": "ok", "service": "LLM Webhook Service", "version": "0.1.0"}

Process Webhook

POST /webhook Request:

{
    "message": "Hello, how are you?",
    "callback_url": "https://example.com/callback",
}

Response:

{
    "status": "accepted",
    "message": "Request placed in queue",
    "conversation_id": "904d270d-69ba-4057-b712-b349d4ff0d5a"
}

Automatically generated OpenAPI-based documentation

• GET /schema (ReDoc)
• GET /schema/swagger (Swagger)
• GET /schema/elements (Elements)
• GET /schema/rapidoc (RapiDoc)

Architecture

The service consists of three main components:

1. API Service: Handles incoming webhook requests and queues them in Redis
2. Worker Service: Processes queued messages, calls OpenAI API, and sends responses
3. Database: Stores conversation history and messages

Development

1. Run tests:

poetry run pytest

2. Run API locally:

poetry run python -m app.main

3. Run Worker locally:

poetry run python -m app.worker

Configuration

The service can be configured using environment variables:

• OPENAI_API_KEY: OpenAI API key
• OPENAI_MODEL: Model to use (default: gpt-3.5-turbo)
• RPM_LIMIT: Rate limit for OpenAI API calls
• REDIS_URL: Redis connection URL
• DATABASE_URL: PostgreSQL connection URL
• DEBUG: Enable debug mode

Project Structure

• app/: Application code
• tests/: Test code
• docker/: Dockerfiles
• compose.yml: Docker Compose file
• alembic.ini: Alembic configuration
• app/migrations/: Database migrations
• app/config.py: Configuration settings
• app/models/: Database models
• app/services/: Service code
• app/main.py: Main application entry point
• app/worker.py: Worker entry point