AI Reply Service

A Python-based AI service for generating contextual replies using LangGraph workflows.

Features

• User Summary Workflow: Analyzes user data to generate keyword profiles and embeddings
• Reply Generation Workflow: Three-step process for intelligent reply generation
  1. Intent Analysis (GPT-4)
  2. Content Discovery (GPT-4)
  3. Reply Generation (GPT-3.5)
• Embeddings Workflow: Generates text embeddings with optional preprocessing

Setup

1. Install Poetry (package manager):

curl -sSL https://install.python-poetry.org | python3 -

2. Install dependencies:

poetry install

3. Create a .env file with your OpenAI API key:

OPENAI_API_KEY=your_api_key_here

Usage

Running the Example Script

poetry run python example.py

This will demonstrate all three workflows with sample data.

Starting the API Server

poetry run uvicorn app.main:app --reload

The API will be available at http://localhost:8000.

API Endpoints

1. User Summary

POST /user-summary
{
    "user_data": {
        "username": "string",
        "bio": "string",
        "recent_casts": ["string"],
        "interests": ["string"],
        "engagement_stats": {
            "avg_replies": number,
            "avg_likes": number,
            "top_channels": ["string"]
        }
    }
}

2. Reply Generation

POST /generate-reply
{
    "cast_text": "string",
    "available_feeds": [
        {
            "text": "string",
            "url": "string",
            "author": "string",
            "timestamp": "string"
        }
    ]
}

3. Embeddings Generation

POST /generate-embeddings
{
    "input_data": {
        "title": "string",
        "content": "string",
        "tags": ["string"]
    }
}

Project Structure

.
├── app/
│   ├── __init__.py
│   ├── main.py              # FastAPI application
│   ├── nodes.py             # LangGraph node implementations
│   ├── prompts.py           # Centralized prompt management
│   └── workflows/           # Workflow implementations
│       ├── __init__.py
│       ├── user_summary.py
│       ├── reply_generation.py
│       └── embeddings.py
├── example.py               # Example usage script
├── pyproject.toml          # Poetry configuration
└── README.md

Development

• Uses Poetry for dependency management
• FastAPI for the REST API
• LangGraph for workflow orchestration
• OpenAI's GPT-4 and GPT-3.5 for different processing steps
• text-embedding-3-small for embeddings generation

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request