Chain of Thought: Personalized Message Generation API

Objective

This project implements a FastAPI-based application that generates personalized messages mimicking the style, tone, and structure of specific users. It uses OpenAI's GPT model model to create context-aware messages based on historical data and new input signals.

The main goals of this API are:

1. Use Chain of Thought to analyze input contex/signal and intent category to generated key point a message should address.
2. Incorporate new context (signals) into the generated messages.
3. Generate messages according to the key point and historical messages.

Chain of thought example:

# Thought process:
# 1. Analyze the signal: The signal provides context for {signal_brief}.
# 2. Consider the category: The category suggests {category_brief}.
# 3. Identify key points to address in Signal based on intent of Category: Based on the signal and intent category, the key points to address are {message_key_point}.
# 4. Write the message: Consider Singal, Category, Signal Key points and write the message

### Message: "{message}"

API Details

Endpoint

POST /generate_message

This endpoint generates a new message based on the provided input.

Input Schema

The API accepts the following JSON input:

{
  "sender": "string",
  "receiver": "string",
  "category": "string",
  "signal": "string",
  "generate_from_best": "boolean (optional, default: false), when samples are not available for the given sender"
}

• sender: The name of the user who composed the message.
• receiver: The name of the user who receives the message.
• category: The detected signal category.
• signal: The content of the signal.
• generate_from_best: If true, the API will use a (ideally best) random user's data when the specified sender is not found.

Output Schema

The API returns the following JSON output:

{
  "message_body": "string",
  "reference_message": "string (optional)",
  "status": "string (optional)"
}

• message_body: The generated message.
• reference_message: A sample message used as a reference for generation.
• status: The status of the message generation process.

Setup and Running

1. Install the required dependencies:

   pip install -r requirements.txt
   

2. Set OpenAI API key:

   export OPENAI_API_KEY=your_api_key_here
   

3. Ensure that the sample_data.csv file is present in the project data directory.

4. Run the FastAPI application:

   python main.py
   

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

5. You can interact with the API using the Swagger UI at http://localhost:8000/docs.