This project provides a Flask-based API for document embedding and chat functionality. It allows users to upload documents, generate embeddings, and interact with a chat system based on those embeddings.

Before you begin, ensure you have met the following requirements:

• You have a working Python environment (3.7+)
• You have Conda installed on your system

1. Create a new Conda environment:

   conda create -n chat-api python=3.8
   

2. Activate the environment:

   conda activate chat-api
   

3. Install the required packages:

   pip install -r requirements.txt
   

Set the following environment variables or update them in the script:

• JWT_SECRET_KEY: Secret key for JWT token generation (default: 'chatwithme')
• CHAT_URL: URL for the chat service (default: 'http://localhost:8000/query')
• MASTER_API_KEY: Master API key for authentication (default: '1234567890')

To run the application, execute:

python flask_server_docker.py

The server will start on port 5996 with SSL enabled. Make sure you have the certificate.crt and private.key files in the same directory.

• /get_temp_api_key (POST): Get a temporary API key
• /api/start_chat (POST): Start a chat session
• /api/chat (POST): Send a chat message
• /make_embedding (POST): Generate embeddings for uploaded documents
• /start_chat (POST): Initiate a chat session
• /chat (POST): Send a chat message (alternative endpoint)

This application uses JWT for authentication and HTTPS for secure communication. Ensure to keep your secret keys and SSL certificates secure.

The embeddings.py file contains functions for generating embeddings from documents and converting document objects to dictionaries.

1. get_embeddings(directory): Generates embeddings for documents in the specified directory.
2. document_to_dict(doc): Converts a Document object to a dictionary.
3. get_documents(directory): Loads documents from a directory and converts them to a list of dictionaries.

The api_test.ipynb Jupyter notebook demonstrates how to interact with the API endpoints. Here's a breakdown of the notebook:

1. Initiating a chat session:

   • Makes a POST request to the /api/start_chat endpoint with an API key.
   • Retrieves a JWT token and model URL from the response.

2. Sending a chat message:

   • Uses the JWT token to authenticate a POST request to the /api/chat endpoint.
   • Sends a query and receives a response from the chat model.

import requests

# Start chat session
api_key = "your_api_key_here"
start_chat_url = "https://your-api-url.com/api/start_chat"
response = requests.post(start_chat_url, headers={"X-API-Key": api_key})
jwt_token = response.json()["jwt_token"]
model_url = response.json()["url"]

# Send chat message
chat_url = "https://your-api-url.com/api/chat"
query = "Your question here"
response = requests.post(
    chat_url,
    headers={"Authorization": jwt_token},
    json={"query": query, "url": model_url}
)

print(response.json()["query"])
print(response.json()["answer"])

This is a development server. For production deployment, consider using a production-grade WSGI server and implement proper security measures.