kiss-ai-stack-server 0.1.0a15

KISS AI Stack's Server stub - Simplify AI Agent Development

KISS AI Stack - Server

The KISS AI Stack Server is an server stub designed to support RESTful and WebSocket APIs for handling AI-agent sessions with kiss-ai-stack-core tasks like agent lifecycle management, query execution, and document storage.

Features

• REST API for authentication, session actions, queries, and document storage.
• WebSocket API for real-time, event-driven interactions.
• Built-in persistent and temporary session management.
• Flexible architecture to handle server events though AI agent's lifecycle events.

---

Agent's session lifecycle Events

• ON_AUTH: Authenticate a session.
• ON_INIT: Initialize a session.
• ON_CLOSE: Close a session.
• ON_QUERY: Execute a query.
• ON_STORE: Store documents.

---

Getting Started

Requirements

• Python 3.12

Installation

1. Install kiss-ai-stack-server package:

   pip install kiss-ai-stack-server
   

2. Set environment variables file

   # .env
    ACCESS_TOKEN_SECRET_KEY = "your-secure-random-secret-key"
    ACCESS_TOKEN_ALGORITHM = "HS256"
    ACCESS_TOKEN_EXPIRE_MINUTES = 30
   
    SESSION_DB_URL="sqlite://sessions.db"
   

3. Run the server:

   from kiss_ai_stack_server import bootstrap_session_schema, agent_server
   

---

REST API Endpoints

1. Authentication

Endpoint: /auth
Method: POST
Request Body:

{
  "client_id": "string",
  "client_secret": "string"
}

Response:

{
  "session_id": "string",
  "access_token": "string",
  "expires_in": 3600
}

---

2. Session Actions

Endpoint: /sessions?action={init|close}
Method: POST
Query Parameter:

• action (required): Action to perform on the session (init or close).

Request Body:

{
  "session_id": "string"
}

Response: Session-related details or status.

---

3. Query Execution

Endpoint: /queries
Method: POST
Request Body:

{
  "query": "string",
  "parameters": {
    "key": "value"
  }
}

Response: Query results.

---

4. Document Storage

Endpoint: /documents
Method: POST
Request Body:

{
  "documents": [
    {
      "id": "string",
      "content": "string",
      "metadata": {}
    }
  ]
}

Response: Document storage confirmation.

---

WebSocket API

Endpoint: /ws

Workflow

1. Establish a WebSocket connection:

   ws://localhost:8080/ws
   

2. Send a message:

   {
     "event": "ON_QUERY",
     "data": {
       "query": "example query",
       "parameters": {
         "key": "value"
       }
     }
   }
   

3. Receive a response:

   {
     "event": "ON_QUERY",
     "result": {
       "response_key": "response_value"
     }
   }
   

Contributing

Contributions are welcome! Feel free to fork the repository and submit a pull request.

---

License

This project is licensed under the MIT License.