zeebee-ai-client 0.1.6

Python SDK for the ZeebeeAI Chat Platform

ZeebeeAI Python SDK

The ZeebeeAI Python client library provides a simple and powerful way to integrate AI functionality into your Python applications, including multi-model support, agent orchestration, and advanced routing capabilities.

Features

• AI Chat: Access to various language models including GPT-4o through a unified API
• Voice Chat: Real-time bidirectional voice chat with WebSocket support
• Dynamic Layout Engine: Intelligently generate appropriate layouts for content based on message complexity
• Agent Orchestration: Create, configure, and execute specialized AI agents for specific tasks
• Autonomous Routing: Intelligent routing of user messages to appropriate agents, pipelines, or models
• Conversation Management: Tools for handling conversation history and context

Installation

pip install zeebee-ai-client

Basic Usage - AI Chat

import os
from zeebee_ai_client import ZeebeeClient

# Initialize the client
client = ZeebeeClient(
    api_key=os.environ.get("ZEEBEE_API_KEY"),  # Replace with your API key
)

# Send a simple message
response = client.chat("Hello, how can you help me today?")
print(response["message"])

# Or create a conversation for multi-turn interactions
conversation = client.create_conversation()
conversation_id = conversation["id"]
response = client.chat("Tell me about quantum computing", conversation_id=conversation_id)
print(response["message"])

# Continue the conversation
follow_up = client.chat("How is that related to cryptography?", conversation_id=conversation_id)
print(follow_up["message"])

Voice Chat

import os
import asyncio
import soundfile as sf  # pip install soundfile
import sounddevice as sd  # pip install sounddevice
from zeebee_ai_client import WebSocketVoiceChat

# Example callback functions
def on_transcript(text):
    """Called when your audio is transcribed to text"""
    print(f"\n🎤 You said: {text}")

def on_response(text):
    """Called when AI responds with text"""
    print(f"\n🤖 AI response: {text}")

def on_audio(audio_data):
    """Called when AI responds with audio"""
    # Save the audio to a file
    with open("ai_response.wav", "wb") as f:
        f.write(audio_data)

    # If you have soundfile/sounddevice installed, play the audio
    try:
        # Save temporarily and play
        with open("temp_response.wav", "wb") as f:
            f.write(audio_data)

        # Load and play audio
        data, samplerate = sf.read("temp_response.wav")
        sd.play(data, samplerate)
        sd.wait()  # Wait until audio is finished playing
        print(f"🔊 Playing audio response ({len(audio_data)} bytes)")
    except Exception as e:
        print(f"Could not play audio: {e}")
        print(f"Audio saved to ai_response.wav ({len(audio_data)} bytes)")

def on_error(error_message):
    """Called when an error occurs"""
    print(f"\n❌ Error: {error_message}")

def on_status(status):
    """Called when connection status changes"""
    status_symbols = {
        "connecting": "🔄",
        "connected": "🟢",
        "ready": "✅",
        "processing": "⏳",
        "disconnected": "🔴",
        "reconnecting": "🔁"
    }
    symbol = status_symbols.get(status, "ℹ️")
    print(f"{symbol} Status: {status}")

async def main():
    # Initialize the WebSocket voice chat client
    voice_chat = WebSocketVoiceChat(
        api_key=os.environ.get("ZEEBEE_API_KEY")
    )

    # Create a voice chat session with our callback functions
    session = voice_chat.create_session(
        model="gpt-4o",  # AI model to use
        on_transcript=on_transcript,
        on_response=on_response,
        on_audio=on_audio,
        on_error=on_error,
        on_status=on_status
    )

    # Connect to the WebSocket server
    connected = await session.connect()
    if not connected:
        print("Failed to connect to voice chat server")
        return

    # Send an audio file to the server
    audio_file_path = "recording.wav"  # Path to your WAV audio file

    print(f"\nSending audio file: {audio_file_path}")
    success = await session.send_audio(audio_file_path)

    if success:
        print("Audio sent successfully")
    else:
        print("Failed to send audio")

    # Wait for the interaction to complete
    print("Waiting for response...")
    await asyncio.sleep(30)

    # Disconnect when done
    await session.disconnect()
    await voice_chat.close_all_sessions()
    print("\nSession ended")

# Run the async main function
if __name__ == "__main__":
    asyncio.run(main())

Dynamic Layout Engine

from zeebee_ai_client import ZeebeeClient, LayoutController, LayoutType
import os

# Initialize the SDK
client = ZeebeeClient(api_key=os.environ.get("ZEEBEE_API_KEY"))

# Initialize the layout controller for layout generation
layout = LayoutController(client)

# Generate a dynamic layout for tabular data
table_layout = layout.generate_layout(
    message="Create a table comparing Python, JavaScript, and Java programming languages",
    routing_result={
        "suggested_template": LayoutType.TABLE_LAYOUT,
        "content_analysis": {
            "contentTypes": ["table", "comparison"],
            "complexity": "medium",
            "formality": "formal"
        }
    }
)

print(f"Generated layout template: {table_layout['layout']['template']}")
print(f"Layout type: {table_layout['layout']['type']}")
print(f"Number of components: {len(table_layout['layout']['components'])}")
print(f"Content analysis: {table_layout['layout']['content_analysis']}")

Agent Orchestration

from zeebee_ai_client import ZeebeeClient, AgentController, AgentTypes, PipelineController
import os

# Initialize the SDK
client = ZeebeeClient(api_key=os.environ.get("ZEEBEE_API_KEY"))

# Initialize the controllers
agents = AgentController(client)
pipelines = PipelineController(client)

# Create a specialized research agent
research_agent = agents.create_agent(
    name="Research Agent",
    agent_type=AgentTypes.RETRIEVAL,
    configuration={
        "description": "Retrieves and analyzes research information",
        "system_prompt": "You are a specialized research assistant. Provide detailed, accurate information with sources."
    },
    description="A specialized assistant for research tasks",
    model_id="gpt-4o"
)

# Create a summarization agent
summarize_agent = agents.create_agent(
    name="Summarize Agent",
    agent_type=AgentTypes.SUMMARIZATION,
    configuration={
        "system_prompt": "You are a summarization expert. Create concise, accurate summaries of complex information."
    },
    description="An agent that summarizes complex information",
    model_id="gpt-4o"
)

# Create a pipeline of multiple agents
pipeline = pipelines.create_pipeline(
    name="Research and Summarize",
    stages=[
        {
            "agent_id": research_agent["agent_id"],
            "name": "Research Stage",
            "input_mapping": {"query": "$.input.research_topic"},
            "output_mapping": {"research_results": "$.output.result"}
        },
        {
            "agent_id": summarize_agent["agent_id"],
            "name": "Summarize Stage",
            "input_mapping": {"text": "$.stages.Research Stage.research_results"},
            "output_mapping": {"summary": "$.output.result"}
        }
    ],
    description="Pipeline that researches a topic and summarizes the findings"
)

# Execute the pipeline
pipeline_result = pipelines.execute_pipeline(
    pipeline_id=pipeline["pipeline_id"],
    input_data={"research_topic": "Recent breakthroughs in quantum computing"}
)

print(f"Pipeline results: {pipeline_result}")

Autonomous Routing

from zeebee_ai_client import ZeebeeClient, RoutingController
import os

# Initialize the SDK
client = ZeebeeClient(api_key=os.environ.get("ZEEBEE_API_KEY"))

# Initialize the routing controller
routing = RoutingController(client)

# Route a message to the appropriate agent, pipeline, or model
routing_result = routing.route_message(
    message="Can you analyze this data and create a visualization?"
)

print(f"Routing to: {routing_result['route_to']} ({routing_result['route_type']})")
print(f"Confidence: {routing_result['confidence']}")
print(f"Intent information: {routing_result['intent']}")
print(f"Reasoning: {routing_result['reasoning']}")

Error Handling

The SDK includes custom exceptions for different error types:

from zeebee_ai_client import (
    AuthenticationError,
    RateLimitError,
    AgentException,
    PipelineException
)

try:
    response = client.chat(message="Hello")
except AuthenticationError as e:
    print(f"Authentication error: {e}")
except RateLimitError as e:
    print(f"Rate limit exceeded: {e}")
except AgentException as e:
    print(f"Agent error: {e}")
except PipelineException as e:
    print(f"Pipeline error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Compatibility

The ZeebeeAI Python SDK requires Python 3.8 or higher.

Documentation

For more detailed information and examples, visit the official ZeebeeAI documentation.

License

This SDK is licensed under the MIT License.