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Create Chat

Create a new chat instance to interact with AI models using Python.

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Constructor

Chat(broker: ZGComputeBroker, provider_address: str, temperature: float = 0.7, max_tokens: int = 1000)

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Parameters

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broker
ZGComputeBroker
required
The compute broker instance for connecting to the 0G network
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provider_address
str
required
The provider address on the 0G network for the AI model
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temperature
float
default:"0.7"
Controls randomness in responses (0.0-2.0)
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max_tokens
int
default:"1000"
Maximum number of tokens in the response

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Response

Returns a Chat instance that can be used to send messages and stream responses.

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Example

import asyncio
from zg_ai_sdk import create_agent

async def main():
    agent = await create_agent({
        'name': 'My Assistant',
        'provider_address': '0xf07240Efa67755B5311bc75784a061eDB47165Dd',  # llama-3.3-70b-instruct
        'memory_bucket': 'my-agent-memory',
        'private_key': 'your-private-key',
        'max_tokens': 2000,
        'temperature': 0.8
    })
    
    response = await agent.ask('Hello, how are you?')
    print(response)

asyncio.run(main())

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Available Models

The 0G AI SDK connects to models running on the 0G decentralized compute network:
ModelProvider AddressDescriptionVerification
llama-3.3-70b-instruct0xf07240Efa67755B5311bc75784a061eDB47165DdState-of-the-art 70B parameter model for general AI tasksTEE (TeeML)
deepseek-r1-70b0x3feE5a4dd5FDb8a32dDA97Bed899830605dBD9D3Advanced reasoning model optimized for complex problem solvingTEE (TeeML)

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Methods

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ask()

Send a simple question to the AI model. 
async def ask(self, question: str, system_prompt: Optional[str] = None) -> str
Parameters:
  • question (str): The question to ask the AI
  • system_prompt (Optional[str]): Optional system prompt to set context
Returns: str - The AI’s response Example: 
response = await chat.ask('What is machine learning?')
print(response)

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chat_completion()

Send a structured chat completion request. 
async def chat_completion(self, messages: List[ChatMessage]) -> ChatCompletionResponse
Parameters:
  • messages (List[ChatMessage]): List of chat messages
Returns: ChatCompletionResponse - Structured response with usage info Example: 
from zg_ai_sdk import ChatMessage

messages = [
    ChatMessage(role='system', content='You are a helpful assistant'),
    ChatMessage(role='user', content='Explain quantum computing')
]

response = await chat.chat_completion(messages)
print(response.choices[0].message.content)

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stream_chat_completion()

Stream responses for real-time chat. 
async def stream_chat_completion(
    self, 
    messages: List[ChatMessage], 
    on_chunk: callable
) -> str
Parameters:
  • messages (List[ChatMessage]): List of chat messages
  • on_chunk (callable): Callback function for each chunk
Returns: str - Complete response text Example: 
def handle_chunk(chunk: str):
    print(chunk, end='', flush=True)

messages = [ChatMessage(role='user', content='Tell me a story')]
full_response = await chat.stream_chat_completion(messages, handle_chunk)

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get_service_info()

Get information about the AI service. 
async def get_service_info(self) -> ServiceMetadata
Returns: ServiceMetadata - Service information including endpoint and model Example: 
info = await chat.get_service_info()
print(f"Model: {info.model}, Endpoint: {info.endpoint}")

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Configuration Methods

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set_temperature()

Update the temperature setting. 
def set_temperature(self, temperature: float) -> None
Parameters:
  • temperature (float): New temperature value (0.0-2.0)
Example: 
chat.set_temperature(0.9)  # More creative responses

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set_max_tokens()

Update the maximum tokens setting. 
def set_max_tokens(self, max_tokens: int) -> None
Parameters:
  • max_tokens (int): New maximum tokens value
Example: 
chat.set_max_tokens(2000)  # Allow longer responses

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get_config()

Get current configuration. 
def get_config(self) -> Dict[str, Any]
Returns: Dict[str, Any] - Current configuration settings Example: 
config = chat.get_config()
print(f"Temperature: {config['temperature']}, Max Tokens: {config['max_tokens']}")

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Error Handling

The constructor and methods will raise exceptions if:
  • Invalid provider address is provided
  • Network connection fails
  • Invalid configuration parameters are passed
from zg_ai_sdk import SDKError

try:
    chat = Chat(
        broker=broker,
        provider_address='invalid-address',
        temperature=0.7
    )
    response = await chat.ask('Hello')
except SDKError as error:
    print(f'Failed to create chat: {error.message} (Code: {error.code})')
except Exception as error:
    print(f'Unexpected error: {error}')

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Next Steps