agentscope.models.zhipu_model module

Model wrapper for ZhipuAI models

class ZhipuAIChatWrapper(config_name: str, model_name: str | None = None, api_key: str | None = None, stream: bool = False, client_args: dict | None = None, generate_args: dict | None = None, **kwargs: Any)

Bases: ZhipuAIWrapperBase

The model wrapper for ZhipuAI’s chat API.

Response:

• From https://maas.aminer.cn/dev/api#glm-4

{
    "created": 1703487403,
    "id": "8239375684858666781",
    "model": "glm-4",
    "request_id": "8239375684858666781",
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": "Drawing blueprints with ...",
                "role": "assistant"
            }
        }
    ],
    "usage": {
        "completion_tokens": 217,
        "prompt_tokens": 31,
        "total_tokens": 248
    }
}

format(*args: Msg | Sequence[Msg]) → List[dict]

A common format strategy for chat models, which will format the input messages into a user message.

Note this strategy maybe not suitable for all scenarios, and developers are encouraged to implement their own prompt engineering strategies.

The following is an example:

prompt1 = model.format(
    Msg("system", "You're a helpful assistant", role="system"),
    Msg("Bob", "Hi, how can I help you?", role="assistant"),
    Msg("user", "What's the date today?", role="user")
)

prompt2 = model.format(
    Msg("Bob", "Hi, how can I help you?", role="assistant"),
    Msg("user", "What's the date today?", role="user")
)

The prompt will be as follows:

# prompt1
[
    {
        "role": "system",
        "content": "You're a helpful assistant"
    },
    {
        "role": "user",
        "content": (
            "## Conversation History\n"
            "Bob: Hi, how can I help you?\n"
            "user: What's the date today?"
        )
    }
]

# prompt2
[
    {
        "role": "user",
        "content": (
            "## Conversation History\n"
            "Bob: Hi, how can I help you?\n"
            "user: What's the date today?"
        )
    }
]

Parameters:

args (Union[Msg, Sequence[Msg]]) – The input arguments to be formatted, where each argument should be a Msg object, or a list of Msg objects. In distribution, placeholder is also allowed.

Returns:

The formatted messages.

Return type:

List[dict]

config_name: str

The name of the model configuration.

model_name: str

The name of the model, which is used in model api calling.

model_type: str = 'zhipuai_chat'

The type of the model wrapper, which is to identify the model wrapper class in model configuration.

class ZhipuAIEmbeddingWrapper(config_name: str, model_name: str | None = None, api_key: str | None = None, client_args: dict | None = None, generate_args: dict | None = None, **kwargs: Any)

Bases: ZhipuAIWrapperBase

The model wrapper for ZhipuAI embedding API.

Example Response:

{
    "model": "embedding-2",
    "data": [
        {
            "embedding": [ (a total of 1024 elements)
                -0.02675454691052437,
                0.019060475751757622,
                ......
                -0.005519774276763201,
                0.014949671924114227
            ],
            "index": 0,
            "object": "embedding"
        }
    ],
    "object": "list",
    "usage": {
        "completion_tokens": 0,
        "prompt_tokens": 4,
        "total_tokens": 4
    }
}

config_name: str

The name of the model configuration.

model_name: str

The name of the model, which is used in model api calling.

model_type: str = 'zhipuai_embedding'

The type of the model wrapper, which is to identify the model wrapper class in model configuration.

class ZhipuAIWrapperBase(config_name: str, model_name: str | None = None, api_key: str | None = None, client_args: dict | None = None, generate_args: dict | None = None, **kwargs: Any)

Bases: ModelWrapperBase, ABC

The model wrapper for ZhipuAI API.

format(*args: Msg | Sequence[Msg]) → List[dict] | str

Format the input messages into the format that the model API required.