agentscope.models.dashscope_model module
Model wrapper for DashScope models
- class DashScopeChatWrapper(config_name: str, model_name: str | None = None, api_key: str | None = None, stream: bool = False, generate_args: dict | None = None, **kwargs: Any)[source]
Bases:
DashScopeWrapperBaseThe model wrapper for DashScope’s chat API, refer to https://help.aliyun.com/zh/dashscope/developer-reference/api-details
- Example Response:
Refer to
{ "status_code": 200, "request_id": "a75a1b22-e512-957d-891b-37db858ae738", "code": "", "message": "", "output": { "text": null, "finish_reason": null, "choices": [ { "finish_reason": "stop", "message": { "role": "assistant", "content": "xxx" } } ] }, "usage": { "input_tokens": 25, "output_tokens": 77, "total_tokens": 102 } }
- format(*args: Msg | Sequence[Msg]) List[dict][source]
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 = 'dashscope_chat'
The type of the model wrapper, which is to identify the model wrapper class in model configuration.
- class DashScopeImageSynthesisWrapper(config_name: str, model_name: str | None = None, api_key: str | None = None, generate_args: dict | None = None, **kwargs: Any)[source]
Bases:
DashScopeWrapperBaseThe model wrapper for DashScope Image Synthesis API, refer to https://help.aliyun.com/zh/dashscope/developer-reference/quick-start-1
- Response:
Refer to
{ "status_code": 200, "request_id": "b54ffeb8-6212-9dac-808c-b3771cba3788", "code": null, "message": "", "output": { "task_id": "996523eb-034d-459b-ac88-b340b95007a4", "task_status": "SUCCEEDED", "results": [ { "url": "RESULT_URL1" }, { "url": "RESULT_URL2" }, ], "task_metrics": { "TOTAL": 2, "SUCCEEDED": 2, "FAILED": 0 } }, "usage": { "image_count": 2 } }
- 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 = 'dashscope_image_synthesis'
The type of the model wrapper, which is to identify the model wrapper class in model configuration.
- class DashScopeMultiModalWrapper(config_name: str, model_name: str | None = None, api_key: str | None = None, generate_args: dict | None = None, **kwargs: Any)[source]
Bases:
DashScopeWrapperBaseThe model wrapper for DashScope Multimodal API, refer to https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-qianwen-vl-api
- Response:
Refer to
{ "status_code": 200, "request_id": "a0dc436c-2ee7-93e0-9667-c462009dec4d", "code": "", "message": "", "output": { "text": null, "finish_reason": null, "choices": [ { "finish_reason": "stop", "message": { "role": "assistant", "content": [ { "text": "这张图片显..." } ] } } ] }, "usage": { "input_tokens": 1277, "output_tokens": 81, "image_tokens": 1247 } }
- convert_url(url: str | Sequence[str] | None) List[dict][source]
Convert the url to the format of DashScope API. Note for local files, a prefix “file://” will be added.
- Parameters:
url (Union[str, Sequence[str], None]) – A string of url of a list of urls to be converted.
- Returns:
A list of dictionaries with key as the type of the url and value as the url. Only “image” and “audio” are supported.
- Return type:
List[dict]
- format(*args: Msg | Sequence[Msg]) List[source]
Format the messages for DashScope Multimodal API.
The multimodal API has the following requirements:
The roles of messages must alternate between “user” and “assistant”.
The message with the role “system” should be the first message
in the list.
If the system message exists, then the second message must
have the role “user”.
The last message in the list should have the role “user”.
In each message, more than one figure is allowed.
With the above requirements, we format the messages as follows:
If the first message is a system message, then we will keep it as
system prompt.
We merge all messages into a conversation history prompt in a
single message with the role “user”. - When there are multiple figures in the given messages, we will
attach it to the user message by order. Note if there are multiple figures, this strategy may cause misunderstanding for the model. For advanced solutions, developers are encouraged to implement their own prompt engineering strategies.
The following is an example:
prompt = model.format( Msg( "system", "You're a helpful assistant", role="system", url="figure1" ), Msg( "Bob", "How about this picture?", role="assistant", url="figure2" ), Msg( "user", "It's wonderful! How about mine?", role="user", image="figure3" ) )
The prompt will be as follows:
[ { "role": "system", "content": [ {"text": "You are a helpful assistant"}, {"image": "figure1"} ] }, { "role": "user", "content": [ {"image": "figure2"}, {"image": "figure3"}, { "text": ( "## Conversation History\n" "Bob: How about this picture?\n" "user: It's wonderful! How about mine?" ) }, ] } ]
Note
In multimodal API, the url of local files should be prefixed with “file://”, which will be attached in this format function.
- 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 = 'dashscope_multimodal'
The type of the model wrapper, which is to identify the model wrapper class in model configuration.
- class DashScopeTextEmbeddingWrapper(config_name: str, model_name: str | None = None, api_key: str | None = None, generate_args: dict | None = None, **kwargs: Any)[source]
Bases:
DashScopeWrapperBaseThe model wrapper for DashScope Text Embedding API.
- Response:
Refer to
{ "status_code": 200, // 200 indicate success otherwise failed. "request_id": "fd564688-43f7-9595-b986", // The request id. "code": "", // If failed, the error code. "message": "", // If failed, the error message. "output": { "embeddings": [ // embeddings { "embedding": [ // one embedding output -3.8450357913970947, ..., ], "text_index": 0 // the input index. } ] }, "usage": { "total_tokens": 3 // the request tokens. } }
- 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 = 'dashscope_text_embedding'
The type of the model wrapper, which is to identify the model wrapper class in model configuration.
- class DashScopeWrapperBase(config_name: str, model_name: str | None = None, api_key: str | None = None, generate_args: dict | None = None, **kwargs: Any)[source]
Bases:
ModelWrapperBase,ABCThe model wrapper for DashScope API.