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chore: improve zhipu LLM (#11321)
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@ -8,6 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 131072
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -8,6 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 131072
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -8,6 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 8192
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -8,6 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 131072
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -8,6 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 131072
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -8,6 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 131072
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -8,6 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 131072
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -8,7 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 10240
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context_size: 1048576
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -8,6 +8,7 @@ features:
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- stream-tool-call
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model_properties:
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mode: chat
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context_size: 131072
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parameter_rules:
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- name: temperature
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use_template: temperature
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@ -4,6 +4,7 @@ label:
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model_type: llm
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model_properties:
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mode: chat
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context_size: 2048
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features:
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- vision
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parameter_rules:
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@ -4,6 +4,7 @@ label:
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model_type: llm
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model_properties:
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mode: chat
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context_size: 8192
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features:
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- vision
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- video
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@ -22,18 +22,6 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
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from core.model_runtime.model_providers.zhipuai._common import _CommonZhipuaiAI
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from core.model_runtime.utils import helper
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GLM_JSON_MODE_PROMPT = """You should always follow the instructions and output a valid JSON object.
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The structure of the JSON object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
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if you are not sure about the structure.
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And you should always end the block with a "```" to indicate the end of the JSON object.
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<instructions>
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{{instructions}}
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</instructions>
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```JSON""" # noqa: E501
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class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
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def _invoke(
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@ -64,42 +52,8 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
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credentials_kwargs = self._to_credential_kwargs(credentials)
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# invoke model
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# stop = stop or []
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# self._transform_json_prompts(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
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return self._generate(model, credentials_kwargs, prompt_messages, model_parameters, tools, stop, stream, user)
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# def _transform_json_prompts(self, model: str, credentials: dict,
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# prompt_messages: list[PromptMessage], model_parameters: dict,
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# tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
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# stream: bool = True, user: str | None = None) \
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# -> None:
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# """
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# Transform json prompts to model prompts
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# """
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# if "}\n\n" not in stop:
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# stop.append("}\n\n")
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# # check if there is a system message
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# if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
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# # override the system message
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# prompt_messages[0] = SystemPromptMessage(
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# content=GLM_JSON_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content)
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# )
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# else:
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# # insert the system message
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# prompt_messages.insert(0, SystemPromptMessage(
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# content=GLM_JSON_MODE_PROMPT.replace("{{instructions}}", "Please output a valid JSON object.")
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# ))
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# # check if the last message is a user message
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# if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
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# # add ```JSON\n to the last message
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# prompt_messages[-1].content += "\n```JSON\n"
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# else:
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# # append a user message
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# prompt_messages.append(UserPromptMessage(
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# content="```JSON\n"
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# ))
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def get_num_tokens(
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self,
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model: str,
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@ -170,7 +124,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
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:return: full response or stream response chunk generator result
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"""
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extra_model_kwargs = {}
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# request to glm-4v-plus with stop words will always response "finish_reason":"network_error"
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# request to glm-4v-plus with stop words will always respond "finish_reason":"network_error"
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if stop and model != "glm-4v-plus":
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extra_model_kwargs["stop"] = stop
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@ -186,7 +140,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
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# resolve zhipuai model not support system message and user message, assistant message must be in sequence
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new_prompt_messages: list[PromptMessage] = []
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for prompt_message in prompt_messages:
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copy_prompt_message = prompt_message.copy()
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copy_prompt_message = prompt_message.model_copy()
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if copy_prompt_message.role in {PromptMessageRole.USER, PromptMessageRole.SYSTEM, PromptMessageRole.TOOL}:
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if isinstance(copy_prompt_message.content, list):
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# check if model is 'glm-4v'
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@ -238,59 +192,38 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
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params = self._construct_glm_4v_parameter(model, new_prompt_messages, model_parameters)
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else:
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params = {"model": model, "messages": [], **model_parameters}
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# glm model
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if not model.startswith("chatglm"):
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for prompt_message in new_prompt_messages:
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if prompt_message.role == PromptMessageRole.TOOL:
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for prompt_message in new_prompt_messages:
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if prompt_message.role == PromptMessageRole.TOOL:
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params["messages"].append(
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{
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"role": "tool",
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"content": prompt_message.content,
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"tool_call_id": prompt_message.tool_call_id,
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}
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)
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elif isinstance(prompt_message, AssistantPromptMessage):
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if prompt_message.tool_calls:
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params["messages"].append(
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{
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"role": "tool",
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"role": "assistant",
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"content": prompt_message.content,
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"tool_call_id": prompt_message.tool_call_id,
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"tool_calls": [
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{
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"id": tool_call.id,
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"type": tool_call.type,
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"function": {
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"name": tool_call.function.name,
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"arguments": tool_call.function.arguments,
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},
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}
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for tool_call in prompt_message.tool_calls
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],
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}
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)
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elif isinstance(prompt_message, AssistantPromptMessage):
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if prompt_message.tool_calls:
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params["messages"].append(
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{
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"role": "assistant",
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"content": prompt_message.content,
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"tool_calls": [
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{
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"id": tool_call.id,
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"type": tool_call.type,
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"function": {
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"name": tool_call.function.name,
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"arguments": tool_call.function.arguments,
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},
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}
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for tool_call in prompt_message.tool_calls
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],
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}
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)
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else:
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params["messages"].append({"role": "assistant", "content": prompt_message.content})
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else:
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params["messages"].append(
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{"role": prompt_message.role.value, "content": prompt_message.content}
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)
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else:
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# chatglm model
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for prompt_message in new_prompt_messages:
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# merge system message to user message
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if prompt_message.role in {
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PromptMessageRole.SYSTEM,
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PromptMessageRole.TOOL,
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PromptMessageRole.USER,
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}:
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if len(params["messages"]) > 0 and params["messages"][-1]["role"] == "user":
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params["messages"][-1]["content"] += "\n\n" + prompt_message.content
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else:
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params["messages"].append({"role": "user", "content": prompt_message.content})
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else:
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params["messages"].append(
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{"role": prompt_message.role.value, "content": prompt_message.content}
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)
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params["messages"].append({"role": "assistant", "content": prompt_message.content})
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else:
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params["messages"].append({"role": prompt_message.role.value, "content": prompt_message.content})
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if tools and len(tools) > 0:
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params["tools"] = [{"type": "function", "function": helper.dump_model(tool)} for tool in tools]
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@ -406,7 +339,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
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Handle llm stream response
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:param model: model name
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:param response: response
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:param responses: response
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:param prompt_messages: prompt messages
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:return: llm response chunk generator result
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"""
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@ -505,7 +438,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
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if tools and len(tools) > 0:
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text += "\n\nTools:"
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for tool in tools:
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text += f"\n{tool.json()}"
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text += f"\n{tool.model_dump_json()}"
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# trim off the trailing ' ' that might come from the "Assistant: "
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return text.rstrip()
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