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Add support of tool-call for model provider "hunyuan" (#6656)
Co-authored-by: sun <sun@centen.cn>
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@ -14,6 +14,7 @@ from core.model_runtime.entities.message_entities import (
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PromptMessage,
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PromptMessageTool,
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SystemPromptMessage,
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ToolPromptMessage,
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UserPromptMessage,
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)
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from core.model_runtime.errors.invoke import InvokeError
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@ -44,6 +45,17 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
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"Stream": stream,
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**custom_parameters,
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}
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# add Tools and ToolChoice
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if (tools and len(tools) > 0):
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params['ToolChoice'] = "auto"
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params['Tools'] = [{
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"Type": "function",
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"Function": {
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"Name": tool.name,
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"Description": tool.description,
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"Parameters": json.dumps(tool.parameters)
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}
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} for tool in tools]
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request.from_json_string(json.dumps(params))
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response = client.ChatCompletions(request)
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@ -89,9 +101,43 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
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def _convert_prompt_messages_to_dicts(self, prompt_messages: list[PromptMessage]) -> list[dict]:
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"""Convert a list of PromptMessage objects to a list of dictionaries with 'Role' and 'Content' keys."""
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return [{"Role": message.role.value, "Content": message.content} for message in prompt_messages]
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dict_list = []
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for message in prompt_messages:
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if isinstance(message, AssistantPromptMessage):
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tool_calls = message.tool_calls
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if (tool_calls and len(tool_calls) > 0):
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dict_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 if (tool_call.function.arguments == "") else "{}"
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}
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} for tool_call in tool_calls]
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dict_list.append({
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"Role": message.role.value,
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# fix set content = "" while tool_call request
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# fix [hunyuan] None, [TencentCloudSDKException] code:InvalidParameter message:Messages Content and Contents not allowed empty at the same time.
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"Content": " ", # message.content if (message.content is not None) else "",
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"ToolCalls": dict_tool_calls
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})
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else:
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dict_list.append({ "Role": message.role.value, "Content": message.content })
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elif isinstance(message, ToolPromptMessage):
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tool_execute_result = { "result": message.content }
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content =json.dumps(tool_execute_result, ensure_ascii=False)
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dict_list.append({ "Role": message.role.value, "Content": content, "ToolCallId": message.tool_call_id })
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else:
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dict_list.append({ "Role": message.role.value, "Content": message.content })
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return dict_list
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def _handle_stream_chat_response(self, model, credentials, prompt_messages, resp):
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tool_call = None
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tool_calls = []
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for index, event in enumerate(resp):
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logging.debug("_handle_stream_chat_response, event: %s", event)
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@ -109,20 +155,54 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
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usage = data.get('Usage', {})
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prompt_tokens = usage.get('PromptTokens', 0)
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completion_tokens = usage.get('CompletionTokens', 0)
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usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
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response_tool_calls = delta.get('ToolCalls')
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if (response_tool_calls is not None):
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new_tool_calls = self._extract_response_tool_calls(response_tool_calls)
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if (len(new_tool_calls) > 0):
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new_tool_call = new_tool_calls[0]
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if (tool_call is None): tool_call = new_tool_call
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elif (tool_call.id != new_tool_call.id):
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tool_calls.append(tool_call)
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tool_call = new_tool_call
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else:
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tool_call.function.name += new_tool_call.function.name
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tool_call.function.arguments += new_tool_call.function.arguments
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if (tool_call is not None and len(tool_call.function.name) > 0 and len(tool_call.function.arguments) > 0):
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tool_calls.append(tool_call)
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tool_call = None
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assistant_prompt_message = AssistantPromptMessage(
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content=message_content,
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tool_calls=[]
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)
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# rewrite content = "" while tool_call to avoid show content on web page
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if (len(tool_calls) > 0): assistant_prompt_message.content = ""
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# add tool_calls to assistant_prompt_message
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if (finish_reason == 'tool_calls'):
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assistant_prompt_message.tool_calls = tool_calls
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tool_call = None
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tool_calls = []
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delta_chunk = LLMResultChunkDelta(
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index=index,
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role=delta.get('Role', 'assistant'),
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message=assistant_prompt_message,
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usage=usage,
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finish_reason=finish_reason,
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)
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if (len(finish_reason) > 0):
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usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
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delta_chunk = LLMResultChunkDelta(
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index=index,
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role=delta.get('Role', 'assistant'),
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message=assistant_prompt_message,
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usage=usage,
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finish_reason=finish_reason,
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)
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tool_call = None
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tool_calls = []
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else:
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delta_chunk = LLMResultChunkDelta(
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index=index,
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message=assistant_prompt_message,
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)
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yield LLMResultChunk(
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model=model,
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@ -177,12 +257,15 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
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"""
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human_prompt = "\n\nHuman:"
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ai_prompt = "\n\nAssistant:"
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tool_prompt = "\n\nTool:"
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content = message.content
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if isinstance(message, UserPromptMessage):
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message_text = f"{human_prompt} {content}"
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elif isinstance(message, AssistantPromptMessage):
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message_text = f"{ai_prompt} {content}"
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elif isinstance(message, ToolPromptMessage):
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message_text = f"{tool_prompt} {content}"
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elif isinstance(message, SystemPromptMessage):
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message_text = content
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else:
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@ -203,3 +286,30 @@ class HunyuanLargeLanguageModel(LargeLanguageModel):
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return {
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InvokeError: [TencentCloudSDKException],
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}
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def _extract_response_tool_calls(self,
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response_tool_calls: list[dict]) \
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-> list[AssistantPromptMessage.ToolCall]:
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"""
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Extract tool calls from response
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:param response_tool_calls: response tool calls
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:return: list of tool calls
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"""
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tool_calls = []
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if response_tool_calls:
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for response_tool_call in response_tool_calls:
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response_function = response_tool_call.get('Function', {})
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function = AssistantPromptMessage.ToolCall.ToolCallFunction(
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name=response_function.get('Name', ''),
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arguments=response_function.get('Arguments', '')
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)
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tool_call = AssistantPromptMessage.ToolCall(
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id=response_tool_call.get('Id', 0),
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type='function',
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function=function
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)
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tool_calls.append(tool_call)
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return tool_calls
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