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Feat/open ai compatible functioncall (#2783)
Co-authored-by: jyong <jyong@dify.ai>
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Jyong
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@ -472,7 +472,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
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else:
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raise ValueError(f"Got unknown type {message}")
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if message.name is not None:
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if message.name:
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message_dict["user_name"] = message.name
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return message_dict
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@ -24,7 +24,7 @@ parameter_rules:
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min: 1
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max: 8000
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- name: safe_prompt
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defulat: false
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default: false
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type: boolean
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help:
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en_US: Whether to inject a safety prompt before all conversations.
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@ -24,7 +24,7 @@ parameter_rules:
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min: 1
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max: 8000
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- name: safe_prompt
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defulat: false
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default: false
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type: boolean
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help:
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en_US: Whether to inject a safety prompt before all conversations.
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@ -24,7 +24,7 @@ parameter_rules:
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min: 1
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max: 8000
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- name: safe_prompt
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defulat: false
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default: false
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type: boolean
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help:
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en_US: Whether to inject a safety prompt before all conversations.
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@ -24,7 +24,7 @@ parameter_rules:
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min: 1
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max: 2048
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- name: safe_prompt
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defulat: false
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default: false
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type: boolean
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help:
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en_US: Whether to inject a safety prompt before all conversations.
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@ -24,7 +24,7 @@ parameter_rules:
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min: 1
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max: 8000
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- name: safe_prompt
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defulat: false
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default: false
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type: boolean
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help:
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en_US: Whether to inject a safety prompt before all conversations.
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@ -25,6 +25,7 @@ from core.model_runtime.entities.model_entities import (
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AIModelEntity,
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DefaultParameterName,
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FetchFrom,
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ModelFeature,
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ModelPropertyKey,
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ModelType,
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ParameterRule,
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@ -166,11 +167,23 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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"""
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generate custom model entities from credentials
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"""
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support_function_call = False
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features = []
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function_calling_type = credentials.get('function_calling_type', 'no_call')
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if function_calling_type == 'function_call':
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features = [ModelFeature.TOOL_CALL]
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support_function_call = True
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endpoint_url = credentials["endpoint_url"]
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# if not endpoint_url.endswith('/'):
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# endpoint_url += '/'
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# if 'https://api.openai.com/v1/' == endpoint_url:
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# features = [ModelFeature.STREAM_TOOL_CALL]
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entity = AIModelEntity(
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model=model,
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label=I18nObject(en_US=model),
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model_type=ModelType.LLM,
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fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
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features=features if support_function_call else [],
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model_properties={
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ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', "4096")),
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ModelPropertyKey.MODE: credentials.get('mode'),
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@ -194,14 +207,6 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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max=1,
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precision=2
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),
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ParameterRule(
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name="top_k",
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label=I18nObject(en_US="Top K"),
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type=ParameterType.INT,
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default=int(credentials.get('top_k', 1)),
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min=1,
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max=100
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),
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ParameterRule(
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name=DefaultParameterName.FREQUENCY_PENALTY.value,
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label=I18nObject(en_US="Frequency Penalty"),
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@ -232,7 +237,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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output=Decimal(credentials.get('output_price', 0)),
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unit=Decimal(credentials.get('unit', 0)),
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currency=credentials.get('currency', "USD")
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)
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),
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)
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if credentials['mode'] == 'chat':
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@ -292,14 +297,22 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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raise ValueError("Unsupported completion type for model configuration.")
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# annotate tools with names, descriptions, etc.
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function_calling_type = credentials.get('function_calling_type', 'no_call')
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formatted_tools = []
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if tools:
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data["tool_choice"] = "auto"
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if function_calling_type == 'function_call':
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data['functions'] = [{
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"name": tool.name,
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"description": tool.description,
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"parameters": tool.parameters
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} for tool in tools]
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elif function_calling_type == 'tool_call':
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data["tool_choice"] = "auto"
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for tool in tools:
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formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool)))
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for tool in tools:
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formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool)))
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data["tools"] = formatted_tools
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data["tools"] = formatted_tools
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if stop:
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data["stop"] = stop
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@ -367,9 +380,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
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if chunk:
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#ignore sse comments
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# ignore sse comments
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if chunk.startswith(':'):
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continue
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continue
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decoded_chunk = chunk.strip().lstrip('data: ').lstrip()
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chunk_json = None
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try:
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@ -452,10 +465,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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response_content = ''
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tool_calls = None
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function_calling_type = credentials.get('function_calling_type', 'no_call')
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if completion_type is LLMMode.CHAT:
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response_content = output.get('message', {})['content']
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tool_calls = output.get('message', {}).get('tool_calls')
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if function_calling_type == 'tool_call':
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tool_calls = output.get('message', {}).get('tool_calls')
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elif function_calling_type == 'function_call':
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tool_calls = output.get('message', {}).get('function_call')
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elif completion_type is LLMMode.COMPLETION:
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response_content = output['text']
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@ -463,7 +479,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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assistant_message = AssistantPromptMessage(content=response_content, tool_calls=[])
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if tool_calls:
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assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls)
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if function_calling_type == 'tool_call':
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assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls)
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elif function_calling_type == 'function_call':
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assistant_message.tool_calls = [self._extract_response_function_call(tool_calls)]
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usage = response_json.get("usage")
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if usage:
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@ -522,33 +541,34 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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message = cast(AssistantPromptMessage, message)
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message_dict = {"role": "assistant", "content": message.content}
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if message.tool_calls:
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message_dict["tool_calls"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) for tool_call
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in
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message.tool_calls]
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# function_call = message.tool_calls[0]
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# message_dict["function_call"] = {
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# "name": function_call.function.name,
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# "arguments": function_call.function.arguments,
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# }
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# message_dict["tool_calls"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) for tool_call
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# in
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# message.tool_calls]
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function_call = message.tool_calls[0]
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message_dict["function_call"] = {
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"name": function_call.function.name,
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"arguments": function_call.function.arguments,
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}
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elif isinstance(message, SystemPromptMessage):
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message = cast(SystemPromptMessage, message)
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, ToolPromptMessage):
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message = cast(ToolPromptMessage, message)
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message_dict = {
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"role": "tool",
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"content": message.content,
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"tool_call_id": message.tool_call_id
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}
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# message_dict = {
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# "role": "function",
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# "role": "tool",
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# "content": message.content,
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# "name": message.tool_call_id
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# "tool_call_id": message.tool_call_id
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# }
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.tool_call_id
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}
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else:
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raise ValueError(f"Got unknown type {message}")
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if message.name is not None:
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if message.name:
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message_dict["name"] = message.name
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return message_dict
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@ -693,3 +713,26 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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tool_calls.append(tool_call)
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return tool_calls
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def _extract_response_function_call(self, response_function_call) \
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-> AssistantPromptMessage.ToolCall:
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"""
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Extract function call from response
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:param response_function_call: response function call
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:return: tool call
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"""
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tool_call = None
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if response_function_call:
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function = AssistantPromptMessage.ToolCall.ToolCallFunction(
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name=response_function_call['name'],
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arguments=response_function_call['arguments']
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)
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tool_call = AssistantPromptMessage.ToolCall(
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id=response_function_call['name'],
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type="function",
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function=function
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)
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return tool_call
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@ -75,6 +75,28 @@ model_credential_schema:
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value: llm
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default: '4096'
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type: text-input
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- variable: function_calling_type
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show_on:
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- variable: __model_type
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value: llm
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label:
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en_US: Function calling
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type: select
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required: false
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default: no_call
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options:
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- value: function_call
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label:
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en_US: Support
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zh_Hans: 支持
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# - value: tool_call
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# label:
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# en_US: Tool Call
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# zh_Hans: Tool Call
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- value: no_call
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label:
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en_US: Not Support
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zh_Hans: 不支持
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- variable: stream_mode_delimiter
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label:
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zh_Hans: 流模式返回结果的分隔符
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