Merge 15160646fd into a30945312a
This commit is contained in:
Obada Khalili
GitHub
commit
cd1c2769d9
@ -32,7 +32,9 @@ logger = logging.getLogger(__name__)
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class FunctionCallAgentRunner(BaseAgentRunner):
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def run(self, message: Message, query: str, **kwargs: Any) -> Generator[LLMResultChunk, None, None]:
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def run(
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self, message: Message, query: str, **kwargs: Any
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) -> Union[Generator[LLMResultChunk, None, None], LLMResult]:
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"""
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Run FunctionCall agent application
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"""
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@ -72,55 +74,98 @@ class FunctionCallAgentRunner(BaseAgentRunner):
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model_instance = self.model_instance
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while function_call_state and iteration_step <= max_iteration_steps:
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function_call_state = False
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final_prompt_messages = None
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final_system_fingerprint = None
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if iteration_step == max_iteration_steps:
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# the last iteration, remove all tools
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prompt_messages_tools = []
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def response_generator() -> Generator[LLMResultChunk, None, None]:
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nonlocal \
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function_call_state, \
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function_call_state, \
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iteration_step, \
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prompt_messages_tools, \
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final_answer, \
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final_prompt_messages, \
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final_system_fingerprint
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message_file_ids: list[str] = []
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agent_thought = self.create_agent_thought(
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message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
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)
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while function_call_state and iteration_step <= max_iteration_steps:
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function_call_state = False
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# recalc llm max tokens
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prompt_messages = self._organize_prompt_messages()
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self.recalc_llm_max_tokens(self.model_config, prompt_messages)
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# invoke model
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chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
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prompt_messages=prompt_messages,
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model_parameters=app_generate_entity.model_conf.parameters,
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tools=prompt_messages_tools,
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stop=app_generate_entity.model_conf.stop,
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stream=self.stream_tool_call,
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user=self.user_id,
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callbacks=[],
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)
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if iteration_step == max_iteration_steps:
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# the last iteration, remove all tools
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prompt_messages_tools = []
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tool_calls: list[tuple[str, str, dict[str, Any]]] = []
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message_file_ids: list[str] = []
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agent_thought = self.create_agent_thought(
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message_id=message.id, message="", tool_name="", tool_input="", messages_ids=message_file_ids
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)
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# save full response
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response = ""
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# recalc llm max tokens
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prompt_messages = self._organize_prompt_messages()
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self.recalc_llm_max_tokens(self.model_config, prompt_messages)
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# invoke model
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chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
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prompt_messages=prompt_messages,
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model_parameters=app_generate_entity.model_conf.parameters,
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tools=prompt_messages_tools,
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stop=app_generate_entity.model_conf.stop,
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stream=self.stream_tool_call,
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user=self.user_id,
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callbacks=[],
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)
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# save tool call names and inputs
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tool_call_names = ""
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tool_call_inputs = ""
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tool_calls: list[tuple[str, str, dict[str, Any]]] = []
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current_llm_usage = None
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# save full response
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response = ""
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if isinstance(chunks, Generator):
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is_first_chunk = True
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for chunk in chunks:
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if is_first_chunk:
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self.queue_manager.publish(
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QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
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)
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is_first_chunk = False
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# save tool call names and inputs
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tool_call_names = ""
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tool_call_inputs = ""
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current_llm_usage = None
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if isinstance(chunks, Generator):
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is_first_chunk = True
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for chunk in chunks:
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if is_first_chunk:
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self.queue_manager.publish(
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QueueAgentThoughtEvent(agent_thought_id=agent_thought.id),
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PublishFrom.APPLICATION_MANAGER,
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)
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is_first_chunk = False
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# check if there is any tool call
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if self.check_tool_calls(chunk):
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function_call_state = True
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tool_calls.extend(self.extract_tool_calls(chunk) or [])
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tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
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try:
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tool_call_inputs = json.dumps(
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{tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
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)
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except json.JSONDecodeError:
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# ensure ascii to avoid encoding error
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tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
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final_prompt_messages = chunk.prompt_messages
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final_system_fingerprint = chunk.system_fingerprint
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if chunk.delta.message and chunk.delta.message.content:
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if isinstance(chunk.delta.message.content, list):
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for content in chunk.delta.message.content:
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response += content.data
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else:
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response += str(chunk.delta.message.content)
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if chunk.delta.usage:
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increase_usage(llm_usage, chunk.delta.usage)
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current_llm_usage = chunk.delta.usage
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yield chunk
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else:
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result = chunks
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# check if there is any tool call
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if self.check_tool_calls(chunk):
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if self.check_blocking_tool_calls(result):
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function_call_state = True
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tool_calls.extend(self.extract_tool_calls(chunk) or [])
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tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
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tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
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try:
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tool_call_inputs = json.dumps(
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@ -130,189 +175,180 @@ class FunctionCallAgentRunner(BaseAgentRunner):
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# ensure ascii to avoid encoding error
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tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
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if chunk.delta.message and chunk.delta.message.content:
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if isinstance(chunk.delta.message.content, list):
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for content in chunk.delta.message.content:
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if result.usage:
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increase_usage(llm_usage, result.usage)
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current_llm_usage = result.usage
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final_prompt_messages = result.prompt_messages
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final_system_fingerprint = result.system_fingerprint
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if result.message and result.message.content:
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if isinstance(result.message.content, list):
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for content in result.message.content:
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response += content.data
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else:
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response += str(chunk.delta.message.content)
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response += str(result.message.content)
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if chunk.delta.usage:
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increase_usage(llm_usage, chunk.delta.usage)
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current_llm_usage = chunk.delta.usage
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if not result.message.content:
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result.message.content = ""
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yield chunk
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else:
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result = chunks
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# check if there is any tool call
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if self.check_blocking_tool_calls(result):
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function_call_state = True
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tool_calls.extend(self.extract_blocking_tool_calls(result) or [])
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tool_call_names = ";".join([tool_call[1] for tool_call in tool_calls])
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try:
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tool_call_inputs = json.dumps(
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{tool_call[1]: tool_call[2] for tool_call in tool_calls}, ensure_ascii=False
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)
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except json.JSONDecodeError:
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# ensure ascii to avoid encoding error
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tool_call_inputs = json.dumps({tool_call[1]: tool_call[2] for tool_call in tool_calls})
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self.queue_manager.publish(
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QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
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)
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if result.usage:
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increase_usage(llm_usage, result.usage)
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current_llm_usage = result.usage
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if result.message and result.message.content:
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if isinstance(result.message.content, list):
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for content in result.message.content:
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response += content.data
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else:
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response += str(result.message.content)
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if not result.message.content:
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result.message.content = ""
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self.queue_manager.publish(
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QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
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)
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yield LLMResultChunk(
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model=model_instance.model,
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prompt_messages=result.prompt_messages,
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system_fingerprint=result.system_fingerprint,
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delta=LLMResultChunkDelta(
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index=0,
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message=result.message,
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usage=result.usage,
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),
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)
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assistant_message = AssistantPromptMessage(content="", tool_calls=[])
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if tool_calls:
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assistant_message.tool_calls = [
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AssistantPromptMessage.ToolCall(
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id=tool_call[0],
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type="function",
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function=AssistantPromptMessage.ToolCall.ToolCallFunction(
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name=tool_call[1], arguments=json.dumps(tool_call[2], ensure_ascii=False)
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yield LLMResultChunk(
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model=model_instance.model,
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prompt_messages=result.prompt_messages,
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system_fingerprint=result.system_fingerprint,
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delta=LLMResultChunkDelta(
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index=0,
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message=result.message,
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usage=result.usage,
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),
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)
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for tool_call in tool_calls
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]
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else:
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assistant_message.content = response
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self._current_thoughts.append(assistant_message)
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# save thought
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name=tool_call_names,
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tool_input=tool_call_inputs,
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thought=response,
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tool_invoke_meta=None,
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observation=None,
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answer=response,
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messages_ids=[],
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llm_usage=current_llm_usage,
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)
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self.queue_manager.publish(
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QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
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)
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final_answer += response + "\n"
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# call tools
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tool_responses = []
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for tool_call_id, tool_call_name, tool_call_args in tool_calls:
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tool_instance = tool_instances.get(tool_call_name)
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if not tool_instance:
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tool_response = {
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"tool_call_id": tool_call_id,
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"tool_call_name": tool_call_name,
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"tool_response": f"there is not a tool named {tool_call_name}",
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"meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict(),
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}
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assistant_message = AssistantPromptMessage(content="", tool_calls=[])
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if tool_calls:
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assistant_message.tool_calls = [
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AssistantPromptMessage.ToolCall(
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id=tool_call[0],
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type="function",
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function=AssistantPromptMessage.ToolCall.ToolCallFunction(
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name=tool_call[1], arguments=json.dumps(tool_call[2], ensure_ascii=False)
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),
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)
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for tool_call in tool_calls
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]
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else:
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# invoke tool
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tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
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tool=tool_instance,
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tool_parameters=tool_call_args,
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user_id=self.user_id,
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tenant_id=self.tenant_id,
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message=self.message,
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invoke_from=self.application_generate_entity.invoke_from,
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agent_tool_callback=self.agent_callback,
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trace_manager=trace_manager,
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app_id=self.application_generate_entity.app_config.app_id,
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message_id=self.message.id,
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conversation_id=self.conversation.id,
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)
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# publish files
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for message_file_id in message_files:
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# publish message file
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self.queue_manager.publish(
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QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
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)
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# add message file ids
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message_file_ids.append(message_file_id)
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assistant_message.content = response
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tool_response = {
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"tool_call_id": tool_call_id,
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"tool_call_name": tool_call_name,
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"tool_response": tool_invoke_response,
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"meta": tool_invoke_meta.to_dict(),
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}
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self._current_thoughts.append(assistant_message)
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tool_responses.append(tool_response)
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if tool_response["tool_response"] is not None:
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self._current_thoughts.append(
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ToolPromptMessage(
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content=str(tool_response["tool_response"]),
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tool_call_id=tool_call_id,
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name=tool_call_name,
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)
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)
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if len(tool_responses) > 0:
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# save agent thought
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# save thought
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name="",
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tool_input="",
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thought="",
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tool_invoke_meta={
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tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
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},
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observation={
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tool_response["tool_call_name"]: tool_response["tool_response"]
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for tool_response in tool_responses
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},
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answer="",
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messages_ids=message_file_ids,
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tool_name=tool_call_names,
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tool_input=tool_call_inputs,
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thought=response,
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tool_invoke_meta=None,
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observation=None,
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answer=response,
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messages_ids=[],
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llm_usage=current_llm_usage,
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)
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self.queue_manager.publish(
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QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
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)
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# update prompt tool
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for prompt_tool in prompt_messages_tools:
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self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
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final_answer += response
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iteration_step += 1
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# call tools
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tool_responses = []
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for tool_call_id, tool_call_name, tool_call_args in tool_calls:
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tool_instance = tool_instances.get(tool_call_name)
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if not tool_instance:
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tool_response = {
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"tool_call_id": tool_call_id,
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"tool_call_name": tool_call_name,
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"tool_response": f"there is not a tool named {tool_call_name}",
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"meta": ToolInvokeMeta.error_instance(
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f"there is not a tool named {tool_call_name}"
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).to_dict(),
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}
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else:
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# invoke tool
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tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
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tool=tool_instance,
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tool_parameters=tool_call_args,
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user_id=self.user_id,
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tenant_id=self.tenant_id,
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message=self.message,
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invoke_from=self.application_generate_entity.invoke_from,
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agent_tool_callback=self.agent_callback,
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trace_manager=trace_manager,
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app_id=self.application_generate_entity.app_config.app_id,
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message_id=self.message.id,
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conversation_id=self.conversation.id,
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)
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# publish files
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for message_file_id in message_files:
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# publish message file
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self.queue_manager.publish(
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QueueMessageFileEvent(message_file_id=message_file_id), PublishFrom.APPLICATION_MANAGER
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)
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# add message file ids
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message_file_ids.append(message_file_id)
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# publish end event
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self.queue_manager.publish(
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QueueMessageEndEvent(
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llm_result=LLMResult(
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model=model_instance.model,
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prompt_messages=prompt_messages,
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message=AssistantPromptMessage(content=final_answer),
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usage=llm_usage["usage"] or LLMUsage.empty_usage(),
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system_fingerprint="",
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)
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),
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PublishFrom.APPLICATION_MANAGER,
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)
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tool_response = {
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"tool_call_id": tool_call_id,
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"tool_call_name": tool_call_name,
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"tool_response": tool_invoke_response,
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"meta": tool_invoke_meta.to_dict(),
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}
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tool_responses.append(tool_response)
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if tool_response["tool_response"] is not None:
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self._current_thoughts.append(
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ToolPromptMessage(
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content=str(tool_response["tool_response"]),
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tool_call_id=tool_call_id,
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name=tool_call_name,
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)
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)
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if len(tool_responses) > 0:
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# save agent thought
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name="",
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tool_input="",
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thought="",
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tool_invoke_meta={
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tool_response["tool_call_name"]: tool_response["meta"] for tool_response in tool_responses
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},
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observation={
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tool_response["tool_call_name"]: tool_response["tool_response"]
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for tool_response in tool_responses
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||||
},
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answer="",
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messages_ids=message_file_ids,
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)
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self.queue_manager.publish(
|
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QueueAgentThoughtEvent(agent_thought_id=agent_thought.id), PublishFrom.APPLICATION_MANAGER
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)
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# update prompt tool
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for prompt_tool in prompt_messages_tools:
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self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
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iteration_step += 1
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|
||||
# publish end event
|
||||
self.queue_manager.publish(
|
||||
QueueMessageEndEvent(
|
||||
llm_result=LLMResult(
|
||||
model=model_instance.model,
|
||||
prompt_messages=prompt_messages,
|
||||
message=AssistantPromptMessage(content=final_answer),
|
||||
usage=llm_usage["usage"] or LLMUsage.empty_usage(),
|
||||
system_fingerprint="",
|
||||
)
|
||||
),
|
||||
PublishFrom.APPLICATION_MANAGER,
|
||||
)
|
||||
|
||||
chunk_generator = response_generator()
|
||||
|
||||
if app_generate_entity.stream:
|
||||
return chunk_generator
|
||||
else:
|
||||
list(chunk_generator)
|
||||
return LLMResult(
|
||||
model=model_instance.model,
|
||||
prompt_messages=final_prompt_messages or [],
|
||||
message=AssistantPromptMessage(content=final_answer),
|
||||
usage=llm_usage["usage"] or LLMUsage.empty_usage(),
|
||||
system_fingerprint=final_system_fingerprint or "",
|
||||
)
|
||||
|
||||
def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
|
||||
"""
|
||||
|
||||
@ -82,9 +82,6 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
|
||||
:param invoke_from: invoke from source
|
||||
:param stream: is stream
|
||||
"""
|
||||
if not streaming:
|
||||
raise ValueError("Agent Chat App does not support blocking mode")
|
||||
|
||||
if not args.get("query"):
|
||||
raise ValueError("query is required")
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user