feat: remove llm client use (#1316)
takatost
2 years ago
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GitHub
parent
c007dbdc13
commit
cbf095465c
@ -1,140 +0,0 @@
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from typing import cast, List
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from langchain.chat_models import ChatOpenAI
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from langchain.chat_models.openai import _convert_message_to_dict
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from langchain.memory.summary import SummarizerMixin
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from langchain.schema import SystemMessage, HumanMessage, BaseMessage, AIMessage
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from langchain.schema.language_model import BaseLanguageModel
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from pydantic import BaseModel
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from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError, CalcTokenMixin
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from core.model_providers.models.llm.base import BaseLLM
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class OpenAIFunctionCallSummarizeMixin(BaseModel, CalcTokenMixin):
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moving_summary_buffer: str = ""
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moving_summary_index: int = 0
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summary_llm: BaseLanguageModel = None
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model_instance: BaseLLM
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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def summarize_messages_if_needed(self, messages: List[BaseMessage], **kwargs) -> List[BaseMessage]:
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# calculate rest tokens and summarize previous function observation messages if rest_tokens < 0
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rest_tokens = self.get_message_rest_tokens(self.model_instance, messages, **kwargs)
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rest_tokens = rest_tokens - 20 # to deal with the inaccuracy of rest_tokens
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if rest_tokens >= 0:
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return messages
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system_message = None
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human_message = None
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should_summary_messages = []
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for message in messages:
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if isinstance(message, SystemMessage):
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system_message = message
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elif isinstance(message, HumanMessage):
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human_message = message
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else:
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should_summary_messages.append(message)
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if len(should_summary_messages) > 2:
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ai_message = should_summary_messages[-2]
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function_message = should_summary_messages[-1]
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should_summary_messages = should_summary_messages[self.moving_summary_index:-2]
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self.moving_summary_index = len(should_summary_messages)
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else:
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error_msg = "Exceeded LLM tokens limit, stopped."
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raise ExceededLLMTokensLimitError(error_msg)
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new_messages = [system_message, human_message]
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if self.moving_summary_index == 0:
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should_summary_messages.insert(0, human_message)
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summary_handler = SummarizerMixin(llm=self.summary_llm)
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self.moving_summary_buffer = summary_handler.predict_new_summary(
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messages=should_summary_messages,
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existing_summary=self.moving_summary_buffer
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)
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new_messages.append(AIMessage(content=self.moving_summary_buffer))
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new_messages.append(ai_message)
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new_messages.append(function_message)
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return new_messages
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def get_num_tokens_from_messages(self, model_instance: BaseLLM, messages: List[BaseMessage], **kwargs) -> int:
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"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
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Official documentation: https://github.com/openai/openai-cookbook/blob/
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main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
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llm = cast(ChatOpenAI, model_instance.client)
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model, encoding = llm._get_encoding_model()
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if model.startswith("gpt-3.5-turbo"):
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# every message follows <im_start>{role/name}\n{content}<im_end>\n
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tokens_per_message = 4
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# if there's a name, the role is omitted
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tokens_per_name = -1
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elif model.startswith("gpt-4"):
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tokens_per_message = 3
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tokens_per_name = 1
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else:
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raise NotImplementedError(
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f"get_num_tokens_from_messages() is not presently implemented "
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f"for model {model}."
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"See https://github.com/openai/openai-python/blob/main/chatml.md for "
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"information on how messages are converted to tokens."
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)
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num_tokens = 0
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for m in messages:
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message = _convert_message_to_dict(m)
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num_tokens += tokens_per_message
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for key, value in message.items():
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if key == "function_call":
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for f_key, f_value in value.items():
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num_tokens += len(encoding.encode(f_key))
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num_tokens += len(encoding.encode(f_value))
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else:
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num_tokens += len(encoding.encode(value))
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if key == "name":
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num_tokens += tokens_per_name
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# every reply is primed with <im_start>assistant
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num_tokens += 3
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if kwargs.get('functions'):
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for function in kwargs.get('functions'):
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num_tokens += len(encoding.encode('name'))
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num_tokens += len(encoding.encode(function.get("name")))
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num_tokens += len(encoding.encode('description'))
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num_tokens += len(encoding.encode(function.get("description")))
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parameters = function.get("parameters")
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num_tokens += len(encoding.encode('parameters'))
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if 'title' in parameters:
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num_tokens += len(encoding.encode('title'))
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num_tokens += len(encoding.encode(parameters.get("title")))
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num_tokens += len(encoding.encode('type'))
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num_tokens += len(encoding.encode(parameters.get("type")))
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if 'properties' in parameters:
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num_tokens += len(encoding.encode('properties'))
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for key, value in parameters.get('properties').items():
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num_tokens += len(encoding.encode(key))
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for field_key, field_value in value.items():
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num_tokens += len(encoding.encode(field_key))
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if field_key == 'enum':
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for enum_field in field_value:
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num_tokens += 3
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num_tokens += len(encoding.encode(enum_field))
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else:
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num_tokens += len(encoding.encode(field_key))
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num_tokens += len(encoding.encode(str(field_value)))
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if 'required' in parameters:
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num_tokens += len(encoding.encode('required'))
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for required_field in parameters['required']:
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num_tokens += 3
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num_tokens += len(encoding.encode(required_field))
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return num_tokens
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@ -1,107 +0,0 @@
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from typing import List, Tuple, Any, Union, Sequence, Optional
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from langchain.agents import BaseMultiActionAgent
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from langchain.agents.openai_functions_multi_agent.base import OpenAIMultiFunctionsAgent, _format_intermediate_steps, \
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_parse_ai_message
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.callbacks.manager import Callbacks
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from langchain.prompts.chat import BaseMessagePromptTemplate
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from langchain.schema import AgentAction, AgentFinish, SystemMessage
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from langchain.schema.language_model import BaseLanguageModel
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from langchain.tools import BaseTool
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from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError
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from core.agent.agent.openai_function_call_summarize_mixin import OpenAIFunctionCallSummarizeMixin
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class AutoSummarizingOpenMultiAIFunctionCallAgent(OpenAIMultiFunctionsAgent, OpenAIFunctionCallSummarizeMixin):
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@classmethod
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def from_llm_and_tools(
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cls,
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llm: BaseLanguageModel,
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tools: Sequence[BaseTool],
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callback_manager: Optional[BaseCallbackManager] = None,
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extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
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system_message: Optional[SystemMessage] = SystemMessage(
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content="You are a helpful AI assistant."
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),
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**kwargs: Any,
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) -> BaseMultiActionAgent:
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return super().from_llm_and_tools(
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llm=llm,
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tools=tools,
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callback_manager=callback_manager,
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extra_prompt_messages=extra_prompt_messages,
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system_message=cls.get_system_message(),
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**kwargs,
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)
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def should_use_agent(self, query: str):
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"""
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return should use agent
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:param query:
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:return:
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"""
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original_max_tokens = self.llm.max_tokens
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self.llm.max_tokens = 15
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prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
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messages = prompt.to_messages()
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try:
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predicted_message = self.llm.predict_messages(
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messages, functions=self.functions, callbacks=None
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)
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except Exception as e:
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new_exception = self.model_instance.handle_exceptions(e)
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raise new_exception
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function_call = predicted_message.additional_kwargs.get("function_call", {})
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self.llm.max_tokens = original_max_tokens
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return True if function_call else False
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def plan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date, along with observations
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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agent_scratchpad = _format_intermediate_steps(intermediate_steps)
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selected_inputs = {
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k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
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}
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full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
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prompt = self.prompt.format_prompt(**full_inputs)
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messages = prompt.to_messages()
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# summarize messages if rest_tokens < 0
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try:
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messages = self.summarize_messages_if_needed(messages, functions=self.functions)
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except ExceededLLMTokensLimitError as e:
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return AgentFinish(return_values={"output": str(e)}, log=str(e))
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predicted_message = self.llm.predict_messages(
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messages, functions=self.functions, callbacks=callbacks
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)
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agent_decision = _parse_ai_message(predicted_message)
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return agent_decision
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@classmethod
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def get_system_message(cls):
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# get current time
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return SystemMessage(content="You are a helpful AI assistant.\n"
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"The current date or current time you know is wrong.\n"
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"Respond directly if appropriate.")
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@ -0,0 +1,36 @@
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from typing import List, Dict, Any, Optional
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from langchain import LLMChain as LCLLMChain
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.schema import LLMResult, Generation
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from langchain.schema.language_model import BaseLanguageModel
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from core.model_providers.models.entity.message import to_prompt_messages
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from core.model_providers.models.llm.base import BaseLLM
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from core.third_party.langchain.llms.fake import FakeLLM
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class LLMChain(LCLLMChain):
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model_instance: BaseLLM
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"""The language model instance to use."""
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llm: BaseLanguageModel = FakeLLM(response="")
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def generate(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> LLMResult:
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"""Generate LLM result from inputs."""
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prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
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messages = prompts[0].to_messages()
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prompt_messages = to_prompt_messages(messages)
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result = self.model_instance.run(
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messages=prompt_messages,
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stop=stop
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)
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generations = [
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[Generation(text=result.content)]
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]
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return LLMResult(generations=generations)
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