mirror of
https://github.com/FoundationAgents/MetaGPT.git
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feat: merge send18:dev
This commit is contained in:
莘权 马
commit
7effe7f74c
92 changed files with 4830 additions and 302 deletions
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@ -4,9 +4,11 @@
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@Time : 2023/5/5 22:59
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@Author : alexanderwu
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@File : __init__.py
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@Modified By: mashenquan, 2023/9/8. Add `MetaGPTLLMAPI`
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"""
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from metagpt.provider.openai_api import OpenAIGPTAPI
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from metagpt.provider.metagpt_llm_api import MetaGPTLLMAPI
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__all__ = ["OpenAIGPTAPI"]
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__all__ = ["OpenAIGPTAPI", "MetaGPTLLMAPI"]
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@ -4,6 +4,7 @@
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@Time : 2023/5/5 23:04
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@Author : alexanderwu
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@File : base_gpt_api.py
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@Desc : mashenquan, 2023/8/22. + try catch
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"""
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import json
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from abc import abstractmethod
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278
metagpt/provider/metagpt_llm_api.py
Normal file
278
metagpt/provider/metagpt_llm_api.py
Normal file
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@ -0,0 +1,278 @@
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# -*- coding: utf-8 -*-
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"""
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@Time : 2023/8/30
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@Author : mashenquan
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@File : metagpt_llm_api.py
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@Desc : MetaGPT LLM related APIs
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"""
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from metagpt.provider.openai_api import OpenAIGPTAPI
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# from metagpt.provider.base_gpt_api import BaseGPTAPI
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# from metagpt.provider.openai_api import RateLimiter
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class MetaGPTLLMAPI(OpenAIGPTAPI):
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"""MetaGPT LLM api"""
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def __init__(self):
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super(MetaGPTLLMAPI, self).__init__()
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# def __init__(self):
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# self.__init_openai(CONFIG)
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# self.llm = openai
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# self.model = CONFIG.openai_api_model
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# self.auto_max_tokens = False
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# self._cost_manager = CostManager()
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# RateLimiter.__init__(self, rpm=self.rpm)
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#
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# def __init_openai(self, config):
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# openai.api_key = config.openai_api_key
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# if config.openai_api_base:
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# openai.api_base = config.openai_api_base
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# if config.openai_api_type:
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# openai.api_type = config.openai_api_type
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# openai.api_version = config.openai_api_version
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# self.rpm = int(config.get("RPM", 10))
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#
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# async def _achat_completion_stream(self, messages: list[dict]) -> str:
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# response = await openai.ChatCompletion.acreate(**self._cons_kwargs(messages), stream=True)
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#
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# # create variables to collect the stream of chunks
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# collected_chunks = []
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# collected_messages = []
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# # iterate through the stream of events
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# async for chunk in response:
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# collected_chunks.append(chunk) # save the event response
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# choices = chunk["choices"]
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# if len(choices) > 0:
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# chunk_message = chunk["choices"][0].get("delta", {}) # extract the message
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# collected_messages.append(chunk_message) # save the message
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# if "content" in chunk_message:
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# print(chunk_message["content"], end="")
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# print()
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#
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# full_reply_content = "".join([m.get("content", "") for m in collected_messages])
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# usage = self._calc_usage(messages, full_reply_content)
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# self._update_costs(usage)
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# return full_reply_content
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#
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# def _cons_kwargs(self, messages: list[dict], **configs) -> dict:
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# kwargs = {
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# "messages": messages,
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# "max_tokens": self.get_max_tokens(messages),
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# "n": 1,
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# "stop": None,
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# "temperature": 0.3,
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# "timeout": 3,
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# }
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# if configs:
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# kwargs.update(configs)
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#
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# if CONFIG.openai_api_type == "azure":
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# if CONFIG.deployment_name and CONFIG.deployment_id:
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# raise ValueError("You can only use one of the `deployment_id` or `deployment_name` model")
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# elif not CONFIG.deployment_name and not CONFIG.deployment_id:
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# raise ValueError("You must specify `DEPLOYMENT_NAME` or `DEPLOYMENT_ID` parameter")
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# kwargs_mode = (
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# {"engine": CONFIG.deployment_name}
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# if CONFIG.deployment_name
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# else {"deployment_id": CONFIG.deployment_id}
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# )
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# else:
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# kwargs_mode = {"model": self.model}
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# kwargs.update(kwargs_mode)
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# return kwargs
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#
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# async def _achat_completion(self, messages: list[dict]) -> dict:
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# rsp = await self.llm.ChatCompletion.acreate(**self._cons_kwargs(messages))
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# self._update_costs(rsp.get("usage"))
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# return rsp
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#
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# def _chat_completion(self, messages: list[dict]) -> dict:
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# rsp = self.llm.ChatCompletion.create(**self._cons_kwargs(messages))
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# self._update_costs(rsp)
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# return rsp
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#
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# def completion(self, messages: list[dict]) -> dict:
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# # if isinstance(messages[0], Message):
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# # messages = self.messages_to_dict(messages)
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# return self._chat_completion(messages)
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#
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# async def acompletion(self, messages: list[dict]) -> dict:
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# # if isinstance(messages[0], Message):
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# # messages = self.messages_to_dict(messages)
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# return await self._achat_completion(messages)
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#
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# @retry(
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# wait=wait_random_exponential(min=1, max=60),
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# stop=stop_after_attempt(6),
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# after=after_log(logger, logger.level("WARNING").name),
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# retry=retry_if_exception_type(APIConnectionError),
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# retry_error_callback=log_and_reraise,
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# )
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# async def acompletion_text(self, messages: list[dict], stream=False) -> str:
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# """when streaming, print each token in place."""
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# if stream:
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# return await self._achat_completion_stream(messages)
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# rsp = await self._achat_completion(messages)
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# return self.get_choice_text(rsp)
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#
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# def _func_configs(self, messages: list[dict], **kwargs) -> dict:
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# """
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# Note: Keep kwargs consistent with the parameters in the https://platform.openai.com/docs/api-reference/chat/create
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# """
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# if "tools" not in kwargs:
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# configs = {
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# "tools": [{"type": "function", "function": GENERAL_FUNCTION_SCHEMA}],
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# "tool_choice": GENERAL_TOOL_CHOICE,
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# }
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# kwargs.update(configs)
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#
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# return self._cons_kwargs(messages, **kwargs)
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#
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# def _chat_completion_function(self, messages: list[dict], **kwargs) -> dict:
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# rsp = self.llm.ChatCompletion.create(**self._func_configs(messages, **kwargs))
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# self._update_costs(rsp.get("usage"))
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# return rsp
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#
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# async def _achat_completion_function(self, messages: list[dict], **chat_configs) -> dict:
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# rsp = await self.llm.ChatCompletion.acreate(**self._func_configs(messages, **chat_configs))
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# self._update_costs(rsp.get("usage"))
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# return rsp
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#
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# def _process_message(self, messages: Union[str, Message, list[dict], list[Message], list[str]]) -> list[dict]:
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# """convert messages to list[dict]."""
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# if isinstance(messages, list):
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# messages = [Message(msg) if isinstance(msg, str) else msg for msg in messages]
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# return [msg if isinstance(msg, dict) else msg.to_dict() for msg in messages]
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#
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# if isinstance(messages, Message):
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# messages = [messages.to_dict()]
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# elif isinstance(messages, str):
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# messages = [{"role": "user", "content": messages}]
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# else:
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# raise ValueError(
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# f"Only support messages type are: str, Message, list[dict], but got {type(messages).__name__}!"
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# )
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# return messages
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#
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# def ask_code(self, messages: Union[str, Message, list[dict]], **kwargs) -> dict:
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# """Use function of tools to ask a code.
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#
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# Note: Keep kwargs consistent with the parameters in the https://platform.openai.com/docs/api-reference/chat/create
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#
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# Examples:
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#
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# >>> llm = OpenAIGPTAPI()
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# >>> llm.ask_code("Write a python hello world code.")
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# {'language': 'python', 'code': "print('Hello, World!')"}
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# >>> msg = [{'role': 'user', 'content': "Write a python hello world code."}]
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# >>> llm.ask_code(msg)
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# {'language': 'python', 'code': "print('Hello, World!')"}
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# """
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# messages = self._process_message(messages)
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# rsp = self._chat_completion_function(messages, **kwargs)
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# return self.get_choice_function_arguments(rsp)
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#
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# async def aask_code(self, messages: Union[str, Message, list[dict]], **kwargs) -> dict:
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# """Use function of tools to ask a code.
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#
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# Note: Keep kwargs consistent with the parameters in the https://platform.openai.com/docs/api-reference/chat/create
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#
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# Examples:
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#
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# >>> llm = OpenAIGPTAPI()
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# >>> rsp = await llm.ask_code("Write a python hello world code.")
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# >>> rsp
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# {'language': 'python', 'code': "print('Hello, World!')"}
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# >>> msg = [{'role': 'user', 'content': "Write a python hello world code."}]
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# >>> rsp = await llm.aask_code(msg) # -> {'language': 'python', 'code': "print('Hello, World!')"}
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# """
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# messages = self._process_message(messages)
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# rsp = await self._achat_completion_function(messages, **kwargs)
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# return self.get_choice_function_arguments(rsp)
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#
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# def _calc_usage(self, messages: list[dict], rsp: str) -> dict:
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# usage = {}
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# if CONFIG.calc_usage:
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# try:
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# prompt_tokens = count_message_tokens(messages, self.model)
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# completion_tokens = count_string_tokens(rsp, self.model)
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# usage["prompt_tokens"] = prompt_tokens
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# usage["completion_tokens"] = completion_tokens
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# return usage
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# except Exception as e:
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# logger.error("usage calculation failed!", e)
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# else:
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# return usage
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#
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# async def acompletion_batch(self, batch: list[list[dict]]) -> list[dict]:
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# """Return full JSON"""
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# split_batches = self.split_batches(batch)
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# all_results = []
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#
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# for small_batch in split_batches:
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# logger.info(small_batch)
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# await self.wait_if_needed(len(small_batch))
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#
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# future = [self.acompletion(prompt) for prompt in small_batch]
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# results = await asyncio.gather(*future)
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# logger.info(results)
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# all_results.extend(results)
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#
|
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# return all_results
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#
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# async def acompletion_batch_text(self, batch: list[list[dict]]) -> list[str]:
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# """Only return plain text"""
|
||||
# raw_results = await self.acompletion_batch(batch)
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# results = []
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# for idx, raw_result in enumerate(raw_results, start=1):
|
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# result = self.get_choice_text(raw_result)
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# results.append(result)
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||||
# logger.info(f"Result of task {idx}: {result}")
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# return results
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#
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# def _update_costs(self, usage: dict):
|
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# if CONFIG.calc_usage:
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# try:
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# prompt_tokens = int(usage["prompt_tokens"])
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# completion_tokens = int(usage["completion_tokens"])
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# self._cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
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# except Exception as e:
|
||||
# logger.error("updating costs failed!", e)
|
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#
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# def get_costs(self) -> Costs:
|
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# return self._cost_manager.get_costs()
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#
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# def get_max_tokens(self, messages: list[dict]):
|
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# if not self.auto_max_tokens:
|
||||
# return CONFIG.max_tokens_rsp
|
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# return get_max_completion_tokens(messages, self.model, CONFIG.max_tokens_rsp)
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#
|
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# def moderation(self, content: Union[str, list[str]]):
|
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# try:
|
||||
# if not content:
|
||||
# logger.error("content cannot be empty!")
|
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# else:
|
||||
# rsp = self._moderation(content=content)
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# return rsp
|
||||
# except Exception as e:
|
||||
# logger.error(f"moderating failed:{e}")
|
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#
|
||||
# def _moderation(self, content: Union[str, list[str]]):
|
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# rsp = self.llm.Moderation.create(input=content)
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# return rsp
|
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#
|
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# async def amoderation(self, content: Union[str, list[str]]):
|
||||
# try:
|
||||
# if not content:
|
||||
# logger.error("content cannot be empty!")
|
||||
# else:
|
||||
# rsp = await self._amoderation(content=content)
|
||||
# return rsp
|
||||
# except Exception as e:
|
||||
# logger.error(f"moderating failed:{e}")
|
||||
#
|
||||
# async def _amoderation(self, content: Union[str, list[str]]):
|
||||
# rsp = await self.llm.Moderation.acreate(input=content)
|
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# return rsp
|
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|
|
@ -8,13 +8,13 @@
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@Modified By: mashenquan, 2023/11/21. Fix bug: ReadTimeout.
|
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@Modified By: mashenquan, 2023/12/1. Fix bug: Unclosed connection caused by openai 0.x.
|
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"""
|
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import asyncio
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import time
|
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from typing import NamedTuple, Union
|
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|
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import openai
|
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from typing import Union
|
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from openai import APIConnectionError, AsyncAzureOpenAI, AsyncOpenAI, RateLimitError
|
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from openai.types import CompletionUsage
|
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import asyncio
|
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import time
|
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import openai
|
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from tenacity import (
|
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after_log,
|
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retry,
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|
|
@ -22,15 +22,14 @@ from tenacity import (
|
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stop_after_attempt,
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wait_random_exponential,
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)
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|
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from metagpt.config import CONFIG
|
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from metagpt.llm import LLMType
|
||||
from metagpt.logs import logger
|
||||
from metagpt.provider.base_gpt_api import BaseGPTAPI
|
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from metagpt.provider.constant import GENERAL_FUNCTION_SCHEMA, GENERAL_TOOL_CHOICE
|
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from metagpt.schema import Message
|
||||
from metagpt.utils.singleton import Singleton
|
||||
from metagpt.utils.cost_manager import Costs
|
||||
from metagpt.utils.token_counter import (
|
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TOKEN_COSTS,
|
||||
count_message_tokens,
|
||||
count_string_tokens,
|
||||
get_max_completion_tokens,
|
||||
|
|
@ -62,75 +61,6 @@ class RateLimiter:
|
|||
self.last_call_time = time.time()
|
||||
|
||||
|
||||
class Costs(NamedTuple):
|
||||
total_prompt_tokens: int
|
||||
total_completion_tokens: int
|
||||
total_cost: float
|
||||
total_budget: float
|
||||
|
||||
|
||||
class CostManager(metaclass=Singleton):
|
||||
"""计算使用接口的开销"""
|
||||
|
||||
def __init__(self):
|
||||
self.total_prompt_tokens = 0
|
||||
self.total_completion_tokens = 0
|
||||
self.total_cost = 0
|
||||
self.total_budget = 0
|
||||
|
||||
def update_cost(self, prompt_tokens, completion_tokens, model):
|
||||
"""
|
||||
Update the total cost, prompt tokens, and completion tokens.
|
||||
|
||||
Args:
|
||||
prompt_tokens (int): The number of tokens used in the prompt.
|
||||
completion_tokens (int): The number of tokens used in the completion.
|
||||
model (str): The model used for the API call.
|
||||
"""
|
||||
self.total_prompt_tokens += prompt_tokens
|
||||
self.total_completion_tokens += completion_tokens
|
||||
cost = (
|
||||
prompt_tokens * TOKEN_COSTS[model]["prompt"] + completion_tokens * TOKEN_COSTS[model]["completion"]
|
||||
) / 1000
|
||||
self.total_cost += cost
|
||||
logger.info(
|
||||
f"Total running cost: ${self.total_cost:.3f} | Max budget: ${CONFIG.max_budget:.3f} | "
|
||||
f"Current cost: ${cost:.3f}, prompt_tokens: {prompt_tokens}, completion_tokens: {completion_tokens}"
|
||||
)
|
||||
CONFIG.total_cost = self.total_cost
|
||||
|
||||
def get_total_prompt_tokens(self):
|
||||
"""
|
||||
Get the total number of prompt tokens.
|
||||
|
||||
Returns:
|
||||
int: The total number of prompt tokens.
|
||||
"""
|
||||
return self.total_prompt_tokens
|
||||
|
||||
def get_total_completion_tokens(self):
|
||||
"""
|
||||
Get the total number of completion tokens.
|
||||
|
||||
Returns:
|
||||
int: The total number of completion tokens.
|
||||
"""
|
||||
return self.total_completion_tokens
|
||||
|
||||
def get_total_cost(self):
|
||||
"""
|
||||
Get the total cost of API calls.
|
||||
|
||||
Returns:
|
||||
float: The total cost of API calls.
|
||||
"""
|
||||
return self.total_cost
|
||||
|
||||
def get_costs(self) -> Costs:
|
||||
"""Get all costs"""
|
||||
return Costs(self.total_prompt_tokens, self.total_completion_tokens, self.total_cost, self.total_budget)
|
||||
|
||||
|
||||
def log_and_reraise(retry_state):
|
||||
logger.error(f"Retry attempts exhausted. Last exception: {retry_state.outcome.exception()}")
|
||||
logger.warning(
|
||||
|
|
@ -161,7 +91,6 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
|
|||
else:
|
||||
# https://github.com/openai/openai-python#async-usage
|
||||
self._client = AsyncOpenAI(api_key=CONFIG.openai_api_key, base_url=CONFIG.openai_api_base)
|
||||
self._cost_manager = CostManager()
|
||||
RateLimiter.__init__(self, rpm=self.rpm)
|
||||
|
||||
async def _achat_completion_stream(self, messages: list[dict], timeout=3) -> str:
|
||||
|
|
@ -362,12 +291,15 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
|
|||
|
||||
def _update_costs(self, usage: CompletionUsage):
|
||||
if CONFIG.calc_usage:
|
||||
prompt_tokens = usage.prompt_tokens
|
||||
completion_tokens = usage.completion_tokens
|
||||
self._cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
|
||||
try:
|
||||
prompt_tokens = usage.prompt_tokens
|
||||
completion_tokens = usage.completion_tokens
|
||||
CONFIG.cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
|
||||
except Exception as e:
|
||||
logger.error("updating costs failed!", e)
|
||||
|
||||
def get_costs(self) -> Costs:
|
||||
return self._cost_manager.get_costs()
|
||||
return CONFIG.cost_manager.get_costs()
|
||||
|
||||
def get_max_tokens(self, messages: list[dict]):
|
||||
if not self.auto_max_tokens:
|
||||
|
|
@ -410,3 +342,10 @@ class OpenAIGPTAPI(BaseGPTAPI, RateLimiter):
|
|||
return loop
|
||||
else:
|
||||
raise e
|
||||
|
||||
async def get_summary(self, text: str, max_words=200, keep_language: bool = False, **kwargs) -> str:
|
||||
from metagpt.memory.brain_memory import BrainMemory
|
||||
|
||||
memory = BrainMemory(llm_type=LLMType.OPENAI.value, historical_summary=text, cacheable=False)
|
||||
return await memory.summarize(llm=self, max_words=max_words, keep_language=keep_language)
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue