mirror of
https://github.com/FoundationAgents/MetaGPT.git
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Merge pull request #940 from better629/feat_new_qianfan
Feat add qianfan and dashscope
This commit is contained in:
Alexander Wu
GitHub
commit
d3961a630e
16 changed files with 845 additions and 8 deletions
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@ -13,7 +13,18 @@ from metagpt.logs import logger
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async def main():
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llm = LLM()
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logger.info(await llm.aask("hello world"))
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# llm type check
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question = "what's your name"
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logger.info(f"{question}: ")
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logger.info(await llm.aask(question))
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logger.info("\n\n")
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logger.info(
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await llm.aask(
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"who are you", system_msgs=["act as a robot, just answer 'I'am robot' if the question is 'who are you'"]
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)
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)
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logger.info(await llm.aask_batch(["hi", "write python hello world."]))
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hello_msg = [{"role": "user", "content": "count from 1 to 10. split by newline."}]
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@ -24,6 +24,8 @@ class LLMType(Enum):
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METAGPT = "metagpt"
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AZURE = "azure"
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OLLAMA = "ollama"
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QIANFAN = "qianfan" # Baidu BCE
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DASHSCOPE = "dashscope" # Aliyun LingJi DashScope
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def __missing__(self, key):
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return self.OPENAI
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@ -36,13 +38,18 @@ class LLMConfig(YamlModel):
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Optional Fields in pydantic: https://docs.pydantic.dev/latest/migration/#required-optional-and-nullable-fields
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"""
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api_key: str
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api_key: str = "sk-"
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api_type: LLMType = LLMType.OPENAI
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base_url: str = "https://api.openai.com/v1"
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api_version: Optional[str] = None
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model: Optional[str] = None # also stands for DEPLOYMENT_NAME
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# For Cloud Service Provider like Baidu/ Alibaba
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access_key: Optional[str] = None
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secret_key: Optional[str] = None
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endpoint: Optional[str] = None # for self-deployed model on the cloud
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# For Spark(Xunfei), maybe remove later
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app_id: Optional[str] = None
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api_secret: Optional[str] = None
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@ -16,6 +16,8 @@ from metagpt.provider.azure_openai_api import AzureOpenAILLM
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from metagpt.provider.metagpt_api import MetaGPTLLM
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from metagpt.provider.human_provider import HumanProvider
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from metagpt.provider.spark_api import SparkLLM
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from metagpt.provider.qianfan_api import QianFanLLM
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from metagpt.provider.dashscope_api import DashScopeLLM
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__all__ = [
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"FireworksLLM",
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@ -28,4 +30,6 @@ __all__ = [
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"OllamaLLM",
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"HumanProvider",
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"SparkLLM",
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"QianFanLLM",
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"DashScopeLLM",
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]
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@ -11,11 +11,12 @@ from abc import ABC, abstractmethod
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from typing import Optional, Union
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from openai import AsyncOpenAI
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from pydantic import BaseModel
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from metagpt.configs.llm_config import LLMConfig
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from metagpt.logs import logger
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from metagpt.schema import Message
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from metagpt.utils.cost_manager import CostManager
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from metagpt.utils.cost_manager import CostManager, Costs
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class BaseLLM(ABC):
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@ -67,6 +68,28 @@ class BaseLLM(ABC):
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def _default_system_msg(self):
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return self._system_msg(self.system_prompt)
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def _update_costs(self, usage: Union[dict, BaseModel], model: str = None, local_calc_usage: bool = True):
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"""update each request's token cost
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Args:
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model (str): model name or in some scenarios called endpoint
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local_calc_usage (bool): some models don't calculate usage, it will overwrite LLMConfig.calc_usage
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"""
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calc_usage = self.config.calc_usage and local_calc_usage
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model = model or self.model
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usage = usage.model_dump() if isinstance(usage, BaseModel) else usage
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if calc_usage and self.cost_manager:
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try:
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prompt_tokens = int(usage.get("prompt_tokens", 0))
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completion_tokens = int(usage.get("completion_tokens", 0))
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self.cost_manager.update_cost(prompt_tokens, completion_tokens, model)
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except Exception as e:
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logger.error(f"{self.__class__.__name__} updates costs failed! exp: {e}")
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def get_costs(self) -> Costs:
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if not self.cost_manager:
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return Costs(0, 0, 0, 0)
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return self.cost_manager.get_costs()
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async def aask(
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self,
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msg: str,
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248
metagpt/provider/dashscope_api.py
Normal file
248
metagpt/provider/dashscope_api.py
Normal file
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@ -0,0 +1,248 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# @Desc :
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import json
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from http import HTTPStatus
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from typing import Any, AsyncGenerator, Dict, List, Union
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import dashscope
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from dashscope.aigc.generation import Generation
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from dashscope.api_entities.aiohttp_request import AioHttpRequest
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from dashscope.api_entities.api_request_data import ApiRequestData
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from dashscope.api_entities.api_request_factory import _get_protocol_params
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from dashscope.api_entities.dashscope_response import (
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GenerationOutput,
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GenerationResponse,
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Message,
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)
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from dashscope.client.base_api import BaseAioApi
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from dashscope.common.constants import SERVICE_API_PATH, ApiProtocol
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from dashscope.common.error import (
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InputDataRequired,
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InputRequired,
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ModelRequired,
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UnsupportedApiProtocol,
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)
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from tenacity import (
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after_log,
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retry,
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retry_if_exception_type,
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stop_after_attempt,
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wait_random_exponential,
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)
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from metagpt.logs import log_llm_stream, logger
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from metagpt.provider.base_llm import BaseLLM, LLMConfig
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from metagpt.provider.llm_provider_registry import LLMType, register_provider
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from metagpt.provider.openai_api import log_and_reraise
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from metagpt.utils.cost_manager import CostManager
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from metagpt.utils.token_counter import DASHSCOPE_TOKEN_COSTS
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def build_api_arequest(
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model: str, input: object, task_group: str, task: str, function: str, api_key: str, is_service=True, **kwargs
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):
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(
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api_protocol,
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ws_stream_mode,
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is_binary_input,
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http_method,
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stream,
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async_request,
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query,
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headers,
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request_timeout,
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form,
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resources,
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) = _get_protocol_params(kwargs)
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task_id = kwargs.pop("task_id", None)
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if api_protocol in [ApiProtocol.HTTP, ApiProtocol.HTTPS]:
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if not dashscope.base_http_api_url.endswith("/"):
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http_url = dashscope.base_http_api_url + "/"
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else:
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http_url = dashscope.base_http_api_url
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if is_service:
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http_url = http_url + SERVICE_API_PATH + "/"
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if task_group:
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http_url += "%s/" % task_group
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if task:
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http_url += "%s/" % task
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if function:
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http_url += function
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request = AioHttpRequest(
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url=http_url,
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api_key=api_key,
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http_method=http_method,
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stream=stream,
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async_request=async_request,
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query=query,
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timeout=request_timeout,
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task_id=task_id,
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)
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else:
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raise UnsupportedApiProtocol("Unsupported protocol: %s, support [http, https, websocket]" % api_protocol)
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if headers is not None:
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request.add_headers(headers=headers)
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if input is None and form is None:
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raise InputDataRequired("There is no input data and form data")
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request_data = ApiRequestData(
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model,
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task_group=task_group,
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task=task,
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function=function,
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input=input,
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form=form,
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is_binary_input=is_binary_input,
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api_protocol=api_protocol,
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)
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request_data.add_resources(resources)
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request_data.add_parameters(**kwargs)
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request.data = request_data
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return request
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class AGeneration(Generation, BaseAioApi):
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@classmethod
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async def acall(
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cls,
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model: str,
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prompt: Any = None,
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history: list = None,
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api_key: str = None,
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messages: List[Message] = None,
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plugins: Union[str, Dict[str, Any]] = None,
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**kwargs,
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) -> Union[GenerationResponse, AsyncGenerator[GenerationResponse, None]]:
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if (prompt is None or not prompt) and (messages is None or not messages):
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raise InputRequired("prompt or messages is required!")
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if model is None or not model:
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raise ModelRequired("Model is required!")
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task_group, function = "aigc", "generation" # fixed value
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if plugins is not None:
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headers = kwargs.pop("headers", {})
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if isinstance(plugins, str):
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headers["X-DashScope-Plugin"] = plugins
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else:
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headers["X-DashScope-Plugin"] = json.dumps(plugins)
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kwargs["headers"] = headers
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input, parameters = cls._build_input_parameters(model, prompt, history, messages, **kwargs)
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api_key, model = BaseAioApi._validate_params(api_key, model)
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request = build_api_arequest(
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model=model,
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input=input,
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task_group=task_group,
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task=Generation.task,
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function=function,
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api_key=api_key,
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**kwargs,
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)
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response = await request.aio_call()
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is_stream = kwargs.get("stream", False)
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if is_stream:
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async def aresp_iterator(response):
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async for resp in response:
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yield GenerationResponse.from_api_response(resp)
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return aresp_iterator(response)
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else:
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return GenerationResponse.from_api_response(response)
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@register_provider(LLMType.DASHSCOPE)
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class DashScopeLLM(BaseLLM):
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def __init__(self, llm_config: LLMConfig):
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self.config = llm_config
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self.use_system_prompt = False # only some models support system_prompt
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self.__init_dashscope()
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self.cost_manager = CostManager(token_costs=self.token_costs)
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def __init_dashscope(self):
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self.model = self.config.model
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self.api_key = self.config.api_key
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self.token_costs = DASHSCOPE_TOKEN_COSTS
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self.aclient: AGeneration = AGeneration
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# check support system_message models
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support_system_models = [
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"qwen-", # all support
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"llama2-", # all support
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"baichuan2-7b-chat-v1",
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"chatglm3-6b",
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]
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for support_model in support_system_models:
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if support_model in self.model:
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self.use_system_prompt = True
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def _const_kwargs(self, messages: list[dict], stream: bool = False) -> dict:
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kwargs = {
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"api_key": self.api_key,
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"model": self.model,
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"messages": messages,
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"stream": stream,
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"result_format": "message",
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}
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if self.config.temperature > 0:
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# different model has default temperature. only set when it"s specified.
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kwargs["temperature"] = self.config.temperature
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if stream:
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kwargs["incremental_output"] = True
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return kwargs
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def _check_response(self, resp: GenerationResponse):
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if resp.status_code != HTTPStatus.OK:
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raise RuntimeError(f"code: {resp.code}, request_id: {resp.request_id}, message: {resp.message}")
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def get_choice_text(self, output: GenerationOutput) -> str:
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return output.get("choices", [{}])[0].get("message", {}).get("content", "")
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def completion(self, messages: list[dict]) -> GenerationOutput:
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resp: GenerationResponse = self.aclient.call(**self._const_kwargs(messages, stream=False))
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self._check_response(resp)
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self._update_costs(dict(resp.usage))
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return resp.output
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async def _achat_completion(self, messages: list[dict]) -> GenerationOutput:
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resp: GenerationResponse = await self.aclient.acall(**self._const_kwargs(messages, stream=False))
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self._check_response(resp)
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self._update_costs(dict(resp.usage))
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return resp.output
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async def acompletion(self, messages: list[dict], timeout=3) -> GenerationOutput:
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return await self._achat_completion(messages)
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async def _achat_completion_stream(self, messages: list[dict]) -> str:
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resp = await self.aclient.acall(**self._const_kwargs(messages, stream=True))
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collected_content = []
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usage = {}
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async for chunk in resp:
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self._check_response(chunk)
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content = chunk.output.choices[0]["message"]["content"]
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usage = dict(chunk.usage) # each chunk has usage
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log_llm_stream(content)
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collected_content.append(content)
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log_llm_stream("\n")
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self._update_costs(usage)
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full_content = "".join(collected_content)
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return full_content
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_random_exponential(min=1, max=60),
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after=after_log(logger, logger.level("WARNING").name),
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retry=retry_if_exception_type(ConnectionError),
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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, timeout: int = 3) -> str:
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if stream:
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return await self._achat_completion_stream(messages)
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resp = await self._achat_completion(messages)
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return self.get_choice_text(resp)
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@ -16,7 +16,7 @@ from tenacity import (
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)
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from metagpt.configs.llm_config import LLMConfig, LLMType
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from metagpt.logs import logger
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from metagpt.logs import log_llm_stream, logger
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from metagpt.provider.llm_provider_registry import register_provider
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from metagpt.provider.openai_api import OpenAILLM, log_and_reraise
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from metagpt.utils.cost_manager import CostManager, Costs
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@ -107,10 +107,11 @@ class FireworksLLM(OpenAILLM):
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finish_reason = choice.finish_reason if hasattr(choice, "finish_reason") else None
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if choice_delta.content:
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collected_content.append(choice_delta.content)
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print(choice_delta.content, end="")
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log_llm_stream(choice_delta.content)
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if finish_reason:
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# fireworks api return usage when finish_reason is not None
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usage = CompletionUsage(**chunk.usage)
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log_llm_stream("\n")
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full_content = "".join(collected_content)
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self._update_costs(usage)
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|
|
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152
metagpt/provider/qianfan_api.py
Normal file
152
metagpt/provider/qianfan_api.py
Normal file
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@ -0,0 +1,152 @@
|
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# @Desc : llm api of qianfan from Baidu, supports ERNIE(wen xin yi yan) and opensource models
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import copy
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import os
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import qianfan
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from qianfan import ChatCompletion
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from qianfan.resources.typing import JsonBody
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from tenacity import (
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after_log,
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retry,
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retry_if_exception_type,
|
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stop_after_attempt,
|
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wait_random_exponential,
|
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)
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from metagpt.configs.llm_config import LLMConfig, LLMType
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from metagpt.logs import log_llm_stream, logger
|
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from metagpt.provider.base_llm import BaseLLM
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from metagpt.provider.llm_provider_registry import register_provider
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from metagpt.provider.openai_api import log_and_reraise
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from metagpt.utils.cost_manager import CostManager
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from metagpt.utils.token_counter import (
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QIANFAN_ENDPOINT_TOKEN_COSTS,
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QIANFAN_MODEL_TOKEN_COSTS,
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)
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|
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|
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@register_provider(LLMType.QIANFAN)
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class QianFanLLM(BaseLLM):
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"""
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Refs
|
||||
Auth: https://cloud.baidu.com/doc/WENXINWORKSHOP/s/3lmokh7n6#%E3%80%90%E6%8E%A8%E8%8D%90%E3%80%91%E4%BD%BF%E7%94%A8%E5%AE%89%E5%85%A8%E8%AE%A4%E8%AF%81aksk%E9%89%B4%E6%9D%83%E8%B0%83%E7%94%A8%E6%B5%81%E7%A8%8B
|
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Token Price: https://cloud.baidu.com/doc/WENXINWORKSHOP/s/hlrk4akp7#tokens%E5%90%8E%E4%BB%98%E8%B4%B9
|
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Models: https://cloud.baidu.com/doc/WENXINWORKSHOP/s/wlmhm7vuo#%E5%AF%B9%E8%AF%9Dchat
|
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https://cloud.baidu.com/doc/WENXINWORKSHOP/s/xlmokikxe#%E6%94%AF%E6%8C%81%E6%A8%A1%E5%9E%8B%E5%88%97%E8%A1%A8
|
||||
"""
|
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|
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def __init__(self, config: LLMConfig):
|
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self.config = config
|
||||
self.use_system_prompt = False # only some ERNIE-x related models support system_prompt
|
||||
self.__init_qianfan()
|
||||
self.cost_manager = CostManager(token_costs=self.token_costs)
|
||||
|
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def __init_qianfan(self):
|
||||
if self.config.access_key and self.config.secret_key:
|
||||
# for system level auth, use access_key and secret_key, recommended by official
|
||||
# set environment variable due to official recommendation
|
||||
os.environ.setdefault("QIANFAN_ACCESS_KEY", self.config.access_key)
|
||||
os.environ.setdefault("QIANFAN_SECRET_KEY", self.config.secret_key)
|
||||
elif self.config.api_key and self.config.secret_key:
|
||||
# for application level auth, use api_key and secret_key
|
||||
# set environment variable due to official recommendation
|
||||
os.environ.setdefault("QIANFAN_AK", self.config.api_key)
|
||||
os.environ.setdefault("QIANFAN_SK", self.config.secret_key)
|
||||
else:
|
||||
raise ValueError("Set the `access_key`&`secret_key` or `api_key`&`secret_key` first")
|
||||
|
||||
support_system_pairs = [
|
||||
("ERNIE-Bot-4", "completions_pro"), # (model, corresponding-endpoint)
|
||||
("ERNIE-Bot-8k", "ernie_bot_8k"),
|
||||
("ERNIE-Bot", "completions"),
|
||||
("ERNIE-Bot-turbo", "eb-instant"),
|
||||
("ERNIE-Speed", "ernie_speed"),
|
||||
("EB-turbo-AppBuilder", "ai_apaas"),
|
||||
]
|
||||
if self.config.model in [pair[0] for pair in support_system_pairs]:
|
||||
# only some ERNIE models support
|
||||
self.use_system_prompt = True
|
||||
if self.config.endpoint in [pair[1] for pair in support_system_pairs]:
|
||||
self.use_system_prompt = True
|
||||
|
||||
assert not (self.config.model and self.config.endpoint), "Only set `model` or `endpoint` in the config"
|
||||
assert self.config.model or self.config.endpoint, "Should set one of `model` or `endpoint` in the config"
|
||||
|
||||
self.token_costs = copy.deepcopy(QIANFAN_MODEL_TOKEN_COSTS)
|
||||
self.token_costs.update(QIANFAN_ENDPOINT_TOKEN_COSTS)
|
||||
|
||||
# self deployed model on the cloud not to calculate usage, it charges resource pool rental fee
|
||||
self.calc_usage = self.config.calc_usage and self.config.endpoint is None
|
||||
self.aclient: ChatCompletion = qianfan.ChatCompletion()
|
||||
|
||||
def _const_kwargs(self, messages: list[dict], stream: bool = False) -> dict:
|
||||
kwargs = {
|
||||
"messages": messages,
|
||||
"stream": stream,
|
||||
}
|
||||
if self.config.temperature > 0:
|
||||
# different model has default temperature. only set when it's specified.
|
||||
kwargs["temperature"] = self.config.temperature
|
||||
if self.config.endpoint:
|
||||
kwargs["endpoint"] = self.config.endpoint
|
||||
elif self.config.model:
|
||||
kwargs["model"] = self.config.model
|
||||
|
||||
if self.use_system_prompt:
|
||||
# if the model support system prompt, extract and pass it
|
||||
if messages[0]["role"] == "system":
|
||||
kwargs["messages"] = messages[1:]
|
||||
kwargs["system"] = messages[0]["content"] # set system prompt here
|
||||
return kwargs
|
||||
|
||||
def _update_costs(self, usage: dict):
|
||||
"""update each request's token cost"""
|
||||
model_or_endpoint = self.config.model or self.config.endpoint
|
||||
local_calc_usage = model_or_endpoint in self.token_costs
|
||||
super()._update_costs(usage, model_or_endpoint, local_calc_usage)
|
||||
|
||||
def get_choice_text(self, resp: JsonBody) -> str:
|
||||
return resp.get("result", "")
|
||||
|
||||
def completion(self, messages: list[dict]) -> JsonBody:
|
||||
resp = self.aclient.do(**self._const_kwargs(messages=messages, stream=False))
|
||||
self._update_costs(resp.body.get("usage", {}))
|
||||
return resp.body
|
||||
|
||||
async def _achat_completion(self, messages: list[dict]) -> JsonBody:
|
||||
resp = await self.aclient.ado(**self._const_kwargs(messages=messages, stream=False))
|
||||
self._update_costs(resp.body.get("usage", {}))
|
||||
return resp.body
|
||||
|
||||
async def acompletion(self, messages: list[dict], timeout=3) -> JsonBody:
|
||||
return await self._achat_completion(messages)
|
||||
|
||||
async def _achat_completion_stream(self, messages: list[dict]) -> str:
|
||||
resp = await self.aclient.ado(**self._const_kwargs(messages=messages, stream=True))
|
||||
collected_content = []
|
||||
usage = {}
|
||||
async for chunk in resp:
|
||||
content = chunk.body.get("result", "")
|
||||
usage = chunk.body.get("usage", {})
|
||||
log_llm_stream(content)
|
||||
collected_content.append(content)
|
||||
log_llm_stream("\n")
|
||||
|
||||
self._update_costs(usage)
|
||||
full_content = "".join(collected_content)
|
||||
return full_content
|
||||
|
||||
@retry(
|
||||
stop=stop_after_attempt(3),
|
||||
wait=wait_random_exponential(min=1, max=60),
|
||||
after=after_log(logger, logger.level("WARNING").name),
|
||||
retry=retry_if_exception_type(ConnectionError),
|
||||
retry_error_callback=log_and_reraise,
|
||||
)
|
||||
async def acompletion_text(self, messages: list[dict], stream=False, timeout: int = 3) -> str:
|
||||
if stream:
|
||||
return await self._achat_completion_stream(messages)
|
||||
resp = await self._achat_completion(messages)
|
||||
return self.get_choice_text(resp)
|
||||
|
|
@ -29,6 +29,7 @@ class CostManager(BaseModel):
|
|||
total_budget: float = 0
|
||||
max_budget: float = 10.0
|
||||
total_cost: float = 0
|
||||
token_costs: dict[str, dict[str, float]] = TOKEN_COSTS # different model's token cost
|
||||
|
||||
def update_cost(self, prompt_tokens, completion_tokens, model):
|
||||
"""
|
||||
|
|
@ -41,12 +42,13 @@ class CostManager(BaseModel):
|
|||
"""
|
||||
self.total_prompt_tokens += prompt_tokens
|
||||
self.total_completion_tokens += completion_tokens
|
||||
if model not in TOKEN_COSTS:
|
||||
if model not in self.token_costs:
|
||||
logger.warning(f"Model {model} not found in TOKEN_COSTS.")
|
||||
return
|
||||
|
||||
cost = (
|
||||
prompt_tokens * TOKEN_COSTS[model]["prompt"] + completion_tokens * TOKEN_COSTS[model]["completion"]
|
||||
prompt_tokens * self.token_costs[model]["prompt"]
|
||||
+ completion_tokens * self.token_costs[model]["completion"]
|
||||
) / 1000
|
||||
self.total_cost += cost
|
||||
logger.info(
|
||||
|
|
|
|||
|
|
@ -119,6 +119,7 @@ def repair_json_format(output: str) -> str:
|
|||
logger.info(f"repair_json_format: {'}]'}")
|
||||
elif output.startswith("{") and output.endswith("]"):
|
||||
output = output[:-1] + "}"
|
||||
|
||||
# remove comments in output json string, after json value content, maybe start with #, maybe start with //
|
||||
arr = output.split("\n")
|
||||
new_arr = []
|
||||
|
|
@ -208,6 +209,17 @@ def repair_invalid_json(output: str, error: str) -> str:
|
|||
elif (rline[col_no] in ["'", '"']) and (line.startswith('"') or line.startswith("'")) and "," not in line:
|
||||
# problem, `"""` or `'''` without `,`
|
||||
new_line = f",{line}"
|
||||
elif col_no - 1 >= 0 and rline[col_no - 1] in ['"', "'"]:
|
||||
# backslash problem like \" in the output
|
||||
char = rline[col_no - 1]
|
||||
nearest_char_idx = rline[col_no:].find(char)
|
||||
new_line = (
|
||||
rline[: col_no - 1]
|
||||
+ "\\"
|
||||
+ rline[col_no - 1 : col_no + nearest_char_idx]
|
||||
+ "\\"
|
||||
+ rline[col_no + nearest_char_idx :]
|
||||
)
|
||||
elif '",' not in line and "," not in line and '"' not in line:
|
||||
new_line = f'{line}",'
|
||||
elif not line.endswith(","):
|
||||
|
|
|
|||
|
|
@ -38,6 +38,88 @@ TOKEN_COSTS = {
|
|||
}
|
||||
|
||||
|
||||
"""
|
||||
QianFan Token Price https://cloud.baidu.com/doc/WENXINWORKSHOP/s/hlrk4akp7#tokens%E5%90%8E%E4%BB%98%E8%B4%B9
|
||||
Due to QianFan has multi price strategies, we unify `Tokens post-payment` as a statistical method.
|
||||
"""
|
||||
QIANFAN_MODEL_TOKEN_COSTS = {
|
||||
"ERNIE-Bot-4": {"prompt": 0.017, "completion": 0.017},
|
||||
"ERNIE-Bot-8k": {"prompt": 0.0034, "completion": 0.0067},
|
||||
"ERNIE-Bot": {"prompt": 0.0017, "completion": 0.0017},
|
||||
"ERNIE-Bot-turbo": {"prompt": 0.0011, "completion": 0.0011},
|
||||
"EB-turbo-AppBuilder": {"prompt": 0.0011, "completion": 0.0011},
|
||||
"ERNIE-Speed": {"prompt": 0.00056, "completion": 0.0011},
|
||||
"BLOOMZ-7B": {"prompt": 0.00056, "completion": 0.00056},
|
||||
"Llama-2-7B-Chat": {"prompt": 0.00056, "completion": 0.00056},
|
||||
"Llama-2-13B-Chat": {"prompt": 0.00084, "completion": 0.00084},
|
||||
"Llama-2-70B-Chat": {"prompt": 0.0049, "completion": 0.0049},
|
||||
"ChatGLM2-6B-32K": {"prompt": 0.00056, "completion": 0.00056},
|
||||
"AquilaChat-7B": {"prompt": 0.00056, "completion": 0.00056},
|
||||
"Mixtral-8x7B-Instruct": {"prompt": 0.0049, "completion": 0.0049},
|
||||
"SQLCoder-7B": {"prompt": 0.00056, "completion": 0.00056},
|
||||
"CodeLlama-7B-Instruct": {"prompt": 0.00056, "completion": 0.00056},
|
||||
"XuanYuan-70B-Chat-4bit": {"prompt": 0.0049, "completion": 0.0049},
|
||||
"Qianfan-BLOOMZ-7B-compressed": {"prompt": 0.00056, "completion": 0.00056},
|
||||
"Qianfan-Chinese-Llama-2-7B": {"prompt": 0.00056, "completion": 0.00056},
|
||||
"Qianfan-Chinese-Llama-2-13B": {"prompt": 0.00084, "completion": 0.00084},
|
||||
"ChatLaw": {"prompt": 0.0011, "completion": 0.0011},
|
||||
"Yi-34B-Chat": {"prompt": 0.0, "completion": 0.0},
|
||||
}
|
||||
|
||||
QIANFAN_ENDPOINT_TOKEN_COSTS = {
|
||||
"completions_pro": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Bot-4"],
|
||||
"ernie_bot_8k": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Bot-8k"],
|
||||
"completions": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Bot"],
|
||||
"eb-instant": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Bot-turbo"],
|
||||
"ai_apaas": QIANFAN_MODEL_TOKEN_COSTS["EB-turbo-AppBuilder"],
|
||||
"ernie_speed": QIANFAN_MODEL_TOKEN_COSTS["ERNIE-Speed"],
|
||||
"bloomz_7b1": QIANFAN_MODEL_TOKEN_COSTS["BLOOMZ-7B"],
|
||||
"llama_2_7b": QIANFAN_MODEL_TOKEN_COSTS["Llama-2-7B-Chat"],
|
||||
"llama_2_13b": QIANFAN_MODEL_TOKEN_COSTS["Llama-2-13B-Chat"],
|
||||
"llama_2_70b": QIANFAN_MODEL_TOKEN_COSTS["Llama-2-70B-Chat"],
|
||||
"chatglm2_6b_32k": QIANFAN_MODEL_TOKEN_COSTS["ChatGLM2-6B-32K"],
|
||||
"aquilachat_7b": QIANFAN_MODEL_TOKEN_COSTS["AquilaChat-7B"],
|
||||
"mixtral_8x7b_instruct": QIANFAN_MODEL_TOKEN_COSTS["Mixtral-8x7B-Instruct"],
|
||||
"sqlcoder_7b": QIANFAN_MODEL_TOKEN_COSTS["SQLCoder-7B"],
|
||||
"codellama_7b_instruct": QIANFAN_MODEL_TOKEN_COSTS["CodeLlama-7B-Instruct"],
|
||||
"xuanyuan_70b_chat": QIANFAN_MODEL_TOKEN_COSTS["XuanYuan-70B-Chat-4bit"],
|
||||
"qianfan_bloomz_7b_compressed": QIANFAN_MODEL_TOKEN_COSTS["Qianfan-BLOOMZ-7B-compressed"],
|
||||
"qianfan_chinese_llama_2_7b": QIANFAN_MODEL_TOKEN_COSTS["Qianfan-Chinese-Llama-2-7B"],
|
||||
"qianfan_chinese_llama_2_13b": QIANFAN_MODEL_TOKEN_COSTS["Qianfan-Chinese-Llama-2-13B"],
|
||||
"chatlaw": QIANFAN_MODEL_TOKEN_COSTS["ChatLaw"],
|
||||
"yi_34b_chat": QIANFAN_MODEL_TOKEN_COSTS["Yi-34B-Chat"],
|
||||
}
|
||||
|
||||
"""
|
||||
DashScope Token price https://help.aliyun.com/zh/dashscope/developer-reference/tongyi-thousand-questions-metering-and-billing
|
||||
Different model has different detail page. Attention, some model are free for a limited time.
|
||||
"""
|
||||
DASHSCOPE_TOKEN_COSTS = {
|
||||
"qwen-turbo": {"prompt": 0.0011, "completion": 0.0011},
|
||||
"qwen-plus": {"prompt": 0.0028, "completion": 0.0028},
|
||||
"qwen-max": {"prompt": 0.0, "completion": 0.0},
|
||||
"qwen-max-1201": {"prompt": 0.0, "completion": 0.0},
|
||||
"qwen-max-longcontext": {"prompt": 0.0, "completion": 0.0},
|
||||
"llama2-7b-chat-v2": {"prompt": 0.0, "completion": 0.0},
|
||||
"llama2-13b-chat-v2": {"prompt": 0.0, "completion": 0.0},
|
||||
"qwen-72b-chat": {"prompt": 0.0, "completion": 0.0},
|
||||
"qwen-14b-chat": {"prompt": 0.0011, "completion": 0.0011},
|
||||
"qwen-7b-chat": {"prompt": 0.00084, "completion": 0.00084},
|
||||
"qwen-1.8b-chat": {"prompt": 0.0, "completion": 0.0},
|
||||
"baichuan2-13b-chat-v1": {"prompt": 0.0011, "completion": 0.0011},
|
||||
"baichuan2-7b-chat-v1": {"prompt": 0.00084, "completion": 0.00084},
|
||||
"baichuan-7b-v1": {"prompt": 0.0, "completion": 0.0},
|
||||
"chatglm-6b-v2": {"prompt": 0.0011, "completion": 0.0011},
|
||||
"chatglm3-6b": {"prompt": 0.0, "completion": 0.0},
|
||||
"ziya-llama-13b-v1": {"prompt": 0.0, "completion": 0.0}, # no price page, judge it as free
|
||||
"dolly-12b-v2": {"prompt": 0.0, "completion": 0.0},
|
||||
"belle-llama-13b-2m-v1": {"prompt": 0.0, "completion": 0.0},
|
||||
"moss-moon-003-sft-v1": {"prompt": 0.0, "completion": 0.0},
|
||||
"chatyuan-large-v2": {"prompt": 0.0, "completion": 0.0},
|
||||
"billa-7b-sft-v1": {"prompt": 0.0, "completion": 0.0},
|
||||
}
|
||||
|
||||
|
||||
TOKEN_MAX = {
|
||||
"gpt-3.5-turbo": 4096,
|
||||
"gpt-3.5-turbo-0301": 4096,
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ python_docx==0.8.11
|
|||
PyYAML==6.0.1
|
||||
# sentence_transformers==2.2.2
|
||||
setuptools==65.6.3
|
||||
tenacity==8.2.2
|
||||
tenacity==8.2.3
|
||||
tiktoken==0.5.2
|
||||
tqdm==4.65.0
|
||||
#unstructured[local-inference]
|
||||
|
|
@ -68,3 +68,5 @@ anytree
|
|||
ipywidgets==8.1.1
|
||||
Pillow
|
||||
imap_tools==1.5.0 # Used by metagpt/tools/libs/email_login.py
|
||||
qianfan==0.3.2
|
||||
dashscope==1.14.1
|
||||
|
|
|
|||
|
|
@ -42,3 +42,17 @@ mock_llm_config_zhipu = LLMConfig(
|
|||
model="mock_zhipu_model",
|
||||
proxy="http://localhost:8080",
|
||||
)
|
||||
|
||||
|
||||
mock_llm_config_spark = LLMConfig(
|
||||
api_type="spark",
|
||||
app_id="xxx",
|
||||
api_key="xxx",
|
||||
api_secret="xxx",
|
||||
domain="generalv2",
|
||||
base_url="wss://spark-api.xf-yun.com/v3.1/chat",
|
||||
)
|
||||
|
||||
mock_llm_config_qianfan = LLMConfig(api_type="qianfan", access_key="xxx", secret_key="xxx", model="ERNIE-Bot-turbo")
|
||||
|
||||
mock_llm_config_dashscope = LLMConfig(api_type="dashscope", api_key="xxx", model="qwen-max")
|
||||
|
|
|
|||
145
tests/metagpt/provider/req_resp_const.py
Normal file
145
tests/metagpt/provider/req_resp_const.py
Normal file
|
|
@ -0,0 +1,145 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : default request & response data for provider unittest
|
||||
|
||||
|
||||
from dashscope.api_entities.dashscope_response import (
|
||||
DashScopeAPIResponse,
|
||||
GenerationOutput,
|
||||
GenerationResponse,
|
||||
GenerationUsage,
|
||||
)
|
||||
from openai.types.chat.chat_completion import (
|
||||
ChatCompletion,
|
||||
ChatCompletionMessage,
|
||||
Choice,
|
||||
)
|
||||
from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
|
||||
from openai.types.chat.chat_completion_chunk import Choice as AChoice
|
||||
from openai.types.chat.chat_completion_chunk import ChoiceDelta
|
||||
from openai.types.completion_usage import CompletionUsage
|
||||
from qianfan.resources.typing import QfResponse
|
||||
|
||||
from metagpt.provider.base_llm import BaseLLM
|
||||
|
||||
prompt = "who are you?"
|
||||
messages = [{"role": "user", "content": prompt}]
|
||||
|
||||
resp_cont_tmpl = "I'm {name}"
|
||||
default_resp_cont = resp_cont_tmpl.format(name="GPT")
|
||||
|
||||
|
||||
# part of whole ChatCompletion of openai like structure
|
||||
def get_part_chat_completion(name: str) -> dict:
|
||||
part_chat_completion = {
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": resp_cont_tmpl.format(name=name),
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
}
|
||||
],
|
||||
"usage": {"completion_tokens": 22, "prompt_tokens": 19, "total_tokens": 41},
|
||||
}
|
||||
return part_chat_completion
|
||||
|
||||
|
||||
def get_openai_chat_completion(name: str) -> ChatCompletion:
|
||||
openai_chat_completion = ChatCompletion(
|
||||
id="cmpl-a6652c1bb181caae8dd19ad8",
|
||||
model="xx/xxx",
|
||||
object="chat.completion",
|
||||
created=1703300855,
|
||||
choices=[
|
||||
Choice(
|
||||
finish_reason="stop",
|
||||
index=0,
|
||||
message=ChatCompletionMessage(role="assistant", content=resp_cont_tmpl.format(name=name)),
|
||||
logprobs=None,
|
||||
)
|
||||
],
|
||||
usage=CompletionUsage(completion_tokens=110, prompt_tokens=92, total_tokens=202),
|
||||
)
|
||||
return openai_chat_completion
|
||||
|
||||
|
||||
def get_openai_chat_completion_chunk(name: str, usage_as_dict: bool = False) -> ChatCompletionChunk:
|
||||
usage = CompletionUsage(completion_tokens=110, prompt_tokens=92, total_tokens=202)
|
||||
usage = usage if not usage_as_dict else usage.model_dump()
|
||||
openai_chat_completion_chunk = ChatCompletionChunk(
|
||||
id="cmpl-a6652c1bb181caae8dd19ad8",
|
||||
model="xx/xxx",
|
||||
object="chat.completion.chunk",
|
||||
created=1703300855,
|
||||
choices=[
|
||||
AChoice(
|
||||
delta=ChoiceDelta(role="assistant", content=resp_cont_tmpl.format(name=name)),
|
||||
finish_reason="stop",
|
||||
index=0,
|
||||
logprobs=None,
|
||||
)
|
||||
],
|
||||
usage=usage,
|
||||
)
|
||||
return openai_chat_completion_chunk
|
||||
|
||||
|
||||
# For gemini
|
||||
gemini_messages = [{"role": "user", "parts": prompt}]
|
||||
|
||||
|
||||
# For QianFan
|
||||
qf_jsonbody_dict = {
|
||||
"id": "as-4v1h587fyv",
|
||||
"object": "chat.completion",
|
||||
"created": 1695021339,
|
||||
"result": "",
|
||||
"is_truncated": False,
|
||||
"need_clear_history": False,
|
||||
"usage": {"prompt_tokens": 7, "completion_tokens": 15, "total_tokens": 22},
|
||||
}
|
||||
|
||||
|
||||
def get_qianfan_response(name: str) -> QfResponse:
|
||||
qf_jsonbody_dict["result"] = resp_cont_tmpl.format(name=name)
|
||||
return QfResponse(code=200, body=qf_jsonbody_dict)
|
||||
|
||||
|
||||
# For DashScope
|
||||
def get_dashscope_response(name: str) -> GenerationResponse:
|
||||
return GenerationResponse.from_api_response(
|
||||
DashScopeAPIResponse(
|
||||
status_code=200,
|
||||
output=GenerationOutput(
|
||||
**{
|
||||
"text": "",
|
||||
"finish_reason": "",
|
||||
"choices": [
|
||||
{
|
||||
"finish_reason": "stop",
|
||||
"message": {"role": "assistant", "content": resp_cont_tmpl.format(name=name)},
|
||||
}
|
||||
],
|
||||
}
|
||||
),
|
||||
usage=GenerationUsage(**{"input_tokens": 12, "output_tokens": 98, "total_tokens": 110}),
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
# For llm general chat functions call
|
||||
async def llm_general_chat_funcs_test(llm: BaseLLM, prompt: str, messages: list[dict], resp_cont: str):
|
||||
resp = await llm.aask(prompt, stream=False)
|
||||
assert resp == resp_cont
|
||||
|
||||
resp = await llm.aask(prompt)
|
||||
assert resp == resp_cont
|
||||
|
||||
resp = await llm.acompletion_text(messages, stream=False)
|
||||
assert resp == resp_cont
|
||||
|
||||
resp = await llm.acompletion_text(messages, stream=True)
|
||||
assert resp == resp_cont
|
||||
73
tests/metagpt/provider/test_dashscope_api.py
Normal file
73
tests/metagpt/provider/test_dashscope_api.py
Normal file
|
|
@ -0,0 +1,73 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : the unittest of DashScopeLLM
|
||||
|
||||
from typing import AsyncGenerator, Union
|
||||
|
||||
import pytest
|
||||
from dashscope.api_entities.dashscope_response import GenerationResponse
|
||||
|
||||
from metagpt.provider.dashscope_api import DashScopeLLM
|
||||
from tests.metagpt.provider.mock_llm_config import mock_llm_config_dashscope
|
||||
from tests.metagpt.provider.req_resp_const import (
|
||||
get_dashscope_response,
|
||||
llm_general_chat_funcs_test,
|
||||
messages,
|
||||
prompt,
|
||||
resp_cont_tmpl,
|
||||
)
|
||||
|
||||
name = "qwen-max"
|
||||
resp_cont = resp_cont_tmpl.format(name=name)
|
||||
|
||||
|
||||
@classmethod
|
||||
def mock_dashscope_call(
|
||||
cls,
|
||||
messages: list[dict],
|
||||
model: str,
|
||||
api_key: str,
|
||||
result_format: str,
|
||||
incremental_output: bool = True,
|
||||
stream: bool = False,
|
||||
) -> GenerationResponse:
|
||||
return get_dashscope_response(name)
|
||||
|
||||
|
||||
@classmethod
|
||||
async def mock_dashscope_acall(
|
||||
cls,
|
||||
messages: list[dict],
|
||||
model: str,
|
||||
api_key: str,
|
||||
result_format: str,
|
||||
incremental_output: bool = True,
|
||||
stream: bool = False,
|
||||
) -> Union[AsyncGenerator[GenerationResponse, None], GenerationResponse]:
|
||||
resps = [get_dashscope_response(name)]
|
||||
|
||||
if stream:
|
||||
|
||||
async def aresp_iterator(resps: list[GenerationResponse]):
|
||||
for resp in resps:
|
||||
yield resp
|
||||
|
||||
return aresp_iterator(resps)
|
||||
else:
|
||||
return resps[0]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_dashscope_acompletion(mocker):
|
||||
mocker.patch("dashscope.aigc.generation.Generation.call", mock_dashscope_call)
|
||||
mocker.patch("metagpt.provider.dashscope_api.AGeneration.acall", mock_dashscope_acall)
|
||||
|
||||
dashscope_llm = DashScopeLLM(mock_llm_config_dashscope)
|
||||
|
||||
resp = dashscope_llm.completion(messages)
|
||||
assert resp.choices[0]["message"]["content"] == resp_cont
|
||||
|
||||
resp = await dashscope_llm.acompletion(messages)
|
||||
assert resp.choices[0]["message"]["content"] == resp_cont
|
||||
|
||||
await llm_general_chat_funcs_test(dashscope_llm, prompt, messages, resp_cont)
|
||||
56
tests/metagpt/provider/test_qianfan_api.py
Normal file
56
tests/metagpt/provider/test_qianfan_api.py
Normal file
|
|
@ -0,0 +1,56 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
# @Desc : the unittest of qianfan api
|
||||
|
||||
from typing import AsyncIterator, Union
|
||||
|
||||
import pytest
|
||||
from qianfan.resources.typing import JsonBody, QfResponse
|
||||
|
||||
from metagpt.provider.qianfan_api import QianFanLLM
|
||||
from tests.metagpt.provider.mock_llm_config import mock_llm_config_qianfan
|
||||
from tests.metagpt.provider.req_resp_const import (
|
||||
get_qianfan_response,
|
||||
llm_general_chat_funcs_test,
|
||||
messages,
|
||||
prompt,
|
||||
resp_cont_tmpl,
|
||||
)
|
||||
|
||||
name = "ERNIE-Bot-turbo"
|
||||
resp_cont = resp_cont_tmpl.format(name=name)
|
||||
|
||||
|
||||
def mock_qianfan_do(self, messages: list[dict], model: str, stream: bool = False, system: str = None) -> QfResponse:
|
||||
return get_qianfan_response(name=name)
|
||||
|
||||
|
||||
async def mock_qianfan_ado(
|
||||
self, messages: list[dict], model: str, stream: bool = True, system: str = None
|
||||
) -> Union[QfResponse, AsyncIterator[QfResponse]]:
|
||||
resps = [get_qianfan_response(name=name)]
|
||||
if stream:
|
||||
|
||||
async def aresp_iterator(resps: list[JsonBody]):
|
||||
for resp in resps:
|
||||
yield resp
|
||||
|
||||
return aresp_iterator(resps)
|
||||
else:
|
||||
return resps[0]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_qianfan_acompletion(mocker):
|
||||
mocker.patch("qianfan.resources.llm.chat_completion.ChatCompletion.do", mock_qianfan_do)
|
||||
mocker.patch("qianfan.resources.llm.chat_completion.ChatCompletion.ado", mock_qianfan_ado)
|
||||
|
||||
qianfan_llm = QianFanLLM(mock_llm_config_qianfan)
|
||||
|
||||
resp = qianfan_llm.completion(messages)
|
||||
assert resp.get("result") == resp_cont
|
||||
|
||||
resp = await qianfan_llm.acompletion(messages)
|
||||
assert resp.get("result") == resp_cont
|
||||
|
||||
await llm_general_chat_funcs_test(qianfan_llm, prompt, messages, resp_cont)
|
||||
|
|
@ -211,6 +211,11 @@ value
|
|||
output = repair_invalid_json(output, "Expecting ',' delimiter: line 4 column 1")
|
||||
assert output == target_output
|
||||
|
||||
raw_output = '{"key": "url "http" \\"https\\" "}'
|
||||
target_output = '{"key": "url \\"http\\" \\"https\\" "}'
|
||||
output = repair_invalid_json(raw_output, "Expecting ',' delimiter: line 1 column 15 (char 14)")
|
||||
assert output == target_output
|
||||
|
||||
|
||||
def test_retry_parse_json_text():
|
||||
from metagpt.utils.repair_llm_raw_output import retry_parse_json_text
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue