MetaGPT/metagpt/config.py
geekan 331d74059f 1. 动作优化
1. SummarizeCode动作:用于基于代码进行总结,思考bug、逻辑、todo
  2. CodeReview动作优化:目前强制要求回答问题,有更高的成功率了
2. 数据结构
  1. Document的标准化:Env->Repo->Document,其中Document/Asset/Code都只用Document
    1. 原用于检索的Document改为IndexableDocument
  2. Repo结构引入:用于Document装载与元数据装载
  3. RepoParser引入:写了一个简单的AST parser(后续可能要换tree-sitter),给出了整库symbol
3. 配置优化
  1. 默认更换为gpt-4-1106-preview,以获得最好的效果与成本
  2. 提供~/.metagpt作为配置最高优先级目录,从中读取config.yaml
  3. workspace可以灵活指定了,在config中配置
4. metagpt作为默认命令行,而非python startup.py
  1. 使用新的METAGPT_ROOT生成方式,而非寻找git,以便cli安装
  2. 命令行由fire换为了typer,它会带来相对更好的体验
  3. project_name可以灵活指定了,在metagpt命令行输入中配置
5. 其他
  1. BossRequirement -> UserRequirement
  2. 大量错误文本的修正,增加了可读性
  3. 中量提示词优化,稍微提升了一些准确率
  4. 暂时屏蔽了LongtermMemory相关逻辑,这个逻辑底层调用了langchain的FAISS,会带来~5秒加载耗时
  5. 修复了安装包中的部分描述错误
2023-11-27 15:47:06 +08:00

134 lines
5.6 KiB
Python

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Provide configuration, singleton
"""
import os
import openai
import yaml
from pathlib import Path
from metagpt.const import METAGPT_ROOT, DEFAULT_WORKSPACE_ROOT
from metagpt.logs import logger
from metagpt.tools import SearchEngineType, WebBrowserEngineType
from metagpt.utils.singleton import Singleton
class NotConfiguredException(Exception):
"""Exception raised for errors in the configuration.
Attributes:
message -- explanation of the error
"""
def __init__(self, message="The required configuration is not set"):
self.message = message
super().__init__(self.message)
class Config(metaclass=Singleton):
"""
Regular usage method:
config = Config("config.yaml")
secret_key = config.get_key("MY_SECRET_KEY")
print("Secret key:", secret_key)
"""
_instance = None
home_yaml_file = Path.home() / ".metagpt/config.yaml"
key_yaml_file = METAGPT_ROOT / "config/key.yaml"
default_yaml_file = METAGPT_ROOT / "config/config.yaml"
def __init__(self, yaml_file=default_yaml_file):
self._configs = {}
self._init_with_config_files_and_env(self._configs, yaml_file)
# logger.info("Config loading done.")
self.global_proxy = self._get("GLOBAL_PROXY")
self.openai_api_key = self._get("OPENAI_API_KEY")
self.anthropic_api_key = self._get("Anthropic_API_KEY")
self.zhipuai_api_key = self._get("ZHIPUAI_API_KEY")
if (not self.openai_api_key or "YOUR_API_KEY" == self.openai_api_key) and \
(not self.anthropic_api_key or "YOUR_API_KEY" == self.anthropic_api_key) and \
(not self.zhipuai_api_key or "YOUR_API_KEY" == self.zhipuai_api_key):
raise NotConfiguredException("Set OPENAI_API_KEY or Anthropic_API_KEY or ZHIPUAI_API_KEY first")
self.openai_api_base = self._get("OPENAI_API_BASE")
openai_proxy = self._get("OPENAI_PROXY") or self.global_proxy
if openai_proxy:
openai.proxy = openai_proxy
openai.api_base = self.openai_api_base
self.openai_api_type = self._get("OPENAI_API_TYPE")
self.openai_api_version = self._get("OPENAI_API_VERSION")
self.openai_api_rpm = self._get("RPM", 3)
self.openai_api_model = self._get("OPENAI_API_MODEL", "gpt-4")
self.max_tokens_rsp = self._get("MAX_TOKENS", 2048)
self.deployment_name = self._get("DEPLOYMENT_NAME")
self.deployment_id = self._get("DEPLOYMENT_ID")
self.spark_appid = self._get("SPARK_APPID")
self.spark_api_secret = self._get("SPARK_API_SECRET")
self.spark_api_key = self._get("SPARK_API_KEY")
self.domain = self._get("DOMAIN")
self.spark_url = self._get("SPARK_URL")
self.claude_api_key = self._get("Anthropic_API_KEY")
self.serpapi_api_key = self._get("SERPAPI_API_KEY")
self.serper_api_key = self._get("SERPER_API_KEY")
self.google_api_key = self._get("GOOGLE_API_KEY")
self.google_cse_id = self._get("GOOGLE_CSE_ID")
self.search_engine = SearchEngineType(self._get("SEARCH_ENGINE", SearchEngineType.SERPAPI_GOOGLE))
self.web_browser_engine = WebBrowserEngineType(self._get("WEB_BROWSER_ENGINE", WebBrowserEngineType.PLAYWRIGHT))
self.playwright_browser_type = self._get("PLAYWRIGHT_BROWSER_TYPE", "chromium")
self.selenium_browser_type = self._get("SELENIUM_BROWSER_TYPE", "chrome")
self.long_term_memory = self._get("LONG_TERM_MEMORY", False)
if self.long_term_memory:
logger.warning("LONG_TERM_MEMORY is True")
self.max_budget = self._get("MAX_BUDGET", 10.0)
self.total_cost = 0.0
self.puppeteer_config = self._get("PUPPETEER_CONFIG", "")
self.mmdc = self._get("MMDC", "mmdc")
self.calc_usage = self._get("CALC_USAGE", True)
self.model_for_researcher_summary = self._get("MODEL_FOR_RESEARCHER_SUMMARY")
self.model_for_researcher_report = self._get("MODEL_FOR_RESEARCHER_REPORT")
self.mermaid_engine = self._get("MERMAID_ENGINE", "nodejs")
self.pyppeteer_executable_path = self._get("PYPPETEER_EXECUTABLE_PATH", "")
self.prompt_format = self._get("PROMPT_FORMAT", "markdown")
self.workspace_path = Path(self._get("WORKSPACE_PATH", DEFAULT_WORKSPACE_ROOT))
self._ensure_workspace_exists()
def _ensure_workspace_exists(self):
self.workspace_path.mkdir(parents=True, exist_ok=True)
logger.info(f"WORKSPACE_PATH set to {self.workspace_path}")
def _init_with_config_files_and_env(self, configs: dict, yaml_file):
"""Load from config/key.yaml, config/config.yaml, and env in decreasing order of priority"""
configs.update(os.environ)
for _yaml_file in [yaml_file, self.key_yaml_file, self.home_yaml_file]:
if not _yaml_file.exists():
continue
# Load local YAML file
with open(_yaml_file, "r", encoding="utf-8") as file:
yaml_data = yaml.safe_load(file)
if not yaml_data:
continue
os.environ.update({k: v for k, v in yaml_data.items() if isinstance(v, str)})
configs.update(yaml_data)
def _get(self, *args, **kwargs):
return self._configs.get(*args, **kwargs)
def get(self, key, *args, **kwargs):
"""Search for a value in config/key.yaml, config/config.yaml, and env; raise an error if not found"""
value = self._get(key, *args, **kwargs)
if value is None:
raise ValueError(f"Key '{key}' not found in environment variables or in the YAML file")
return value
CONFIG = Config()