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remove Dict, use direct LLMConfig / Browser. / Search. / Mermaid. instead

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geekan 2024-01-11 15:10:07 +08:00
parent 4de8fa3682
commit c275f28a37
16 changed files with 60 additions and 82 deletions
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51
metagpt/config2.py
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@ -9,7 +9,7 @@ import os
from pathlib import Path
from typing import Dict, Iterable, List, Literal, Optional
from pydantic import BaseModel, Field, model_validator
from pydantic import BaseModel, model_validator
from metagpt.configs.browser_config import BrowserConfig
from metagpt.configs.llm_config import LLMConfig, LLMType
@ -44,15 +44,15 @@ class Config(CLIParams, YamlModel):
"""Configurations for MetaGPT"""
# Key Parameters
llm: Dict[str, LLMConfig] = Field(default_factory=Dict)
llm: LLMConfig
# Global Proxy. Will be used if llm.proxy is not set
proxy: str = ""
# Tool Parameters
search: Dict[str, SearchConfig] = {}
browser: Dict[str, BrowserConfig] = {"default": BrowserConfig()}
mermaid: Dict[str, MermaidConfig] = {"default": MermaidConfig()}
search: Optional[SearchConfig] = None
browser: BrowserConfig = BrowserConfig()
mermaid: MermaidConfig = MermaidConfig()
# Storage Parameters
s3: Optional[S3Config] = None
@ -110,46 +110,17 @@ class Config(CLIParams, YamlModel):
self.reqa_file = reqa_file
self.max_auto_summarize_code = max_auto_summarize_code
def _get_llm_config(self, name: Optional[str] = None) -> LLMConfig:
"""Get LLM instance by name"""
if name is None:
# Use the first LLM as default
name = list(self.llm.keys())[0]
if name not in self.llm:
raise ValueError(f"LLM {name} not found in config")
return self.llm[name]
def get_llm_configs_by_type(self, llm_type: LLMType) -> List[LLMConfig]:
"""Get LLM instance by type"""
return [v for k, v in self.llm.items() if v.api_type == llm_type]
def get_llm_config_by_type(self, llm_type: LLMType) -> Optional[LLMConfig]:
"""Get LLM instance by type"""
llm = self.get_llm_configs_by_type(llm_type)
if llm:
return llm[0]
return None
def get_llm_config(self, name: Optional[str] = None, provider: LLMType = None) -> LLMConfig:
"""Return a LLMConfig instance"""
if provider:
llm_configs = self.get_llm_configs_by_type(provider)
if len(llm_configs) == 0:
raise ValueError(f"Cannot find llm config with name {name} and provider {provider}")
# return the first one if name is None, or return the only one
llm_config = llm_configs[0]
else:
llm_config = self._get_llm_config(name)
return llm_config
def get_openai_llm(self) -> Optional[LLMConfig]:
"""Get OpenAI LLMConfig by name. If no OpenAI, raise Exception"""
return self.get_llm_config_by_type(LLMType.OPENAI)
if self.llm.api_type == LLMType.OPENAI:
return self.llm
return None
def get_azure_llm(self) -> Optional[LLMConfig]:
"""Get Azure LLMConfig by name. If no Azure, raise Exception"""
return self.get_llm_config_by_type(LLMType.AZURE)
if self.llm.api_type == LLMType.AZURE:
return self.llm
return None
def merge_dict(dicts: Iterable[Dict]) -> Dict: