add context and config2

metagpt/config2.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2024/1/4 01:25
+@Author  : alexanderwu
+@File    : llm_factory.py
+"""
+import os
+from pathlib import Path
+from typing import Dict, Iterable, List, Literal, Optional
+
+from pydantic import BaseModel, Field, model_validator
+
+from metagpt.configs.browser_config import BrowserConfig
+from metagpt.configs.llm_config import LLMConfig, LLMType
+from metagpt.configs.mermaid_config import MermaidConfig
+from metagpt.configs.redis_config import RedisConfig
+from metagpt.configs.s3_config import S3Config
+from metagpt.configs.search_config import SearchConfig
+from metagpt.configs.workspace_config import WorkspaceConfig
+from metagpt.const import METAGPT_ROOT
+from metagpt.utils.yaml_model import YamlModel
+
+
+class CLIParams(BaseModel):
+    project_path: str = ""
+    project_name: str = ""
+    inc: bool = False
+    reqa_file: str = ""
+    max_auto_summarize_code: int = 0
+    git_reinit: bool = False
+
+    @model_validator(mode="after")
+    def check_project_path(self):
+        if self.project_path:
+            self.inc = True
+            self.project_name = self.project_name or Path(self.project_path).name
+
+
+class Config(CLIParams, YamlModel):
+    # Key Parameters
+    llm: Dict[str, LLMConfig] = Field(default_factory=Dict)
+
+    # 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()}
+
+    # Storage Parameters
+    s3: Optional[S3Config] = None
+    redis: Optional[RedisConfig] = None
+
+    # Misc Parameters
+    repair_llm_output: bool = False
+    prompt_schema: Literal["json", "markdown", "raw"] = "json"
+    workspace: WorkspaceConfig = WorkspaceConfig()
+    enable_longterm_memory: bool = False
+    code_review_k_times: int = 2
+
+    # Will be removed in the future
+    llm_for_researcher_summary: str = "gpt3"
+    llm_for_researcher_report: str = "gpt3"
+    METAGPT_TEXT_TO_IMAGE_MODEL_URL: str = ""
+
+    @classmethod
+    def default(cls):
+        """Load default config
+        - Priority: env < default_config_paths
+        - Inside default_config_paths, the latter one overwrites the former one
+        """
+        default_config_paths: List[Path] = [
+            METAGPT_ROOT / "config/config2.yaml",
+            Path.home() / ".metagpt/config2.yaml",
+        ]
+
+        dicts = [dict(os.environ)]
+        dicts += [Config.read_yaml(path) for path in default_config_paths]
+        final = merge_dict(dicts)
+        return Config(**final)
+
+    def update_via_cli(self, project_path, project_name, inc, reqa_file, max_auto_summarize_code):
+        """update config via cli"""
+
+        # Use in the PrepareDocuments action according to Section 2.2.3.5.1 of RFC 135.
+        if project_path:
+            inc = True
+            project_name = project_name or Path(project_path).name
+        self.project_path = project_path
+        self.project_name = project_name
+        self.inc = inc
+        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_openai_llm(self, name: Optional[str] = None) -> LLMConfig:
+        """Get OpenAI LLMConfig by name. If no OpenAI, raise Exception"""
+        if name is None:
+            # Use the first OpenAI LLM as default
+            name = [k for k, v in self.llm.items() if v.api_type == LLMType.OPENAI][0]
+        if name not in self.llm:
+            raise ValueError(f"OpenAI LLM {name} not found in config")
+        return self.llm[name]
+
+
+def merge_dict(dicts: Iterable[Dict]) -> Dict:
+    """Merge multiple dicts into one, with the latter dict overwriting the former"""
+    result = {}
+    for dictionary in dicts:
+        result.update(dictionary)
+    return result
+
+
+config = Config.default()