feat: +runtime multi-llm support

metagpt/configs/llm_config.py

@@ -10,9 +10,9 @@ from typing import Optional
 
 from pydantic import field_validator
 
-from metagpt.const import LLM_API_TIMEOUT
+from metagpt.const import CONFIG_ROOT, LLM_API_TIMEOUT, METAGPT_ROOT
 from metagpt.utils.yaml_model import YamlModel
-from metagpt.const import METAGPT_ROOT, CONFIG_ROOT
+
 
 class LLMType(Enum):
     OPENAI = "openai"
@@ -97,12 +97,13 @@ class LLMConfig(YamlModel):
             repo_config_path = METAGPT_ROOT / "config/config2.yaml"
             root_config_path = CONFIG_ROOT / "config2.yaml"
             if root_config_path.exists():
-                 raise ValueError(
-                    f"Please set your API key in {root_config_path}. If you also set your config in {repo_config_path}, \nthe former will overwrite the latter. This may cause unexpected result.\n")
+                raise ValueError(
+                    f"Please set your API key in {root_config_path}. If you also set your config in {repo_config_path}, \nthe former will overwrite the latter. This may cause unexpected result.\n"
+                )
             elif repo_config_path.exists():
                 raise ValueError(f"Please set your API key in {repo_config_path}")
             else:
-                raise ValueError(f"Please set your API key in config2.yaml")
+                raise ValueError("Please set your API key in config2.yaml")
         return v
 
     @field_validator("timeout")

metagpt/configs/models_config.py

@@ -0,0 +1,112 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+models_config.py
+
+This module defines the ModelsConfig class for handling configuration of LLM models.
+
+Attributes:
+    CONFIG_ROOT (Path): Root path for configuration files.
+    METAGPT_ROOT (Path): Root path for MetaGPT files.
+
+Classes:
+    ModelsConfig (YamlModel): Configuration class for LLM models.
+"""
+from pathlib import Path
+from typing import Dict, List, Optional
+
+from pydantic import Field, field_validator
+
+from metagpt.config2 import merge_dict
+from metagpt.configs.llm_config import LLMConfig
+from metagpt.const import CONFIG_ROOT, METAGPT_ROOT
+from metagpt.utils.yaml_model import YamlModel
+
+
+class ModelsConfig(YamlModel):
+    """
+    Configuration class for LLM models.
+
+    Attributes:
+        models (Dict[str, LLMConfig]): Dictionary mapping model names to LLMConfig objects.
+
+    Methods:
+        update_llm_model(cls, value): Validates and updates LLM model configurations.
+        from_home(cls, path): Loads configuration from ~/.metagpt/config2.yaml.
+        default(cls): Loads default configuration from predefined paths.
+        get(self, name_or_type: str) -> Optional[LLMConfig]: Retrieves LLMConfig by name or API type.
+    """
+
+    models: Dict[str, LLMConfig] = Field(default_factory=dict)
+
+    @field_validator("models", mode="before")
+    @classmethod
+    def update_llm_model(cls, value):
+        """
+        Validates and updates LLM model configurations.
+
+        Args:
+            value (Dict[str, Union[LLMConfig, dict]]): Dictionary of LLM configurations.
+
+        Returns:
+            Dict[str, Union[LLMConfig, dict]]: Updated dictionary of LLM configurations.
+        """
+        for key, config in value.items():
+            if isinstance(config, LLMConfig):
+                config.model = config.model or key
+            elif isinstance(config, dict):
+                config["model"] = config.get("model") or key
+        return value
+
+    @classmethod
+    def from_home(cls, path):
+        """
+        Loads configuration from ~/.metagpt/config2.yaml.
+
+        Args:
+            path (str): Relative path to configuration file.
+
+        Returns:
+            Optional[ModelsConfig]: Loaded ModelsConfig object or None if file doesn't exist.
+        """
+        pathname = CONFIG_ROOT / path
+        if not pathname.exists():
+            return None
+        return ModelsConfig.from_yaml_file(pathname)
+
+    @classmethod
+    def default(cls):
+        """
+        Loads default configuration from predefined paths.
+
+        Returns:
+            ModelsConfig: Default ModelsConfig object.
+        """
+        default_config_paths: List[Path] = [
+            METAGPT_ROOT / "config/config2.yaml",
+            CONFIG_ROOT / "config2.yaml",
+        ]
+
+        dicts = [ModelsConfig.read_yaml(path) for path in default_config_paths]
+        final = merge_dict(dicts)
+        return ModelsConfig(**final)
+
+    def get(self, name_or_type: str) -> Optional[LLMConfig]:
+        """
+        Retrieves LLMConfig object by name or API type.
+
+        Args:
+            name_or_type (str): Name or API type of the LLM model.
+
+        Returns:
+            Optional[LLMConfig]: LLMConfig object if found, otherwise None.
+        """
+        if not name_or_type:
+            return None
+        model = self.models.get(name_or_type)
+        if model:
+            return model
+        for m in self.models.values():
+            if m.api_type == name_or_type:
+                return m
+        return None