feat: merge geekan:main

metagpt/configs/embedding_config.py

@@ -0,0 +1,54 @@
+from enum import Enum
+from typing import Optional
+
+from pydantic import field_validator
+
+from metagpt.utils.yaml_model import YamlModel
+
+
+class EmbeddingType(Enum):
+    OPENAI = "openai"
+    AZURE = "azure"
+    GEMINI = "gemini"
+    OLLAMA = "ollama"
+
+
+class EmbeddingConfig(YamlModel):
+    """Config for Embedding.
+
+    Examples:
+    ---------
+    api_type: "openai"
+    api_key: "YOU_API_KEY"
+    dimensions: "YOUR_MODEL_DIMENSIONS"
+
+    api_type: "azure"
+    api_key: "YOU_API_KEY"
+    base_url: "YOU_BASE_URL"
+    api_version: "YOU_API_VERSION"
+    dimensions: "YOUR_MODEL_DIMENSIONS"
+
+    api_type: "gemini"
+    api_key: "YOU_API_KEY"
+
+    api_type: "ollama"
+    base_url: "YOU_BASE_URL"
+    model: "YOU_MODEL"
+    dimensions: "YOUR_MODEL_DIMENSIONS"
+    """
+
+    api_type: Optional[EmbeddingType] = None
+    api_key: Optional[str] = None
+    base_url: Optional[str] = None
+    api_version: Optional[str] = None
+
+    model: Optional[str] = None
+    embed_batch_size: Optional[int] = None
+    dimensions: Optional[int] = None  # output dimension of embedding model
+
+    @field_validator("api_type", mode="before")
+    @classmethod
+    def check_api_type(cls, v):
+        if v == "":
+            return None
+        return v

metagpt/configs/file_parser_config.py

@@ -0,0 +1,6 @@
+from metagpt.utils.yaml_model import YamlModel
+
+
+class OmniParseConfig(YamlModel):
+    api_key: str = ""
+    base_url: str = ""

metagpt/configs/llm_config.py

@@ -10,7 +10,7 @@ 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
 
 
@@ -31,6 +31,8 @@ class LLMType(Enum):
     MOONSHOT = "moonshot"
     MISTRAL = "mistral"
     YI = "yi"  # lingyiwanwu
+    OPENROUTER = "openrouter"
+    BEDROCK = "bedrock"
     ARK = "ark"  # https://www.volcengine.com/docs/82379/1263482#python-sdk
 
     def __missing__(self, key):
@@ -73,11 +75,15 @@ class LLMConfig(YamlModel):
     frequency_penalty: float = 0.0
     best_of: Optional[int] = None
     n: Optional[int] = None
-    stream: bool = False
-    logprobs: Optional[bool] = None  # https://cookbook.openai.com/examples/using_logprobs
+    stream: bool = True
+    # https://cookbook.openai.com/examples/using_logprobs
+    logprobs: Optional[bool] = None
     top_logprobs: Optional[int] = None
     timeout: int = 600
 
+    # For Amazon Bedrock
+    region_name: str = None
+
     # For Network
     proxy: Optional[str] = None
 
@@ -88,7 +94,16 @@ class LLMConfig(YamlModel):
     @classmethod
     def check_llm_key(cls, v):
         if v in ["", None, "YOUR_API_KEY"]:
-            raise ValueError("Please set your API key in config2.yaml")
+            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"
+                )
+            elif repo_config_path.exists():
+                raise ValueError(f"Please set your API key in {repo_config_path}")
+            else:
+                raise ValueError("Please set your API key in config2.yaml")
         return v
 
     @field_validator("timeout")

metagpt/configs/mermaid_config.py

@@ -13,7 +13,7 @@ from metagpt.utils.yaml_model import YamlModel
 class MermaidConfig(YamlModel):
     """Config for Mermaid"""
 
-    engine: Literal["nodejs", "ink", "playwright", "pyppeteer"] = "nodejs"
+    engine: Literal["nodejs", "ink", "playwright", "pyppeteer", "none"] = "nodejs"
     path: str = "mmdc"  # mmdc
     puppeteer_config: str = ""
     pyppeteer_path: str = "/usr/bin/google-chrome-stable"

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 `models` in `config2.yaml`.
+
+    Attributes:
+        models (Dict[str, LLMConfig]): Dictionary mapping model names or types 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