Gemini DeepSearch MCP

import os from pydantic import BaseModel, Field from typing import Any, Optional from langchain_core.runnables import RunnableConfig class Configuration(BaseModel): """The configuration for the agent.""" query_generator_model: str = Field( default="gemini-2.5-flash", metadata={ "description": "The name of the language model to use for the agent's query generation." }, ) web_search_model: str = Field( default="gemini-2.5-flash-lite-preview-06-17", metadata={ "description": "The name of the language model to use for the agent's web search." }, ) reflection_model: str = Field( default="gemini-2.5-flash", metadata={ "description": "The name of the language model to use for the agent's reflection." }, ) answer_model: str = Field( default="gemini-2.5-pro", metadata={ "description": "The name of the language model to use for the agent's answer." }, ) number_of_initial_queries: int = Field( default=3, metadata={"description": "The number of initial search queries to generate."}, ) max_research_loops: int = Field( default=2, metadata={"description": "The maximum number of research loops to perform."}, ) @classmethod def from_runnable_config( cls, config: Optional[RunnableConfig] = None ) -> "Configuration": """Create a Configuration instance from a RunnableConfig.""" configurable = ( config["configurable"] if config and "configurable" in config else {} ) # Get raw values from environment or config raw_values: dict[str, Any] = { name: os.environ.get(name.upper(), configurable.get(name)) for name in cls.model_fields.keys() } # Filter out None values values = {k: v for k, v in raw_values.items() if v is not None} return cls(**values)