Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_llm(vectorstore: ~langchain_core.vectorstores.VectorStore, llm: ~langchain_core.language_models.llms.BaseLLM, search: ~langchain_community.utilities.google_search.GoogleSearchAPIWrapper, prompt: ~typing.Optional[~langchain_core.prompts.base.BasePromptTemplate] = None, num_search_results: int = 1, text_splitter: ~langchain.text_splitter.RecursiveCharacterTextSplitter = <langchain.text_splitter.RecursiveCharacterTextSplitter object>) → WebResearchRetriever[source]¶
Initialize from llm using default template.
Parameters
vectorstore – Vector store for storing web pages
llm – llm for search question generation
search – GoogleSearchAPIWrapper
prompt – prompt to generating search questions
num_search_results – Number of pages per Google search
text_splitter – Text splitter for splitting web pages into chunks
Returns
WebResearchRetriever
classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depends on which
configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]