langchain.retrievers.self_query.base.SelfQueryRetriever

class langchain.retrievers.self_query.base.SelfQueryRetriever(*, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, vectorstore: VectorStore, llm_chain: LLMChain, search_type: str = 'similarity', search_kwargs: dict = None, structured_query_translator: Visitor, verbose: bool = False, use_original_query: bool = False)[source]

Bases: BaseRetriever, BaseModel

Retriever that uses a vector store and an LLM to generate the vector store queries.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param llm_chain: langchain.chains.llm.LLMChain [Required]

The LLMChain for generating the vector store queries.

param metadata: Optional[Dict[str, Any]] = None

Optional metadata associated with the retriever. Defaults to None This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case.

param search_kwargs: dict [Optional]

Keyword arguments to pass in to the vector store search.

param search_type: str = 'similarity'

The search type to perform on the vector store.

param structured_query_translator: langchain.chains.query_constructor.ir.Visitor [Required]

Translator for turning internal query language into vectorstore search params.

param tags: Optional[List[str]] = None

Optional list of tags associated with the retriever. Defaults to None These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case.

param use_original_query: bool = False
param vectorstore: langchain.vectorstores.base.VectorStore [Required]

The underlying vector store from which documents will be retrieved.

param verbose: bool = False

Use original query instead of the revised new query from LLM

async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document]

Asynchronously get documents relevant to a query. :param query: string to find relevant documents for :param callbacks: Callback manager or list of callbacks :param tags: Optional list of tags associated with the retriever. Defaults to None

These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks.

Parameters

metadata – Optional metadata associated with the retriever. Defaults to None This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks.

Returns

List of relevant documents

async ainvoke(input: str, config: Optional[RunnableConfig] = None) → List[Document]
classmethod from_llm(llm: BaseLanguageModel, vectorstore: VectorStore, document_contents: str, metadata_field_info: List[AttributeInfo], structured_query_translator: Optional[Visitor] = None, chain_kwargs: Optional[Dict] = None, enable_limit: bool = False, use_original_query: bool = False, **kwargs: Any) → SelfQueryRetriever[source]
get_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document]

Retrieve documents relevant to a query. :param query: string to find relevant documents for :param callbacks: Callback manager or list of callbacks :param tags: Optional list of tags associated with the retriever. Defaults to None

These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks.

Parameters

metadata – Optional metadata associated with the retriever. Defaults to None This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks.

Returns

List of relevant documents

invoke(input: str, config: Optional[RunnableConfig] = None) → List[Document]
to_json() → Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented
validator validate_translator  »  all fields[source]

Validate translator.

property lc_attributes: Dict

Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor.

property lc_namespace: List[str]

Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”]

property lc_secrets: Dict[str, str]

Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”}

property lc_serializable: bool

Return whether or not the class is serializable.

model Config[source]

Bases: object

Configuration for this pydantic object.

arbitrary_types_allowed = True

Examples using SelfQueryRetriever