langchain.vectorstores.vectara.VectaraRetriever¶
- class langchain.vectorstores.vectara.VectaraRetriever(*, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, vectorstore: Vectara, search_type: str = 'similarity', search_kwargs: dict = None)[source]¶
Bases:
VectorStoreRetrieverRetriever class for Vectara.
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 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]¶
Search params. k: Number of Documents to return. Defaults to 5. lambda_val: lexical match parameter for hybrid search. filter: Dictionary of argument(s) to filter on metadata. For example a
filter can be “doc.rating > 3.0 and part.lang = ‘deu’”} see https://docs.vectara.com/docs/search-apis/sql/filter-overview for more details.
n_sentence_context: number of sentences before/after the matching segment to add
- param search_type: str = 'similarity'¶
Type of search to perform. Defaults to “similarity”.
- 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.
- async aadd_documents(documents: List[Document], **kwargs: Any) List[str]¶
Add documents to vectorstore.
- add_texts(texts: List[str], metadatas: Optional[List[dict]] = None, doc_metadata: Optional[dict] = {}) None[source]¶
Add text to the Vectara vectorstore.
- Parameters
texts (List[str]) – The text
metadatas (List[dict]) – Metadata dicts, must line up with existing store
- 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]¶
- 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_search_type » all fields¶
Validate search type.
- allowed_search_types: ClassVar[Collection[str]] = ('similarity', 'similarity_score_threshold', 'mmr')¶
- 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.