protocol="http",
    typesense_collection_name="langchain-memory",
)
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, typesense_client: Optional[Client] = None, typesense_client_params: Optional[dict] = None, typesense_collection_name: Optional[str] = None, text_key: str = 'text', **kwargs: Any) → Typesense[source]#
Construct Typesense wrapper from raw text.
similarity_search(query: str, k: int = 4, filter: Optional[str] = '', **kwargs: Any) → List[langchain.schema.Document][source]#
Return typesense documents most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – typesense filter_by expression to filter documents on
Returns
List of Documents most similar to the query and score for each
similarity_search_with_score(query: str, k: int = 4, filter: Optional[str] = '') → List[Tuple[langchain.schema.Document, float]][source]#
Return typesense documents most similar to query, along with scores.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – typesense filter_by expression to filter documents on
Returns
List of Documents most similar to the query and score for each
class langchain.vectorstores.Vectara(vectara_customer_id: Optional[str] = None, vectara_corpus_id: Optional[str] = None, vectara_api_key: Optional[str] = None)[source]#
Implementation of Vector Store using Vectara (https://vectara.com).
.. rubric:: Example
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html