Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most similar to query.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_client_params(embedding: Embeddings, *, host: str = 'localhost', port: Union[str, int] = '8108', protocol: str = 'http', typesense_api_key: Optional[str] = None, connection_timeout_seconds: int = 2, **kwargs: Any) → Typesense[source]¶
Initialize Typesense directly from client parameters.
Example
from langchain.embedding.openai import OpenAIEmbeddings
from langchain.vectorstores import Typesense
# Pass in typesense_api_key as kwarg or set env var "TYPESENSE_API_KEY".
vectorstore = Typesense(
    OpenAIEmbeddings(),
    host="localhost",
    port="8108",
    protocol="http",
    typesense_collection_name="langchain-memory",
)
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.