Example
from langchain import OpenSearchVectorSearch
opensearch_vector_search = OpenSearchVectorSearch(
    "http://localhost:9200",
    "embeddings",
    embedding_function
)
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, bulk_size: int = 500, **kwargs: Any) → List[str][source]#
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
bulk_size – Bulk API request count; Default: 500
Returns
List of ids from adding the texts into the vectorstore.
Optional Args:vector_field: Document field embeddings are stored in. Defaults to
“vector_field”.
text_field: Document field the text of the document is stored in. Defaults
to “text”.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, bulk_size: int = 500, **kwargs: Any) → langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch[source]#
Construct OpenSearchVectorSearch wrapper from raw documents.
Example
from langchain import OpenSearchVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
opensearch_vector_search = OpenSearchVectorSearch.from_texts(
    texts,
    embeddings,
    opensearch_url="http://localhost:9200"
)
OpenSearch by default supports Approximate Search powered by nmslib, faiss
and lucene engines recommended for large datasets. Also supports brute force
search through Script Scoring and Painless Scripting.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html