from_texts

from_texts(
  cls,
  texts: List[str],
  embedding: Embeddings,
  metadatas: Optional[List[dict]] = None,
  bulk_size: Optional[int] = None,
  ids: Optional[List[str]] = None,
  **kwargs: Any = {}
) -> OpenSearchVectorSearch

Example:

.. code-block:: python

from langchain_community.vectorstores import OpenSearchVectorSearch from langchain_community.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.

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".

Optional Keyword Args for Approximate Search:

engine: "nmslib", "faiss", "lucene"; default: "nmslib"

space_type: "l2", "l1", "cosinesimil", "linf", "innerproduct"; default: "l2"

ef_search: Size of the dynamic list used during k-NN searches. Higher values lead to more accurate but slower searches; default: 512

ef_construction: Size of the dynamic list used during k-NN graph creation. Higher values lead to more accurate graph but slower indexing speed; default: 512

m: Number of bidirectional links created for each new element. Large impact on memory consumption. Between 2 and 100; default: 16

Keyword Args for Script Scoring or Painless Scripting:

is_appx_search: False