Construct Meilisearch wrapper from raw documents.
from_texts(
cls: Type[Meilisearch],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
client: Optional[Client] = None,
url: Optional[str] = None,
api_key: Optional[str] = None,
index_name: str = 'langchain-demo',
ids: Optional[List[str]] = None,
text_key: Optional[str] = 'text',
metadata_key: Optional[str] = 'metadata',
embedders: Dict[str, Any] = {},
embedder_name: Optional[str] = 'default',
**kwargs: Any = {}
) -> MeilisearchThis is a user-friendly interface that:
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_community.vectorstores import Meilisearch from langchain_community.embeddings import OpenAIEmbeddings import meilisearch
client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embedding = OpenAIEmbeddings() embedders: Embedders index setting. embedder_name: Name of the embedder. Defaults to "default". docsearch = Meilisearch.from_texts( client=client, embedding=embedding, )