region – The default location making the API calls. It must have
regional. (the same location as the GCS bucket and must be) – 
gcs_bucket_name – The location where the vectors will be stored in
created. (order for the index to be) – 
index_id – The id of the created index.
endpoint_id – The id of the created endpoint.
credentials_path – (Optional) The path of the Google credentials on
system. (the local file) – 
embedding – The Embeddings that will be used for
texts. (embedding the) – 
Returns
A configured MatchingEngine with the texts added to the index.
classmethod from_texts(texts: List[str], embedding: langchain.embeddings.base.Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → langchain.vectorstores.matching_engine.MatchingEngine[source]#
Use from components instead.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
Parameters
query – The string that will be used to search for similar documents.
k – The amount of neighbors that will be retrieved.
Returns
A list of k matching documents.
class langchain.vectorstores.Milvus(embedding_function: langchain.embeddings.base.Embeddings, collection_name: str = 'LangChainCollection', connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_old: Optional[bool] = False)[source]#
Wrapper around the Milvus vector database.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html