langchain.vectorstores.milvus.Milvus¶
class langchain.vectorstores.milvus.Milvus(embedding_function: 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, *, primary_field: str = 'pk', text_field: str = 'text', vector_field: str = 'vector')[source]¶
Milvus vector store.
You need to install pymilvus and run Milvus.
See the following documentation for how to run a Milvus instance:
https://milvus.io/docs/install_standalone-docker.md
If looking for a hosted Milvus, take a look at this documentation:
https://zilliz.com/cloud and make use of the Zilliz vectorstore found in
this project.
IF USING L2/IP metric, IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
Parameters
embedding_function (Embeddings) – Function used to embed the text.
collection_name (str) – Which Milvus collection to use. Defaults to
“LangChainCollection”.
connection_args (Optional[dict[str, any]]) – The connection args used for
this class comes in the form of a dict.
consistency_level (str) – The consistency level to use for a collection.
Defaults to “Session”.
index_params (Optional[dict]) – Which index params to use. Defaults to
HNSW/AUTOINDEX depending on service.
search_params (Optional[dict]) – Which search params to use. Defaults to
default of index.
drop_old (Optional[bool]) – Whether to drop the current collection. Defaults
to False.