server_name (str): If use tls, need to write the common name.
Example
from langchain.vectorstores import Milvus
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
# Connect to a milvus instance on localhost
milvus_store = Milvus(
embedding_function = Embeddings,
collection_name = “LangChainCollection”,
drop_old = True,
)
Raises
ValueError – If the pymilvus python package is not installed.
Initialize the Milvus vector store.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding_function[, ...])
Initialize the Milvus vector store.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, timeout, ...])
Insert text data into Milvus.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.