= OpenAIEmbeddings()vector_db = Milvus.from_documents(    docs,    embeddings,    connection_args={"host": "127.0.0.1", "port": "19530"},)query = "What did the president say about Ketanji Brown Jackson"docs = vector_db.similarity_search(query)docs[0].page_content    'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.'Compartmentalize the data with Milvus Collections‚ÄãYou can store different unrelated documents in different collections within same Milvus instance to maintain the contextHere's how you can create a new collectionvector_db = Milvus.from_documents(    docs,    embeddings,    collection_name = 'collection_1',    connection_args={"host": "127.0.0.1", "port": "19530"},)And here is how you retrieve that stored collectionvector_db = Milvus(           embeddings,           connection_args={"host": "127.0.0.1", "port": "19530"},           collection_name = 'collection_1'       )After retreival you can go on querying it as usual.PreviousMeilisearchNextMomento Vector Index (MVI)Compartmentalize the data with Milvus CollectionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models. ->: = OpenAIEmbeddings()vector_db = Milvus.from_documents(    docs,    embeddings,    connection_args={"host": "127.0.0.1", "port": "19530"},)query = "What did the president say about Ketanji Brown Jackson"docs = vector_db.similarity_search(query)docs[0].page_content    'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.'Compartmentalize the data with Milvus Collections‚ÄãYou can store different unrelated documents in different collections within same Milvus instance to maintain the contextHere's how you can create a new collectionvector_db = Milvus.from_documents(    docs,    embeddings,    collection_name = 'collection_1',    connection_args={"host": "127.0.0.1", "port": "19530"},)And here is how you retrieve that stored collectionvector_db = Milvus(           embeddings,           connection_args={"host": "127.0.0.1", "port": "19530"},           collection_name = 'collection_1'       )After retreival you can go on querying it as usual.PreviousMeilisearchNextMomento Vector Index (MVI)Compartmentalize the data with Milvus CollectionsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.