Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.

This notebook shows how to use functionality related to the Milvus vector database.

To run, you should have a Milvus instance up and running.

!pip install pymilvus

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import os
import getpass

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
OpenAI API Key:路路路路路路路路
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Milvus
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vector_db = Milvus.from_documents(
    connection_args={"host": "", "port": "19530"},
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_db.similarity_search(query)
'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you鈥檙e at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I鈥檇 like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer鈥攁n 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鈥檚 top legal minds, who will continue Justice Breyer鈥檚 legacy of excellence.'