langchain/docs/modules/indexes/vectorstores/examples/milvus.ipynb

Milvus

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

To run, you should have a Milvus instance up and running: https://milvus.io/docs/install_standalone-docker.md

In [1]:

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Milvus
from langchain.document_loaders import TextLoader

In [2]:

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()

In [ ]:

vector_db = Milvus.from_documents(
    docs,
    embeddings,
    connection_args={"host": "127.0.0.1", "port": "19530"},
)

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docs = vector_db.similarity_search(query)

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docs[0]

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