Milvus
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.
Setup
You'll need to install langchain-milvus with pip install -qU langchain-milvus to use this integration.
%pip install -qU langchain_milvus
The latest version of pymilvus comes with a local vector database called Milvus Lite, which is good for prototyping. If you have a large amount of data (e.g., more than a million vectors), we recommend setting up a more performant Milvus server on Docker or Kubernetes.
Credentials
No credentials are needed to use the Milvus vector store.
Initialization
pip install -qU langchain-openai
import getpass
import os
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
from langchain_milvus import Milvus
# The easiest way is to use Milvus Lite where everything is stored in a local file.
# If you have a Milvus server you can use the server URI such as "http://localhost:19530".
URI = "./milvus_example.db"
vector_store = Milvus(
embedding_function=embeddings,
connection_args={"uri": URI},
# Set index_params if needed
index_params={"index_type": "FLAT", "metric_type": "L2"},
)
Compartmentalize the data with Milvus Collections
You can store unrelated documents in different collections within the same Milvus instance.
Here's how you can create a new collection:
from langchain_core.documents import Document
vector_store_saved = Milvus.from_documents(
[Document(page_content="foo!")],
embeddings,
collection_name="langchain_example",
connection_args={"uri": URI},
)
And here is how you retrieve that stored collection:
vector_store_loaded = Milvus(
embeddings,
connection_args={"uri": URI},
collection_name="langchain_example",
)
Manage vector store
Once you have created your vector store, we can interact with it by adding and deleting different items.
Add items to vector store
We can add items to our vector store by using the add_documents function.
from uuid import uuid4
from langchain_core.documents import Document
document_1 = Document(
page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
metadata={"source": "tweet"},
)
document_2 = Document(
page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
metadata={"source": "news"},
)
document_3 = Document(
page_content="Building an exciting new project with LangChain - come check it out!",
metadata={"source": "tweet"},
)
document_4 = Document(
page_content="Robbers broke into the city bank and stole $1 million in cash.",
metadata={"source": "news"},
)
document_5 = Document(
page_content="Wow! That was an amazing movie. I can't wait to see it again.",
metadata={"source": "tweet"},
)
document_6 = Document(
page_content="Is the new iPhone worth the price? Read this review to find out.",
metadata={"source": "website"},
)
document_7 = Document(
page_content="The top 10 soccer players in the world right now.",
metadata={"source": "website"},
)
document_8 = Document(
page_content="LangGraph is the best framework for building stateful, agentic applications!",
metadata={"source": "tweet"},
)
document_9 = Document(
page_content="The stock market is down 500 points today due to fears of a recession.",
metadata={"source": "news"},
)
document_10 = Document(
page_content="I have a bad feeling I am going to get deleted :(",
metadata={"source": "tweet"},
)
documents = [
document_1,
document_2,
document_3,
document_4,
document_5,
document_6,
document_7,
document_8,
document_9,
document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]
vector_store.add_documents(documents=documents, ids=uuids)
['b0248595-2a41-4f6b-9c25-3a24c1278bb3',
'fa642726-5329-4495-a072-187e948dd71f',
'9905001c-a4a3-455e-ab94-72d0ed11b476',
'eacc7256-d7fa-4036-b1f7-83d7a4bee0c5',
'7508f7ff-c0c9-49ea-8189-634f8a0244d8',
'2e179609-3ff7-4c6a-9e05-08978903fe26',
'fab1f2ac-43e1-45f9-b81b-fc5d334c6508',
'1206d237-ee3a-484f-baf2-b5ac38eeb314',
'd43cbf9a-a772-4c40-993b-9439065fec01',
'25e667bb-6f09-4574-a368-661069301906']
Delete items from vector store
vector_store.delete(ids=[uuids[-1]])
(insert count: 0, delete count: 1, upsert count: 0, timestamp: 0, success count: 0, err count: 0, cost: 0)
Query vector store
Once your vector store has been created and the relevant documents have been added, you will most likely wish to query it during the running of your chain or agent.
Query directly
Similarity search
Performing a simple similarity search with filtering on metadata can be done as follows:
results = vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy",
k=2,
expr='source == "tweet"',
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
* Building an exciting new project with LangChain - come check it out! [{'pk': '9905001c-a4a3-455e-ab94-72d0ed11b476', 'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'pk': '1206d237-ee3a-484f-baf2-b5ac38eeb314', 'source': 'tweet'}]
Similarity search with score
You can also search with score:
results = vector_store.similarity_search_with_score(
"Will it be hot tomorrow?", k=1, expr='source == "news"'
)
for res, score in results:
print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
* [SIM=21192.628906] bar [{'pk': '2', 'source': 'https://example.com'}]
For a full list of all the search options available when using the Milvus vector store, you can visit the API reference.
Query by turning into retriever
You can also transform the vector store into a retriever for easier usage in your chains.
retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1})
retriever.invoke("Stealing from the bank is a crime", filter={"source": "news"})
[Document(metadata={'pk': 'eacc7256-d7fa-4036-b1f7-83d7a4bee0c5', 'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]
Usage for retrieval-augmented generation
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
Per-User Retrieval
When building a retrieval app, you often have to build it with multiple users in mind. This means that you may be storing data not just for one user, but for many different users, and they should not be able to see each other’s data.
Milvus recommends using partition_key to implement multi-tenancy. Here is an example:
The feature of Partition key is now not available in Milvus Lite, if you want to use it, you need to start Milvus server, as mentioned above.
from langchain_core.documents import Document
docs = [
Document(page_content="i worked at kensho", metadata={"namespace": "harrison"}),
Document(page_content="i worked at facebook", metadata={"namespace": "ankush"}),
]
vectorstore = Milvus.from_documents(
docs,
embeddings,
connection_args={"uri": URI},
drop_old=True,
partition_key_field="namespace", # Use the "namespace" field as the partition key
)
To conduct a search using the partition key, you should include either of the following in the boolean expression of the search request:
search_kwargs={"expr": '<partition_key> == "xxxx"'}
search_kwargs={"expr": '<partition_key> == in ["xxx", "xxx"]'}
Do replace <partition_key> with the name of the field that is designated as the partition key.
Milvus changes to a partition based on the specified partition key, filters entities according to the partition key, and searches among the filtered entities.
# This will only get documents for Ankush
vectorstore.as_retriever(search_kwargs={"expr": 'namespace == "ankush"'}).invoke(
"where did i work?"
)
[Document(page_content='i worked at facebook', metadata={'namespace': 'ankush'})]
# This will only get documents for Harrison
vectorstore.as_retriever(search_kwargs={"expr": 'namespace == "harrison"'}).invoke(
"where did i work?"
)
[Document(page_content='i worked at kensho', metadata={'namespace': 'harrison'})]
API reference
For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://python.langchain.com/api_reference/milvus/vectorstores/langchain_milvus.vectorstores.milvus.Milvus.html
Related
- Vector store conceptual guide
- Vector store how-to guides