将 Milvus 用作向量存储库
Milvus是一个数据库，用于存储、索引和管理由深度神经网络和其他机器学习（ML）模型生成的海量嵌入向量。
本笔记本展示了如何使用 Milvus 向量数据库的相关功能。
安装
要使用此集成，您需要安装langchain-milvus ，pip install -qU langchain-milvus 。
%pip install -qU langchain_milvus
最新版本的 pymilvus 自带本地向量数据库 Milvus Lite，适合原型开发。如果你的数据规模较大，比如文档数量超过一百万，我们建议你在docker 或 kubernetes 上安装性能更强的 Milvus 服务器。
证书
使用Milvus 向量存储不需要证书。
初始化
import EmbeddingTabs from "@theme/EmbeddingTabs";
<EmbeddingTabs/>
# | output: false
# | echo: false
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},
)
使用 Milvus 集合分割数据
你可以在同一个 Milvus 实例中将不同的无关文档存储在不同的集合中，以保持上下文的一致性。
以下是创建新集合的方法
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},
)
以下是如何检索存储的集合
vector_store_loaded = Milvus(
embeddings,
connection_args={"uri": URI},
collection_name="langchain_example",
)
管理向量存储
创建向量存储后，我们就可以通过添加和删除不同的项目与之交互。
向向量存储添加项目
我们可以使用add_documents 函数将项目添加到向量存储中。
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']
从向量存储中删除项目
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)
查询向量存储空间
一旦创建了向量存储并添加了相关文件，您很可能希望在运行链或代理时对其进行查询。
直接查询
相似性搜索
执行简单的相似性搜索并对元数据进行过滤的方法如下：
results = vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy",
k=2,
filter={"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'}]
用分数进行相似性搜索
您也可以使用分数进行搜索：
results = vector_store.similarity_search_with_score(
"Will it be hot tomorrow?", k=1, filter={"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'}]
有关使用Milvus 向量存储时可用的所有搜索选项的完整列表，您可以访问API 参考。
通过转化为检索器进行查询
您还可以将向量存储转化为检索器，以便在您的链中更方便地使用。
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.')]
检索增强生成的用法
有关如何将该向量存储用于检索增强生成（RAG）的指南，请参阅以下章节：
按用户检索
在构建检索应用程序时，您通常需要考虑到多个用户。这意味着您可能不仅要为一个用户存储数据，还要为许多不同的用户存储数据，而且这些用户不能查看彼此的数据。
Milvus 建议使用partition_key来实现多租户，下面是一个例子。
Milvus Lite 目前不提供Partition Key功能，如果要使用该功能，需要从docker 或 kubernetes 启动 Milvus 服务器。
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
)
要使用Partition Key进行搜索，应在搜索请求的布尔表达式中包含以下任一内容：
search_kwargs={"expr": '<partition_key> == "xxxx"'}
search_kwargs={"expr": '<partition_key> == in ["xxx", "xxx"]'}
将<partition_key> 替换为指定为Partition Key的字段名称。
Milvus 会根据指定的Partition Key更改分区，根据Partition Key过滤实体，并在过滤后的实体中进行搜索。
# 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 参考
有关所有 __ModuleName__VectorStore 功能和配置的详细文档，请访问 API 参考：https://api.python.langchain.com/en/latest/vectorstores/langchain_milvus.vectorstores.milvus.Milvus.html。
