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将 Milvus 用作 LangChain 向量存储库

本笔记本介绍如何将Milvus作为LangChain 向量存储使用。

安装

您需要安装langchain-milvus 和其他必要的依赖项。

$ pip install -qU langchain-milvus milvus-lite langchain-openai

最新版本的 pymilvus 自带本地向量数据库 Milvus Lite，适合原型开发。如果你有大规模数据，比如超过一百万个文档，我们建议你在docker 或 kubernetes 上设置性能更强的 Milvus 服务器。

初始化

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 Collections 对数据进行分隔

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 Collections 中，以保持上下文的一致性。

下面是如何创建一个新的向量文档存储 Collections：

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 Collections

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)

['31915e2d-55fd-4bfb-ae08-d441252b8e08',
 'dbf6560a-1487-4a6e-8797-245d57874f5b',
 'e991a253-5f37-46ae-850a-82a660e33013',
 '2818c051-5a1a-44cb-9deb-aaaac709f616',
 '91c7ef07-26d1-4319-b48c-9261df9ce8d7',
 'fb258085-6400-4cd7-aa92-fc5e32ca243e',
 'ffea9a9f-460d-4d8d-ba07-c45e9cfa1e33',
 'eb149e29-239a-4e2c-9f99-751cb7207abf',
 '119d4a42-fd6b-433d-842b-1e0be5df81e5',
 '5b099eb0-98fe-40a3-b13a-300c10250960']

从向量存储中删除项目

vector_store.delete(ids=[uuids[-1]])

True

查询向量存储空间

创建向量存储并添加相关文件后，您很可能希望在运行链或 Agents 时对其进行查询。

直接查询

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,
    expr='source == "tweet"',
    # param=...  # Search params for the index type
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1761298048.354308 7886403 fork_posix.cc:71] Other threads are currently calling into gRPC, skipping fork() handlers


* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet', 'pk': 'e991a253-5f37-46ae-850a-82a660e33013'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet', 'pk': 'eb149e29-239a-4e2c-9f99-751cb7207abf'}]

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, expr='source == "news"'
)
for res, score in results:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")

* [SIM=0.893776] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news', 'pk': 'dbf6560a-1487-4a6e-8797-245d57874f5b'}]

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", expr='source == "news"')

I0000 00:00:1761298049.275354 7886403 fork_posix.cc:71] Other threads are currently calling into gRPC, skipping fork() handlers





[Document(metadata={'source': 'news', 'pk': '2818c051-5a1a-44cb-9deb-aaaac709f616'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]

检索增强生成的用法

有关如何将此向量存储用于检索增强生成（RAG）的指南，请参阅此RAG 指南。

按用户检索

在构建检索应用程序时，您往往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。

现在，Milvus Lite 不提供分区密钥功能，如果要使用，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,
    collection_name="partitioned_collection",  # Use a different collection name
    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"]'}

请将<partition_key> 替换为指定为分区密钥的字段名称。

Milvus 会根据指定的分区键更改为一个分区，根据分区键过滤实体，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(metadata={'namespace': 'ankush', 'pk': 460829372217788296}, page_content='i worked at facebook')]

# This will only get documents for Harrison
vectorstore.as_retriever(search_kwargs={"expr": 'namespace == "harrison"'}).invoke(
    "where did i work?"
)

[Document(metadata={'namespace': 'harrison', 'pk': 460829372217788295}, page_content='i worked at kensho')]

应用程序接口参考

有关详细文档，请访问应用程序接口参考： https://reference.langchain.com/python/integrations/langchain_milvus/

目录

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