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MongoDB Atlas

本笔记本介绍如何在LangChain中使用langchain-mongodb包进行MongoDB Atlas向量搜索。

MongoDB Atlas 是一个完全托管的云数据库,适用于AWS、Azure和GCP。它支持对MongoDB文档数据的原生向量搜索和全文搜索(BM25)。

MongoDB Atlas向量搜索 允许将嵌入存储在MongoDB文档中,创建向量搜索索引,并使用近似最近邻算法(Hierarchical Navigable Small Worlds)执行KNN搜索。它使用$vectorSearch MQL阶段

设置

*一个运行MongoDB版本6.0.11、7.0.2或更高版本(包括RC)的Atlas集群。

要使用MongoDB Atlas,您必须首先部署一个集群。我们提供了一个永久免费层的集群可用。要开始,请访问Atlas:快速入门

您需要安装langchain-mongodbpymongo才能使用此集成。

pip install -qU langchain-mongodb pymongo

凭证

在这个笔记本中,您需要找到您的MongoDB集群URI。

有关查找集群URI的信息,请阅读本指南

import getpass

MONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")

如果您想获得最佳的自动跟踪模型调用,您还可以通过取消注释下面的内容来设置您的LangSmith API密钥:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

初始化

pip install -qU langchain-openai
import getpass

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
<!--IMPORTS:[{"imported": "MongoDBAtlasVectorSearch", "source": "langchain_mongodb.vectorstores", "docs": "https://python.langchain.com/api_reference/mongodb/vectorstores/langchain_mongodb.vectorstores.MongoDBAtlasVectorSearch.html", "title": "MongoDB Atlas"}]-->
from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
from pymongo import MongoClient

# initialize MongoDB python client
client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)

DB_NAME = "langchain_test_db"
COLLECTION_NAME = "langchain_test_vectorstores"
ATLAS_VECTOR_SEARCH_INDEX_NAME = "langchain-test-index-vectorstores"

MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]

vector_store = MongoDBAtlasVectorSearch(
collection=MONGODB_COLLECTION,
embedding=embeddings,
index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
relevance_score_fn="cosine",
)

管理向量存储

一旦您创建了向量存储,我们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

我们可以通过使用add_documents函数向我们的向量存储添加项目。

<!--IMPORTS:[{"imported": "Document", "source": "langchain_core.documents", "docs": "https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html", "title": "MongoDB Atlas"}]-->
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)
['03ad81e8-32a0-46f0-b7d8-f5b977a6b52a',
'8396a68d-f4a3-4176-a581-a1a8c303eea4',
'e7d95150-67f6-499f-b611-84367c50fa60',
'8c31b84e-2636-48b6-8b99-9fccb47f7051',
'aa02e8a2-a811-446a-9785-8cea0faba7a9',
'19bd72ff-9766-4c3b-b1fd-195c732c562b',
'642d6f2f-3e34-4efa-a1ed-c4ba4ef0da8d',
'7614bb54-4eb5-4b3b-990c-00e35cb31f99',
'69e18c67-bf1b-43e5-8a6e-64fb3f240e52',
'30d599a7-4a1a-47a9-bbf8-6ed393e2e33c']

从向量存储删除项目

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

查询向量存储

一旦您的向量存储创建完成并且相关文档已添加,您很可能希望在运行链或代理时查询它。

直接查询

相似性搜索

执行简单的相似性搜索可以如下进行:

results = vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy", k=2
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
* Building an exciting new project with LangChain - come check it out! [{'_id': 'e7d95150-67f6-499f-b611-84367c50fa60', 'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'_id': '7614bb54-4eb5-4b3b-990c-00e35cb31f99', 'source': 'tweet'}]

带分数的相似性搜索

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)
for res, score in results:
print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
* [SIM=0.784560] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'_id': '8396a68d-f4a3-4176-a581-a1a8c303eea4', 'source': 'news'}]

使用相似性搜索进行预过滤

Atlas 向量搜索支持使用 MQL 操作符进行过滤的预过滤。以下是一个示例索引和查询,基于上面加载的相同数据,允许您对“页面”字段进行元数据过滤。您可以使用定义的过滤器更新现有索引,并使用向量搜索进行预过滤。

{
"fields":[
{
"type": "vector",
"path": "embedding",
"numDimensions": 1536,
"similarity": "cosine"
},
{
"type": "filter",
"path": "source"
}
]
}

您还可以使用 MongoDBAtlasVectorSearch.create_index 方法以编程方式更新索引。

vectorstore.create_index(
dimensions=1536,
filters=[{"type":"filter", "path":"source"}],
update=True
)

然后您可以使用以下方式运行带过滤器的查询:

results = vector_store.similarity_search(query="foo",k=1,pre_filter={"source": {"$eq": "https://example.com"}})
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")

其他搜索方法

还有多种其他搜索方法未在此笔记本中涵盖,例如MMR搜索或按向量搜索。有关AstraDBVectorStore可用搜索功能的完整列表,请查看API参考

通过转换为检索器进行查询

您还可以将向量存储转换为检索器,以便在您的链中更轻松地使用。

以下是如何将您的向量存储转换为检索器,然后使用简单的查询和过滤器调用检索器。

retriever = vector_store.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 1, "score_threshold": 0.2},
)
retriever.invoke("Stealing from the bank is a crime")
[Document(metadata={'_id': '8c31b84e-2636-48b6-8b99-9fccb47f7051', 'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]

用于检索增强生成的用法

有关如何使用此向量存储进行检索增强生成(RAG)的指南,请参见以下部分:

其他说明

  • 更多文档可以在 LangChain-MongoDB 网站找到
  • 此功能已普遍可用,准备好进行生产部署。
  • LangChain 版本 0.0.305 (发布说明) 引入了对 $vectorSearch MQL 阶段的支持,该功能在 MongoDB Atlas 6.0.11 和 7.0.2 中可用。使用早期版本 MongoDB Atlas 的用户需要将其 LangChain 版本固定为 <=0.0.304

API 参考

有关所有 MongoDBAtlasVectorSearch 功能和配置的详细文档,请访问 API 参考:https://python.langchain.com/api_reference/mongodb/index.html

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