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-mongodb
和pymongo
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
- OpenAI
- HuggingFace
- Fake Embedding
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")
pip install -qU langchain-huggingface
from langchain_huggingface import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model="sentence-transformers/all-mpnet-base-v2")
pip install -qU langchain-core
from langchain_core.embeddings import FakeEmbeddings
embeddings = FakeEmbeddings(size=4096)
<!--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.')]