Azure Cosmos DB Mongo vCore

此笔记本向您展示了如何利用此集成的向量数据库在集合中存储文档，创建索引并使用近似最近邻算法（如 COS（余弦距离）、L2（欧几里得距离）和 IP（内积））执行向量搜索查询，以查找接近查询向量的文档。

Azure Cosmos DB 是为 OpenAI 的 ChatGPT 服务提供支持的数据库。它提供个位数毫秒的响应时间、自动和即时可扩展性，以及在任何规模下保证的速度。

适用于 MongoDB 的 Azure Cosmos DB vCore(https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/) 为开发人员提供了一种完全托管的兼容 MongoDB 的数据库服务，用于构建具有熟悉架构的现代应用程序。您可以应用您的 MongoDB 经验并继续使用您最喜欢的 MongoDB 驱动程序、SDK 和工具，方法是将您的应用程序指向适用于 MongoDB vCore 帐户的连接字符串的 API。

注册终身免费访问权限，立即开始。

%pip install --upgrade --quiet  pymongo langchain-openai langchain-community

[notice] A new release of pip is available: 23.2.1 -> 23.3.2
[notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.

import os

CONNECTION_STRING = "YOUR_CONNECTION_STRING"
INDEX_NAME = "izzy-test-index"
NAMESPACE = "izzy_test_db.izzy_test_collection"
DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")

我们想使用OpenAIEmbeddings，因此我们需要设置 Azure OpenAI API 密钥以及其他环境变量。

# Set up the OpenAI Environment Variables
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-05-15"
os.environ["OPENAI_API_BASE"] = (
    "YOUR_OPEN_AI_ENDPOINT"  # https://example.openai.azure.com/
)
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
os.environ["OPENAI_EMBEDDINGS_DEPLOYMENT"] = (
    "smart-agent-embedding-ada"  # the deployment name for the embedding model
)
os.environ["OPENAI_EMBEDDINGS_MODEL_NAME"] = "text-embedding-ada-002"  # the model name

现在，我们需要将文档加载到集合中，创建索引，然后对索引运行查询以检索匹配项。

如果您对某些参数有疑问，请参阅文档

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores.azure_cosmos_db import (
    AzureCosmosDBVectorSearch,
    CosmosDBSimilarityType,
    CosmosDBVectorSearchType,
)
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

SOURCE_FILE_NAME = "../../how_to/state_of_the_union.txt"

loader = TextLoader(SOURCE_FILE_NAME)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

# OpenAI Settings
model_deployment = os.getenv(
    "OPENAI_EMBEDDINGS_DEPLOYMENT", "smart-agent-embedding-ada"
)
model_name = os.getenv("OPENAI_EMBEDDINGS_MODEL_NAME", "text-embedding-ada-002")


openai_embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
    deployment=model_deployment, model=model_name, chunk_size=1
)

API 参考：TextLoader | AzureCosmosDBVectorSearch | CosmosDBSimilarityType | CosmosDBVectorSearchType | OpenAIEmbeddings | CharacterTextSplitter

from pymongo import MongoClient

# INDEX_NAME = "izzy-test-index-2"
# NAMESPACE = "izzy_test_db.izzy_test_collection"
# DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")

client: MongoClient = MongoClient(CONNECTION_STRING)
collection = client[DB_NAME][COLLECTION_NAME]

model_deployment = os.getenv(
    "OPENAI_EMBEDDINGS_DEPLOYMENT", "smart-agent-embedding-ada"
)
model_name = os.getenv("OPENAI_EMBEDDINGS_MODEL_NAME", "text-embedding-ada-002")

vectorstore = AzureCosmosDBVectorSearch.from_documents(
    docs,
    openai_embeddings,
    collection=collection,
    index_name=INDEX_NAME,
)

# Read more about these variables in detail here. https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search
num_lists = 100
dimensions = 1536
similarity_algorithm = CosmosDBSimilarityType.COS
kind = CosmosDBVectorSearchType.VECTOR_IVF
m = 16
ef_construction = 64
ef_search = 40
score_threshold = 0.1

vectorstore.create_index(
    num_lists, dimensions, similarity_algorithm, kind, m, ef_construction
)

{'raw': {'defaultShard': {'numIndexesBefore': 1,
   'numIndexesAfter': 2,
   'createdCollectionAutomatically': False,
   'ok': 1}},
 'ok': 1}

# perform a similarity search between the embedding of the query and the embeddings of the documents
query = "What did the president say about Ketanji Brown Jackson"
docs = vectorstore.similarity_search(query)

print(docs[0].page_content)

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

加载文档并创建索引后，您现在可以直接实例化向量存储并对索引运行查询

vectorstore = AzureCosmosDBVectorSearch.from_connection_string(
    CONNECTION_STRING, NAMESPACE, openai_embeddings, index_name=INDEX_NAME
)

# perform a similarity search between a query and the ingested documents
query = "What did the president say about Ketanji Brown Jackson"
docs = vectorstore.similarity_search(query)

print(docs[0].page_content)

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

vectorstore = AzureCosmosDBVectorSearch(
    collection, openai_embeddings, index_name=INDEX_NAME
)

# perform a similarity search between a query and the ingested documents
query = "What did the president say about Ketanji Brown Jackson"
docs = vectorstore.similarity_search(query)

print(docs[0].page_content)

Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. 

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. 

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.

过滤向量搜索（预览）

适用于 MongoDB 的 Azure Cosmos DB 支持使用 $lt、$lte、$eq、$neq、$gte、$gt、$in、$nin 和 $regex 进行预过滤。要使用此功能，请在 Azure 订阅的“预览功能”选项卡中启用“过滤向量搜索”。在此处了解有关预览功能的更多信息此处。

相关

• 向量存储概念指南
• 向量存储操作指南