Azure Cosmos DB Mongo vCore
此笔记本向您展示如何利用这个集成的 向量数据库，将文档存储在集合中，创建索引，并使用近似最近邻算法（例如 COS（余弦距离）、L2（欧几里得距离）和 IP（内积））执行向量搜索查询，以定位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。它提供个位数毫秒级的响应时间、自动即时可扩展性，以及在任何规模下都能保证的速度。
Azure Cosmos DB for MongoDB vCore(https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/) 为开发人员提供完全托管的 MongoDB 兼容数据库服务，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，并通过将应用程序指向用于 MongoDB vCore 帐户连接字符串的 API，继续使用您喜欢的 MongoDB 驱动程序、SDK 和工具。
注册即可获得终身免费访问权限，立即开始使用。
%pip install --upgrade --quiet pymongo langchain-openai langchain-community
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(".")
我们想要使用 AzureOpenAIEmbeddings，因此我们需要设置我们的 Azure OpenAI API 密钥以及其他环境变量。
# Set up the OpenAI Environment Variables
os.environ["AZURE_OPENAI_API_KEY"] = "YOUR_AZURE_OPENAI_API_KEY"
os.environ["AZURE_OPENAI_ENDPOINT"] = "YOUR_AZURE_OPENAI_ENDPOINT"
os.environ["AZURE_OPENAI_API_VERSION"] = "2023-05-15"
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，然后针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 AzureOpenAIEmbeddings
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: AzureOpenAIEmbeddings = AzureOpenAIEmbeddings(
model=model_name, chunk_size=1
)
API 参考：TextLoader | AzureCosmosDBVectorSearch | CosmosDBSimilarityType | CosmosDBVectorSearchType | AzureOpenAIEmbeddings | CharacterTextSplitter
docs[0]
Document(metadata={'source': '../../how_to/state_of_the_union.txt'}, page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.')
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
)
"""
# DiskANN vectorstore
maxDegree = 40
dimensions = 1536
similarity_algorithm = CosmosDBSimilarityType.COS
kind = CosmosDBVectorSearchType.VECTOR_DISKANN
lBuild = 20
vectorstore.create_index(
dimensions=dimensions,
similarity=similarity_algorithm,
kind=kind ,
max_degree=maxDegree,
l_build=lBuild,
)
# -----------------------------------------------------------
# HNSW vectorstore
dimensions = 1536
similarity_algorithm = CosmosDBSimilarityType.COS
kind = CosmosDBVectorSearchType.VECTOR_HNSW
m = 16
ef_construction = 64
vectorstore.create_index(
dimensions=dimensions,
similarity=similarity_algorithm,
kind=kind ,
m=m,
ef_construction=ef_construction,
)
"""
'\n# DiskANN vectorstore\nmaxDegree = 40\ndimensions = 1536\nsimilarity_algorithm = CosmosDBSimilarityType.COS\nkind = CosmosDBVectorSearchType.VECTOR_DISKANN\nlBuild = 20\n\nvectorstore.create_index(\n dimensions=dimensions,\n similarity=similarity_algorithm,\n kind=kind ,\n max_degree=maxDegree,\n l_build=lBuild,\n )\n\n# -----------------------------------------------------------\n\n# HNSW vectorstore\ndimensions = 1536\nsimilarity_algorithm = CosmosDBSimilarityType.COS\nkind = CosmosDBVectorSearchType.VECTOR_HNSW\nm = 16\nef_construction = 64\n\nvectorstore.create_index(\n dimensions=dimensions,\n similarity=similarity_algorithm,\n kind=kind ,\n m=m,\n ef_construction=ef_construction,\n )\n'
# 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.
一旦文档被加载并且索引被创建，您现在可以直接实例化向量存储并针对索引运行查询
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.
筛选向量搜索（预览）
Azure Cosmos DB for MongoDB 支持使用 $lt、$lte、$eq、$neq、$gte、$gt、$in、$nin 和 $regex 进行预筛选。要使用此功能，请在 Azure 订阅的“预览功能”选项卡中启用“筛选向量搜索”。在此处了解有关预览功能的更多信息 此处。
# create a filter index
vectorstore.create_filter_index(
property_to_filter="metadata.source", index_name="filter_index"
)
{'raw': {'defaultShard': {'numIndexesBefore': 3,
'numIndexesAfter': 4,
'createdCollectionAutomatically': False,
'ok': 1}},
'ok': 1}
query = "What did the president say about Ketanji Brown Jackson"
docs = vectorstore.similarity_search(
query, pre_filter={"metadata.source": {"$ne": "filter content"}}
)
len(docs)
4
docs = vectorstore.similarity_search(
query,
pre_filter={"metadata.source": {"$ne": "../../how_to/state_of_the_union.txt"}},
)
len(docs)
0