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Fetch the complete documentation index at: https://langchain-zh.cn/llms.txt

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本笔记本向您展示如何利用此集成的 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（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 是支持 OpenAI ChatGPT 服务的数据库。它提供个位数毫秒级的响应时间、自动即时扩展能力，以及任何规模下的速度保证。 Azure Cosmos DB for MongoDB vCore 为开发人员提供了一个完全托管的 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 MongoDB vCore 账户连接字符串的 API，继续使用您最喜欢的 MongoDB 驱动程序、SDK 和工具。 注册 以获取终身免费访问权限，立即开始。 
pip install -qU pymongo langchain-openai langchain-azure-ai

import os

CONNECTION_STRING = "YOUR_CONNECTION_STRING"
INDEX_NAME = "langchain-test-index"
NAMESPACE = "langchain_test_db.langchain_test_collection"
DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")
我们要使用 AzureOpenAIEmbeddings，因此需要设置我们的 Azure OpenAI API 密钥以及其他环境变量。 
# 设置 OpenAI 环境变量

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"
os.environ["OPENAI_EMBEDDINGS_DEPLOYMENT"] = "text-embedding-ada-002"
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_azure_ai.vectorstores.azure_cosmos_db_mongo_vcore import (
    AzureCosmosDBMongoVCoreVectorSearch,
    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 设置
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
)

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 = AzureCosmosDBMongoVCoreVectorSearch.from_documents(
    docs,
    openai_embeddings,
    collection=collection,
    index_name=INDEX_NAME,
)

# 在此处详细了解这些变量的详细信息。 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 向量存储
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 向量存储
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'

# 执行查询嵌入与文档嵌入之间的相似性搜索
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.
一旦文档已加载且索引已创建，您现在可以直接实例化向量存储并针对索引运行查询。 
vectorstore = AzureCosmosDBMongoVCoreVectorSearch.from_connection_string(
    CONNECTION_STRING, NAMESPACE, openai_embeddings, index_name=INDEX_NAME
)

# 执行查询与已加载文档之间的相似性搜索
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 = AzureCosmosDBMongoVCoreVectorSearch(
    collection, openai_embeddings, index_name=INDEX_NAME
)

# 执行查询与已加载文档之间的相似性搜索
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）

Azure Cosmos DB for MongoDB 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”。了解有关 过滤向量搜索预览功能 的更多信息。 
# 创建过滤索引
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
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