Azure AI 搜索
Azure AI Search (formerly known as Azure Search and Azure Cognitive Search)是云搜索服务，为开发人员提供基础设施、API和工具，用于大规模的向量、关键词和混合查询的信息检索。
您需要使用 langchain-community 安装 pip install -qU langchain-community 才能使用此集成
安装 Azure AI 搜索 SDK
使用 azure-search-documents 包版本 11.4.0 或更新的版本。
%pip install --upgrade --quiet azure-search-documents
%pip install --upgrade --quiet azure-identity
导入所需的库
OpenAIEmbeddings 是假设的，但如果你使用的是 Azure OpenAI，请导入 AzureOpenAIEmbeddings。
import os
from langchain_community.vectorstores.azuresearch import AzureSearch
from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings
配置 OpenAI 设置
设置您OpenAI提供商的变量。您需要一个OpenAI账户或一个Azure OpenAI账户来生成嵌入。
# Option 1: use an OpenAI account
openai_api_key: str = "PLACEHOLDER FOR YOUR API KEY"
openai_api_version: str = "2023-05-15"
model: str = "text-embedding-ada-002"
# Option 2: use an Azure OpenAI account with a deployment of an embedding model
azure_endpoint: str = "PLACEHOLDER FOR YOUR AZURE OPENAI ENDPOINT"
azure_openai_api_key: str = "PLACEHOLDER FOR YOUR AZURE OPENAI KEY"
azure_openai_api_version: str = "2023-05-15"
azure_deployment: str = "text-embedding-ada-002"
配置向量存储设置
您需要一个 Azure订阅 和 Azure AI搜索服务 来使用此向量存储集成。对于小型和有限的工作负载，免费版本可用。
设置Azure AI搜索URL和管理员API密钥的变量。您可以在Azure门户中获取这些变量。
vector_store_address: str = "YOUR_AZURE_SEARCH_ENDPOINT"
vector_store_password: str = "YOUR_AZURE_SEARCH_ADMIN_KEY"
创建嵌入和向量存储实例
创建OpenAIEmbeddings和AzureSearch类的实例。完成此步骤后，您应该在Azure AI搜索资源上有一个空的搜索索引。集成模块提供了一个默认模式。
# Option 1: Use OpenAIEmbeddings with OpenAI account
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
openai_api_key=openai_api_key, openai_api_version=openai_api_version, model=model
)
# Option 2: Use AzureOpenAIEmbeddings with an Azure account
embeddings: AzureOpenAIEmbeddings = AzureOpenAIEmbeddings(
azure_deployment=azure_deployment,
openai_api_version=azure_openai_api_version,
azure_endpoint=azure_endpoint,
api_key=azure_openai_api_key,
)
创建向量存储实例
使用上述嵌入创建 AzureSearch 类的实例
index_name: str = "langchain-vector-demo"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query,
)
# Specify additional properties for the Azure client such as the following https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md#configurations
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query,
# Configure max retries for the Azure client
additional_search_client_options={"retry_total": 4},
)
将文本和嵌入插入向量存储
这一步会加载、分块和向量化样本文档，然后将内容索引到Azure AI搜索中的搜索索引中。
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../how_to/state_of_the_union.txt", encoding="utf-8")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
vector_store.add_documents(documents=docs)
API 参考:文本加载器 |CharacterTextSplitter
['M2U1OGM4YzAtYjMxYS00Nzk5LTlhNDgtZTc3MGVkNTg1Mjc0',
'N2I2MGNiZDEtNDdmZS00YWNiLWJhYTYtYWEzMmFiYzU1ZjZm',
'YWFmNDViNTQtZTc4MS00MTdjLTkzZjQtYTJkNmY1MDU4Yzll',
'MjgwY2ExZDctYTUxYi00NjE4LTkxMjctZDA1NDQ1MzU4NmY1',
'NGE4NzhkNTAtZWYxOC00ZmI5LTg0MTItZDQ1NzMxMWVmMTIz',
'MTYwMWU3YjAtZDIzOC00NTYwLTgwMmEtNDI1NzA2MWVhMDYz',
'NGM5N2NlZjgtMTc5Ny00OGEzLWI5YTgtNDFiZWE2MjBlMzA0',
'OWQ4M2MyMTYtMmRkNi00ZDUxLWI0MDktOGE2NjMxNDFhYzFm',
'YWZmZGJkOTAtOGM3My00MmNiLTg5OWUtZGMwMDQwYTk1N2Vj',
'YTc3MTI2OTktYmVkMi00ZGU4LTgyNmUtNTY1YzZjMDg2YWI3',
'MTQwMmVlYjEtNDI0MS00N2E0LWEyN2ItZjhhYWU0YjllMjRk',
'NjJjYWY4ZjctMzgyNi00Y2I5LTkwY2UtZjRkMjJhNDQxYTFk',
'M2ZiM2NiYTMtM2ZiMS00YWJkLWE3ZmQtNDZiODcyOTMyYWYx',
'MzNmZTNkMWYtMjNmYS00Y2NmLTg3ZjQtYTZjOWM1YmJhZTRk',
'ZDY3MDc1NzYtY2YzZS00ZjExLWEyMjAtODhiYTRmNDUzMTBi',
'ZGIyYzA4NzUtZGM2Ni00MDUwLWEzZjYtNTg3MDYyOWQ5MWQy',
'NTA0MjBhMzYtOTYzMi00MDQ2LWExYWQtMzNiN2I4ODM4ZGZl',
'OTdjYzU2NGUtNWZjNC00N2ZmLWExMjQtNjhkYmZkODg4MTY3',
'OThhMWZmMjgtM2EzYS00OWZkLTk1NGEtZTdkNmRjNWYxYmVh',
'ZGVjMTQ0NzctNDVmZC00ZWY4LTg4N2EtMDQ1NWYxNWM5NDVh',
'MjRlYzE4YzItZTMxNy00OGY3LThmM2YtMjM0YmRhYTVmOGY3',
'MWU0NDA3ZDQtZDE4MS00OWMyLTlmMzktZjdkYzZhZmUwYWM3',
'ZGM2ZDhhY2MtM2NkNi00MzZhLWJmNTEtMmYzNjEwMzE3NmZl',
'YjBmMjkyZTItYTNlZC00MmY2LThiMzYtMmUxY2MyNDlhNGUw',
'OThmYTQ0YzEtNjk0MC00NWIyLWE1ZDQtNTI2MTZjN2NlODcw',
'NDdlOGU1ZGQtZTVkMi00M2MyLWExN2YtOTc2ODk3OWJmNmQw',
'MDVmZGNkYTUtNWI2OS00YjllLTk0YTItZDRmNWQxMWU3OTVj',
'YWFlNTVmNjMtMDZlNy00NmE5LWI0ODUtZTI3ZTFmZWRmNzU0',
'MmIzOTkxODQtODYxMi00YWM2LWFjY2YtNjRmMmEyM2JlNzMw',
'ZmI1NDhhNWItZWY0ZS00NTNhLWEyNDEtMTE2OWYyMjc4YTU2',
'YTllYTc5OTgtMzJiNC00ZjZjLWJiMzUtNWVhYzFjYzgxMjU2',
'ODZlZWUyOTctOGY4OS00ZjA3LWIyYTUtNDVlNDUyN2E4ZDFk',
'Y2M0MWRlM2YtZDU4Ny00MjZkLWE5NzgtZmRkMTNhZDg2YjEy',
'MDNjZWQ2ODEtMWZiMy00OTZjLTk3MzAtZjE4YjIzNWVhNTE1',
'OTE1NDY0NzMtODNkZS00MTk4LTk4NWQtZGVmYjQ2YjFlY2Q0',
'ZTgwYWQwMjEtN2ZlOS00NDk2LWIxNzUtNjk2ODE3N2U0Yzlj',
'ZDkxOTgzMGUtZGExMC00Yzg0LWJjMGItOWQ2ZmUwNWUwOGJj',
'ZGViMGI2NDEtZDdlNC00YjhiLTk0MDUtYjEyOTVlMGU1Y2I2',
'ODliZTYzZTctZjdlZS00YjBjLWFiZmYtMDJmNjQ0YjU3ZDcy',
'MDFjZGI1NzUtOTc0Ni00NWNmLThhYzYtYzRlZThkZjMwM2Vl',
'ZjY2ZmRiN2EtZWVhNS00ODViLTk4YjYtYjQ2Zjc4MDdkYjhk',
'ZTQ3NDMwODEtMTQwMy00NDFkLWJhZDQtM2UxN2RkOTU1MTdl']
执行向量相似性搜索
执行纯向量相似度搜索，请使用similarity_search()方法:
# Perform a similarity search
docs = vector_store.similarity_search(
query="What did the president say about Ketanji Brown Jackson",
k=3,
search_type="similarity",
)
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.
执行向量相似性搜索并带有相关性评分
使用`similarity_search_with_relevance_scores()`方法执行纯向量相似性搜索。不符合阈值要求的查询将被排除。
docs_and_scores = vector_store.similarity_search_with_relevance_scores(
query="What did the president say about Ketanji Brown Jackson",
k=4,
score_threshold=0.80,
)
from pprint import pprint
pprint(docs_and_scores)
[(Document(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. \n\nTonight, 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. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'source': '../../how_to/state_of_the_union.txt'}),
0.84402436),
(Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../how_to/state_of_the_union.txt'}),
0.82128483),
(Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../how_to/state_of_the_union.txt'}),
0.8151042),
(Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../how_to/state_of_the_union.txt'}),
0.8148832)]
执行混合搜索
使用search_type方法或hybrid_search()方法执行混合搜索。向量和非向量文本字段并行查询，结果合并，并返回统一结果集中的最相关匹配项。
# Perform a hybrid search using the search_type parameter
docs = vector_store.similarity_search(
query="What did the president say about Ketanji Brown Jackson",
k=3,
search_type="hybrid",
)
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.
# Perform a hybrid search using the hybrid_search method
docs = vector_store.hybrid_search(
query="What did the president say about Ketanji Brown Jackson", k=3
)
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.
自定义模式和查询
此部分向您展示了如何用自定义模式替换默认模式。
创建具有自定义可筛选字段的新索引
此方案显示了字段定义。它基于默认方案，并添加了几项新的可过滤字段。因为使用的是默认向量配置，所以这里不会看到向量配置或向量配置覆盖内容。默认向量配置文件的名称为 "myHnswProfile"，并且使用 Hierarchical Navigable Small World (HNSW) 用于索引和针对 content_vector 字段进行查询。
此步骤中没有此架构的数据。当您执行该单元格时，应在 Azure AI 搜索中得到一个空索引。
from azure.search.documents.indexes.models import (
ScoringProfile,
SearchableField,
SearchField,
SearchFieldDataType,
SimpleField,
TextWeights,
)
# Replace OpenAIEmbeddings with AzureOpenAIEmbeddings if Azure OpenAI is your provider.
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
openai_api_key=openai_api_key, openai_api_version=openai_api_version, model=model
)
embedding_function = embeddings.embed_query
fields = [
SimpleField(
name="id",
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name="content",
type=SearchFieldDataType.String,
searchable=True,
),
SearchField(
name="content_vector",
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=len(embedding_function("Text")),
vector_search_profile_name="myHnswProfile",
),
SearchableField(
name="metadata",
type=SearchFieldDataType.String,
searchable=True,
),
# Additional field to store the title
SearchableField(
name="title",
type=SearchFieldDataType.String,
searchable=True,
),
# Additional field for filtering on document source
SimpleField(
name="source",
type=SearchFieldDataType.String,
filterable=True,
),
]
index_name: str = "langchain-vector-demo-custom"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embedding_function,
fields=fields,
)
添加数据并执行包含过滤器的查询
此示例根据自定义模式将数据添加到向量存储中。它将在标题和来源字段中加载文本。来源字段是可以过滤的。本部分中的样本查询基于来源字段的内容对结果进行筛选。
# Data in the metadata dictionary with a corresponding field in the index will be added to the index.
# In this example, the metadata dictionary contains a title, a source, and a random field.
# The title and the source are added to the index as separate fields, but the random value is ignored because it's not defined in the schema.
# The random field is only stored in the metadata field.
vector_store.add_texts(
["Test 1", "Test 2", "Test 3"],
[
{"title": "Title 1", "source": "A", "random": "10290"},
{"title": "Title 2", "source": "A", "random": "48392"},
{"title": "Title 3", "source": "B", "random": "32893"},
],
)
['ZjhmMTg0NTEtMjgwNC00N2M0LWFiZGEtMDllMGU1Mzk1NWRm',
'MzQwYWUwZDEtNDJkZC00MzgzLWIwMzItYzMwOGZkYTRiZGRi',
'ZjFmOWVlYTQtODRiMC00YTY3LTk2YjUtMzY1NDBjNjY5ZmQ2']
res = vector_store.similarity_search(query="Test 3 source1", k=3, search_type="hybrid")
res
[Document(page_content='Test 3', metadata={'title': 'Title 3', 'source': 'B', 'random': '32893'}),
Document(page_content='Test 1', metadata={'title': 'Title 1', 'source': 'A', 'random': '10290'}),
Document(page_content='Test 2', metadata={'title': 'Title 2', 'source': 'A', 'random': '48392'})]
res = vector_store.similarity_search(
query="Test 3 source1", k=3, search_type="hybrid", filters="source eq 'A'"
)
res
[Document(page_content='Test 1', metadata={'title': 'Title 1', 'source': 'A', 'random': '10290'}),
Document(page_content='Test 2', metadata={'title': 'Title 2', 'source': 'A', 'random': '48392'})]
创建一个具有评分配置文件的新索引
这里展示的是另一个自定义模式，其中包括了一个评分配置文件定义。评分配置文件用于调整非向量内容的相关性，在混合搜索场景中非常有帮助。
from azure.search.documents.indexes.models import (
FreshnessScoringFunction,
FreshnessScoringParameters,
ScoringProfile,
SearchableField,
SearchField,
SearchFieldDataType,
SimpleField,
TextWeights,
)
# Replace OpenAIEmbeddings with AzureOpenAIEmbeddings if Azure OpenAI is your provider.
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
openai_api_key=openai_api_key, openai_api_version=openai_api_version, model=model
)
embedding_function = embeddings.embed_query
fields = [
SimpleField(
name="id",
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name="content",
type=SearchFieldDataType.String,
searchable=True,
),
SearchField(
name="content_vector",
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=len(embedding_function("Text")),
vector_search_profile_name="myHnswProfile",
),
SearchableField(
name="metadata",
type=SearchFieldDataType.String,
searchable=True,
),
# Additional field to store the title
SearchableField(
name="title",
type=SearchFieldDataType.String,
searchable=True,
),
# Additional field for filtering on document source
SimpleField(
name="source",
type=SearchFieldDataType.String,
filterable=True,
),
# Additional data field for last doc update
SimpleField(
name="last_update",
type=SearchFieldDataType.DateTimeOffset,
searchable=True,
filterable=True,
),
]
# Adding a custom scoring profile with a freshness function
sc_name = "scoring_profile"
sc = ScoringProfile(
name=sc_name,
text_weights=TextWeights(weights={"title": 5}),
function_aggregation="sum",
functions=[
FreshnessScoringFunction(
field_name="last_update",
boost=100,
parameters=FreshnessScoringParameters(boosting_duration="P2D"),
interpolation="linear",
)
],
)
index_name = "langchain-vector-demo-custom-scoring-profile"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query,
fields=fields,
scoring_profiles=[sc],
default_scoring_profile=sc_name,
)
# Adding same data with different last_update to show Scoring Profile effect
from datetime import datetime, timedelta
today = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%S-00:00")
yesterday = (datetime.utcnow() - timedelta(days=1)).strftime("%Y-%m-%dT%H:%M:%S-00:00")
one_month_ago = (datetime.utcnow() - timedelta(days=30)).strftime(
"%Y-%m-%dT%H:%M:%S-00:00"
)
vector_store.add_texts(
["Test 1", "Test 1", "Test 1"],
[
{
"title": "Title 1",
"source": "source1",
"random": "10290",
"last_update": today,
},
{
"title": "Title 1",
"source": "source1",
"random": "48392",
"last_update": yesterday,
},
{
"title": "Title 1",
"source": "source1",
"random": "32893",
"last_update": one_month_ago,
},
],
)
['NjUwNGQ5ZDUtMGVmMy00OGM4LWIxMGYtY2Y2MDFmMTQ0MjE5',
'NWFjN2YwY2UtOWQ4Yi00OTNhLTg2MGEtOWE0NGViZTVjOGRh',
'N2Y2NWUyZjctMDBjZC00OGY4LWJlZDEtNTcxYjQ1MmI1NjYx']
res = vector_store.similarity_search(query="Test 1", k=3, search_type="similarity")
res
[Document(page_content='Test 1', metadata={'title': 'Title 1', 'source': 'source1', 'random': '32893', 'last_update': '2024-01-24T22:18:51-00:00'}),
Document(page_content='Test 1', metadata={'title': 'Title 1', 'source': 'source1', 'random': '48392', 'last_update': '2024-02-22T22:18:51-00:00'}),
Document(page_content='Test 1', metadata={'title': 'Title 1', 'source': 'source1', 'random': '10290', 'last_update': '2024-02-23T22:18:51-00:00'})]