Azure AI 搜索集成

pip install -qU  azure-search-documents
pip install -qU  azure-identity

import os

from langchain_community.vectorstores.azuresearch import AzureSearch
from langchain_openai import AzureOpenAIEmbeddings, OpenAIEmbeddings

# 选项 1：使用 OpenAI 账户
openai_api_key: str = "您的 API 密钥占位符"
openai_api_version: str = "2023-05-15"
model: str = "text-embedding-ada-002"

# 选项 2：使用带有嵌入模型部署的 Azure OpenAI 账户
azure_endpoint: str = "您的 Azure OpenAI 端点占位符"
azure_openai_api_key: str = "您的 Azure OpenAI 密钥占位符"
azure_openai_api_version: str = "2023-05-15"
azure_deployment: str = "text-embedding-ada-002"

vector_store_address: str = "您的 Azure 搜索端点"
vector_store_password: str = "您的 Azure 搜索管理员密钥"

# 选项 1：使用 OpenAIEmbeddings 和 OpenAI 账户
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(
    openai_api_key=openai_api_key, openai_api_version=openai_api_version, model=model
)

# 选项 2：使用 AzureOpenAIEmbeddings 和 Azure 账户
embeddings: AzureOpenAIEmbeddings = AzureOpenAIEmbeddings(
    azure_deployment=azure_deployment,
    openai_api_version=azure_openai_api_version,
    azure_endpoint=azure_endpoint,
    api_key=azure_openai_api_key,
)

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,
)

# 为 Azure 客户端指定其他属性，例如以下 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,
    # 配置 Azure 客户端的最大重试次数
    additional_search_client_options={"retry_total": 4},
)

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)

['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']

# 执行相似性搜索
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)

今晚。我呼吁参议院：通过《投票自由法案》。通过《约翰·刘易斯投票权法案》。同时，请通过《披露法案》，以便美国人能够知道谁在资助我们的选举。

今晚，我想向一位毕生致力于为国家服务的人致敬：斯蒂芬·布雷耶大法官——一位陆军退伍军人、宪法学者、即将退休的美国最高法院大法官。布雷耶大法官，感谢您的服务。

总统最严肃的宪法职责之一是提名某人担任美国最高法院大法官。

四天前，我提名了巡回上诉法院法官凯坦吉·布劳恩·杰克逊。她是我们国家顶尖的法律人才之一，将继续布雷耶大法官的卓越传统。

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)]

# 使用 search_type 参数执行混合搜索
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)

今晚。我呼吁参议院：通过《投票自由法案》。通过《约翰·刘易斯投票权法案》。同时，请通过《披露法案》，以便美国人能够知道谁在资助我们的选举。

今晚，我想向一位毕生致力于为国家服务的人致敬：斯蒂芬·布雷耶大法官——一位陆军退伍军人、宪法学者、即将退休的美国最高法院大法官。布雷耶大法官，感谢您的服务。

总统最严肃的宪法职责之一是提名某人担任美国最高法院大法官。

四天前，我提名了巡回上诉法院法官凯坦吉·布劳恩·杰克逊。她是我们国家顶尖的法律人才之一，将继续布雷耶大法官的卓越传统。

# 使用 hybrid_search 方法执行混合搜索
docs = vector_store.hybrid_search(
    query="What did the president say about Ketanji Brown Jackson", k=3
)
print(docs[0].page_content)

今晚。我呼吁参议院：通过《投票自由法案》。通过《约翰·刘易斯投票权法案》。同时，请通过《披露法案》，以便美国人能够知道谁在资助我们的选举。

今晚，我想向一位毕生致力于为国家服务的人致敬：斯蒂芬·布雷耶大法官——一位陆军退伍军人、宪法学者、即将退休的美国最高法院大法官。布雷耶大法官，感谢您的服务。

总统最严肃的宪法职责之一是提名某人担任美国最高法院大法官。

四天前，我提名了巡回上诉法院法官凯坦吉·布劳恩·杰克逊。她是我们国家顶尖的法律人才之一，将继续布雷耶大法官的卓越传统。

from azure.search.documents.indexes.models import (
    ScoringProfile,
    SearchableField,
    SearchField,
    SearchFieldDataType,
    SimpleField,
    TextWeights,
)

#  如果 Azure OpenAI 是您的提供商，请将 OpenAIEmbeddings 替换为 AzureOpenAIEmbeddings。
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,
    ),
    # 存储标题的附加字段
    SearchableField(
        name="title",
        type=SearchFieldDataType.String,
        searchable=True,
    ),
    # 用于按文档源筛选的附加字段
    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,
)

# 元数据字典中的数据，如果索引中有对应的字段，将被添加到索引中。
# 在此示例中，元数据字典包含 title、source 和一个随机字段。
# title 和 source 作为单独字段添加到索引中，但随机值被忽略，因为它未在模式中定义。
# 随机字段仅存储在元数据字段中。
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'})]

from azure.search.documents.indexes.models import (
    FreshnessScoringFunction,
    FreshnessScoringParameters,
    ScoringProfile,
    SearchableField,
    SearchField,
    SearchFieldDataType,
    SimpleField,
    TextWeights,
)

#  如果 Azure OpenAI 是您的提供商，请将 OpenAIEmbeddings 替换为 AzureOpenAIEmbeddings。
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,
    ),
    # 存储标题的附加字段
    SearchableField(
        name="title",
        type=SearchFieldDataType.String,
        searchable=True,
    ),
    # 用于按文档源筛选的附加字段
    SimpleField(
        name="source",
        type=SearchFieldDataType.String,
        filterable=True,
    ),
    # 用于上次文档更新的附加数据字段
    SimpleField(
        name="last_update",
        type=SearchFieldDataType.DateTimeOffset,
        searchable=True,
        filterable=True,
    ),
]
# 添加带有新鲜度函数的自定义评分配置文件
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,
)

# 添加相同数据但不同的 last_update 以显示评分配置文件效果
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'})]