Azure AI Search

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

# 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"

vector_store_address: str = "YOUR_AZURE_SEARCH_ENDPOINT"
vector_store_password: str = "YOUR_AZURE_SEARCH_ADMIN_KEY"

# 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,
)

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},
)

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']

# 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.

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)]

# 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.

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,
)

# 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'})]

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'})]