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'})]Create a new index with a Scoring Profilefrom azure.search.documents.indexes.models import (    SearchableField,    SearchField,    SearchFieldDataType,    SimpleField,    ScoringProfile,    TextWeights,    ScoringFunction,    FreshnessScoringFunction,    FreshnessScoringParameters)embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)embedding_function = embeddings.embed_queryfields = [    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_configuration="default",    ),    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 functionsc_name = "scoring_profile"sc = ScoringProfile(    name=sc_name,    text_weights=TextWeights(weights={"title": 5}),
Azure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
Azure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications. ->: 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'})]Create a new index with a Scoring Profilefrom azure.search.documents.indexes.models import (    SearchableField,    SearchField,    SearchFieldDataType,    SimpleField,    ScoringProfile,    TextWeights,    ScoringFunction,    FreshnessScoringFunction,    FreshnessScoringParameters)embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)embedding_function = embeddings.embed_queryfields = [    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_configuration="default",    ),    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 functionsc_name = "scoring_profile"sc = ScoringProfile(    name=sc_name,    text_weights=TextWeights(weights={"title": 5}),