Enable kwargs for semantic ranking

integrations/azure_ai_search/example/document_store.py

@@ -1,5 +1,4 @@
 from haystack import Document
-from haystack.document_stores.types import DuplicatePolicy
 
 from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore
 
@@ -30,7 +29,7 @@
         meta={"version": 2.0, "label": "chapter_three"},
     ),
 ]
-document_store.write_documents(documents, policy=DuplicatePolicy.SKIP)
+document_store.write_documents(documents)
 
 filters = {
     "operator": "AND",

integrations/azure_ai_search/example/embedding_retrieval.py

@@ -1,7 +1,6 @@
 from haystack import Document, Pipeline
 from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
 from haystack.components.writers import DocumentWriter
-from haystack.document_stores.types import DuplicatePolicy
 
 from haystack_integrations.components.retrievers.azure_ai_search import AzureAISearchEmbeddingRetriever
 from haystack_integrations.document_stores.azure_ai_search import AzureAISearchDocumentStore
@@ -38,9 +37,7 @@
 # Indexing Pipeline
 indexing_pipeline = Pipeline()
 indexing_pipeline.add_component(instance=document_embedder, name="doc_embedder")
-indexing_pipeline.add_component(
-    instance=DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP), name="doc_writer"
-)
+indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name="doc_writer")
 indexing_pipeline.connect("doc_embedder", "doc_writer")
 
 indexing_pipeline.run({"doc_embedder": {"documents": documents}})

integrations/azure_ai_search/src/haystack_integrations/components/retrievers/azure_ai_search/embedding_retriever.py

@@ -25,6 +25,7 @@ def __init__(
         filters: Optional[Dict[str, Any]] = None,
         top_k: int = 10,
         filter_policy: Union[str, FilterPolicy] = FilterPolicy.REPLACE,
+        **kwargs,
     ):
         """
         Create the AzureAISearchEmbeddingRetriever component.
@@ -43,6 +44,7 @@ def __init__(
         self._filter_policy = (
             filter_policy if isinstance(filter_policy, FilterPolicy) else FilterPolicy.from_str(filter_policy)
         )
+        self._kwargs = kwargs
 
         if not isinstance(document_store, AzureAISearchDocumentStore):
             message = "document_store must be an instance of AzureAISearchDocumentStore"
@@ -106,9 +108,7 @@ def run(self, query_embedding: List[float], filters: Optional[Dict[str, Any]] =
 
         try:
             docs = self._document_store._embedding_retrieval(
-                query_embedding=query_embedding,
-                filters=normalized_filters,
-                top_k=top_k,
+                query_embedding=query_embedding, filters=normalized_filters, top_k=top_k, **self._kwargs
             )
         except Exception as e:
             raise e

integrations/azure_ai_search/src/haystack_integrations/document_stores/azure_ai_search/document_store.py

@@ -87,19 +87,20 @@ def __init__(
         :param vector_search_configuration: Configuration option related to vector search.
             Default configuration uses the HNSW algorithm with cosine similarity to handle vector searches.
 
-        :param kwargs: Optional keyword parameters for Azure AI Search.
+        :param kwargs: Optional keyword parameters to be passed to SearchIndex during index creation.
             Some of the supported parameters:
-                - `api_version`: The Search API version to use for requests.
-                - `audience`: sets the Audience to use for authentication with Azure Active Directory (AAD).
-                The audience is not considered when using a shared key. If audience is not provided,
-                the public cloud audience will be assumed.
+                - `semantic_search`: Defines semantic configuration of the search index. This parameter is needed
+                to enable semantic search capabilities in index.
+                - `similarity`: The type of similarity algorithm to be used when scoring and ranking the documents
+                matching a search query. The similarity algorithm can only be defined at index creation time and
+                cannot be modified on existing indexes.
 
         For more information on parameters, see the [official Azure AI Search documentation](https://learn.microsoft.com/en-us/azure/search/)
         """
 
         azure_endpoint = azure_endpoint or os.environ.get("AZURE_SEARCH_SERVICE_ENDPOINT") or None
         if not azure_endpoint:
-            msg = "Please provide an Azure endpoint or set the environment variable AZURE_OPENAI_ENDPOINT."
+            msg = "Please provide an Azure endpoint or set the environment variable AZURE_SEARCH_SERVICE_ENDPOINT."
             raise ValueError(msg)
 
         api_key = api_key or os.environ.get("AZURE_SEARCH_API_KEY") or None
@@ -128,7 +129,10 @@ def client(self) -> SearchClient:
         credential = AzureKeyCredential(resolved_key) if resolved_key else DefaultAzureCredential()
         try:
             if not self._index_client:
-                self._index_client = SearchIndexClient(resolved_endpoint, credential, **self._kwargs)
+                self._index_client = SearchIndexClient(
+                    resolved_endpoint,
+                    credential,
+                )
             if not self._index_exists(self._index_name):
                 # Create a new index if it does not exist
                 logger.debug(
@@ -151,7 +155,7 @@ def client(self) -> SearchClient:
 
         return self._client
 
-    def _create_index(self, index_name: str, **kwargs) -> None:
+    def _create_index(self, index_name: str) -> None:
         """
         Creates a new search index.
         :param index_name: Name of the index to create. If None, the index name from the constructor is used.
@@ -177,7 +181,7 @@ def _create_index(self, index_name: str, **kwargs) -> None:
         if self._metadata_fields:
             default_fields.extend(self._create_metadata_index_fields(self._metadata_fields))
         index = SearchIndex(
-            name=index_name, fields=default_fields, vector_search=self._vector_search_configuration, **kwargs
+            name=index_name, fields=default_fields, vector_search=self._vector_search_configuration, **self._kwargs
         )
         if self._index_client:
             self._index_client.create_index(index)
@@ -411,6 +415,7 @@ def _embedding_retrieval(
         top_k: int = 10,
         fields: Optional[List[str]] = None,
         filters: Optional[Dict[str, Any]] = None,
+        **kwargs,
     ) -> List[Document]:
         """
         Retrieves documents that are most similar to the query embedding using a vector similarity metric.
@@ -435,6 +440,8 @@ def _embedding_retrieval(
             raise ValueError(msg)
 
         vector_query = VectorizedQuery(vector=query_embedding, k_nearest_neighbors=top_k, fields="embedding")
-        result = self.client.search(search_text=None, vector_queries=[vector_query], select=fields, filter=filters)
+        result = self.client.search(
+            search_text=None, vector_queries=[vector_query], select=fields, filter=filters, **kwargs
+        )
         azure_docs = list(result)
         return self._convert_search_result_to_documents(azure_docs)