feat: add Azure AI multi-provider support (TTS, STT, Embeddings, Realtime)

Enables Azure AI services across all model layers so users with Azure
credits can consolidate billing on a single provider.

- Voice (TTS): AzureSpeechTTSConfiguration via azure_speech provider
- Transcriber (STT): AzureSpeechSTTConfiguration via azure_speech provider
- Embedding: AzureOpenAIEmbeddingsConfiguration via azure provider
- Realtime: AzureRealtimeLLMConfiguration via azure_realtime provider

New files:
- api/services/pipecat/realtime/azure_realtime.py
- api/services/gen_ai/embedding/azure_openai_service.py
- api/tests/test_azure_speech_service_factory.py

The UI picks up all four providers automatically from the schema —
no frontend changes required.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

api/services/workflow/tools/knowledge_base.py

@@ -13,7 +13,8 @@ from loguru import logger
 from opentelemetry import trace
 
 from api.db import db_client
-from api.services.gen_ai import OpenAIEmbeddingService
+from api.services.configuration.registry import ServiceProviders
+from api.services.gen_ai import AzureOpenAIEmbeddingService, OpenAIEmbeddingService
 from api.services.pipecat.tracing_config import ensure_tracing
 
 
@@ -25,6 +26,9 @@ async def retrieve_from_knowledge_base(
     embeddings_api_key: Optional[str] = None,
     embeddings_model: Optional[str] = None,
     embeddings_base_url: Optional[str] = None,
+    embeddings_provider: Optional[str] = None,
+    embeddings_endpoint: Optional[str] = None,
+    embeddings_api_version: Optional[str] = None,
     tracing_context=None,
 ) -> Dict[str, Any]:
     """Retrieve relevant information from the knowledge base using vector similarity search.
@@ -68,6 +72,9 @@ async def retrieve_from_knowledge_base(
                 embeddings_api_key,
                 embeddings_model,
                 embeddings_base_url,
+                embeddings_provider,
+                embeddings_endpoint,
+                embeddings_api_version,
             )
 
         # Create span with parent context
@@ -105,6 +112,9 @@ async def retrieve_from_knowledge_base(
                         embeddings_api_key,
                         embeddings_model,
                         embeddings_base_url,
+                        embeddings_provider,
+                        embeddings_endpoint,
+                        embeddings_api_version,
                     )
 
                     # Add result metadata to span
@@ -179,6 +189,9 @@ async def retrieve_from_knowledge_base(
                 embeddings_api_key,
                 embeddings_model,
                 embeddings_base_url,
+                embeddings_provider,
+                embeddings_endpoint,
+                embeddings_api_version,
             )
     else:
         # Tracing is disabled - perform retrieval without tracing
@@ -189,6 +202,10 @@ async def retrieve_from_knowledge_base(
             limit,
             embeddings_api_key,
             embeddings_model,
+            embeddings_base_url,
+            embeddings_provider,
+            embeddings_endpoint,
+            embeddings_api_version,
         )
 
 
@@ -200,6 +217,9 @@ async def _perform_retrieval(
     embeddings_api_key: Optional[str] = None,
     embeddings_model: Optional[str] = None,
     embeddings_base_url: Optional[str] = None,
+    embeddings_provider: Optional[str] = None,
+    embeddings_endpoint: Optional[str] = None,
+    embeddings_api_version: Optional[str] = None,
 ) -> Dict[str, Any]:
     """Internal function to perform the actual retrieval operation.
 
@@ -240,12 +260,21 @@ async def _perform_retrieval(
                     "Model Configurations > Embedding."
                 )
 
-            embedding_service = OpenAIEmbeddingService(
-                db_client=db_client,
-                api_key=embeddings_api_key,
-                model_id=embeddings_model or "text-embedding-3-small",
-                base_url=embeddings_base_url,
-            )
+            if embeddings_provider == ServiceProviders.AZURE.value and embeddings_endpoint:
+                embedding_service = AzureOpenAIEmbeddingService(
+                    db_client=db_client,
+                    api_key=embeddings_api_key,
+                    endpoint=embeddings_endpoint,
+                    model_id=embeddings_model or "text-embedding-3-small",
+                    api_version=embeddings_api_version or "2024-02-15-preview",
+                )
+            else:
+                embedding_service = OpenAIEmbeddingService(
+                    db_client=db_client,
+                    api_key=embeddings_api_key,
+                    model_id=embeddings_model or "text-embedding-3-small",
+                    base_url=embeddings_base_url,
+                )
 
             results = await embedding_service.search_similar_chunks(
                 query=query,