feat: enhance Azure OpenAI embeddings and add voice support for Azure provider

- Introduced a fixed parameter order for AzureOpenAIEmbeddings to resolve compatibility issues.
- Updated the voice selection logic to include Azure voices in the podcaster utility.
- Modified the page limit service to use a more efficient method for retrieving users.

surfsense_backend/app/agents/podcaster/utils.py

@@ -63,6 +63,17 @@ def get_voice_for_provider(provider: str, speaker_id: int) -> dict | str:
             },
         }
         return vertex_voices.get(speaker_id, vertex_voices[0])
+    elif provider_type == "azure":
+        # OpenAI voice mapping - simple string values
+        azure_voices = {
+            0: "alloy",  # Default/intro voice
+            1: "echo",  # First speaker
+            2: "fable",  # Second speaker
+            3: "onyx",  # Third speaker
+            4: "nova",  # Fourth speaker
+            5: "shimmer",  # Fifth speaker
+        }
+        return azure_voices.get(speaker_id, "alloy")
 
     else:
         # Default fallback to OpenAI format for unknown providers

surfsense_backend/app/config/__init__.py

@@ -1,6 +1,7 @@
 import os
 import shutil
 from pathlib import Path
+from typing import Any
 
 from chonkie import AutoEmbeddings, CodeChunker, RecursiveChunker
 from chonkie.embeddings.azure_openai import AzureOpenAIEmbeddings
@@ -8,8 +9,46 @@ from chonkie.embeddings.registry import EmbeddingsRegistry
 from dotenv import load_dotenv
 from rerankers import Reranker
 
+
+# Monkey patch AzureOpenAIEmbeddings to fix parameter order issue
+# This is a temporary workaround until the upstream chonkie library is fixed
+class FixedAzureOpenAIEmbeddings(AzureOpenAIEmbeddings):
+    """Wrapper around AzureOpenAIEmbeddings with fixed parameter order."""
+
+    def __init__(
+        self,
+        model: str = "text-embedding-3-small",
+        azure_endpoint: str | None = None,
+        tokenizer: Any | None = None,
+        dimension: int | None = None,
+        azure_api_key: str | None = None,
+        api_version: str = "2024-10-21",
+        deployment: str | None = None,
+        max_retries: int = 3,
+        timeout: float = 60.0,
+        batch_size: int = 128,
+        **kwargs: dict[str, Any],
+    ):
+        """Initialize with model as first parameter to avoid conflicts."""
+        # Call parent's __init__ by explicitly passing azure_endpoint as first arg
+        # to maintain compatibility with the original signature
+        super().__init__(
+            azure_endpoint=azure_endpoint or os.getenv("AZURE_OPENAI_ENDPOINT", ""),
+            model=model,
+            tokenizer=tokenizer,
+            dimension=dimension,
+            azure_api_key=azure_api_key,
+            api_version=api_version,
+            deployment=deployment,
+            max_retries=max_retries,
+            timeout=timeout,
+            batch_size=batch_size,
+            **kwargs,
+        )
+
+
 # TODO: Fix this in chonkie upstream
-# Register Azure OpenAI embeddings with pattern
+# Register our fixed Azure OpenAI embeddings with pattern
 # This automatically infers the following arguments from their corresponding environment variables if they are not provided:
 # - `api_key` from `AZURE_OPENAI_API_KEY`
 # - `organization` from `OPENAI_ORG_ID`
@@ -17,11 +56,11 @@ from rerankers import Reranker
 # - `azure_ad_token` from `AZURE_OPENAI_AD_TOKEN`
 # - `api_version` from `OPENAI_API_VERSION`
 # - `azure_endpoint` from `AZURE_OPENAI_ENDPOINT`
-EmbeddingsRegistry.register_provider("azure_openai", AzureOpenAIEmbeddings)
+EmbeddingsRegistry.register_provider("azure_openai", FixedAzureOpenAIEmbeddings)
-EmbeddingsRegistry.register_pattern(r"^text-embedding-", AzureOpenAIEmbeddings)
+EmbeddingsRegistry.register_pattern(r"^text-embedding-", FixedAzureOpenAIEmbeddings)
-EmbeddingsRegistry.register_model("text-embedding-ada-002", AzureOpenAIEmbeddings)
+EmbeddingsRegistry.register_model("text-embedding-ada-002", FixedAzureOpenAIEmbeddings)
-EmbeddingsRegistry.register_model("text-embedding-3-small", AzureOpenAIEmbeddings)
+EmbeddingsRegistry.register_model("text-embedding-3-small", FixedAzureOpenAIEmbeddings)
-EmbeddingsRegistry.register_model("text-embedding-3-large", AzureOpenAIEmbeddings)
+EmbeddingsRegistry.register_model("text-embedding-3-large", FixedAzureOpenAIEmbeddings)
 
 
 # Get the base directory of the project
@@ -83,7 +122,21 @@ class Config:
 
     # Chonkie Configuration | Edit this to your needs
     EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL")
-    embedding_model_instance = AutoEmbeddings.get_embeddings(EMBEDDING_MODEL)
+    # Azure OpenAI credentials from environment variables
+    AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
+    AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
+
+    # Pass Azure credentials to embeddings when using Azure OpenAI
+    embedding_kwargs = {}
+    if AZURE_OPENAI_ENDPOINT:
+        embedding_kwargs["azure_endpoint"] = AZURE_OPENAI_ENDPOINT
+    if AZURE_OPENAI_API_KEY:
+        embedding_kwargs["azure_api_key"] = AZURE_OPENAI_API_KEY
+
+    embedding_model_instance = AutoEmbeddings.get_embeddings(
+        EMBEDDING_MODEL,
+        **embedding_kwargs,
+    )
     chunker_instance = RecursiveChunker(
         chunk_size=getattr(embedding_model_instance, "max_seq_length", 512)
     )

surfsense_backend/app/services/page_limit_service.py

@@ -98,7 +98,7 @@ class PageLimitService:
 
         # Get user
         result = await self.session.execute(select(User).where(User.id == user_id))
-        user = result.scalar_one_or_none()
+        user = result.unique().scalar_one_or_none()
 
         if not user:
             raise ValueError(f"User with ID {user_id} not found")

surfsense_backend/scripts/docker/entrypoint.sh

@@ -19,7 +19,7 @@ backend_pid=$!
 sleep 5
 
 echo "Starting Celery Worker..."
-celery -A app.celery_app worker --loglevel=info --concurrency=1 --pool=solo &
+celery -A app.celery_app worker --loglevel=info &
 celery_worker_pid=$!
 
 # Wait a bit for worker to initialize