fix document processing embedding model bug

src/config/settings.py

@@ -595,3 +595,8 @@ def get_knowledge_config():
 def get_agent_config():
     """Get agent configuration."""
     return get_openrag_config().agent
+
+
+def get_embedding_model() -> str:
+    """Return the currently configured embedding model."""
+    return get_openrag_config().knowledge.embedding_model

src/main.py

@@ -53,11 +53,11 @@ from auth_middleware import optional_auth, require_auth
 # Configuration and setup
 from config.settings import (
     DISABLE_INGEST_WITH_LANGFLOW,
-    EMBED_MODEL,
     INDEX_BODY,
     INDEX_NAME,
     SESSION_SECRET,
     clients,
+    get_embedding_model,
     is_no_auth_mode,
     get_openrag_config,
 )
@@ -505,7 +505,7 @@ async def initialize_services():
     openrag_connector_service = ConnectorService(
         patched_async_client=clients.patched_async_client,
         process_pool=process_pool,
-        embed_model=EMBED_MODEL,
+        embed_model=get_embedding_model(),
         index_name=INDEX_NAME,
         task_service=task_service,
         session_manager=session_manager,
@@ -1228,4 +1228,4 @@ if __name__ == "__main__":
         host="0.0.0.0",
         port=8000,
         reload=False,  # Disable reload since we're running from main
-    )
+    )

src/models/processors.py

@@ -163,16 +163,17 @@ class TaskProcessor:
         docling conversion + embeddings + OpenSearch indexing.
 
         Args:
-            embedding_model: Embedding model to use (defaults to EMBED_MODEL from settings)
+            embedding_model: Embedding model to use (defaults to the current
+                embedding model from settings)
         """
         import datetime
-        from config.settings import INDEX_NAME, EMBED_MODEL, clients
+        from config.settings import INDEX_NAME, clients, get_embedding_model
         from services.document_service import chunk_texts_for_embeddings
         from utils.document_processing import extract_relevant
         from utils.embedding_fields import get_embedding_field_name, ensure_embedding_field_exists
 
         # Use provided embedding model or fall back to default
-        embedding_model = embedding_model or EMBED_MODEL
+        embedding_model = embedding_model or get_embedding_model()
 
         # Get user's OpenSearch client with JWT for OIDC auth
         opensearch_client = self.document_service.session_manager.get_user_opensearch_client(
@@ -575,7 +576,7 @@ class S3FileProcessor(TaskProcessor):
         import time
         import asyncio
         import datetime
-        from config.settings import INDEX_NAME, EMBED_MODEL, clients
+        from config.settings import INDEX_NAME, clients, get_embedding_model
         from services.document_service import chunk_texts_for_embeddings
         from utils.document_processing import process_document_sync
 

src/services/document_service.py

@@ -12,12 +12,13 @@ from utils.logging_config import get_logger
 
 logger = get_logger(__name__)
 
-from config.settings import clients, INDEX_NAME, EMBED_MODEL
+from config.settings import clients, INDEX_NAME, get_embedding_model
 from utils.document_processing import extract_relevant, process_document_sync
 
 
-def get_token_count(text: str, model: str = EMBED_MODEL) -> int:
+def get_token_count(text: str, model: str = None) -> int:
     """Get accurate token count using tiktoken"""
+    model = model or get_embedding_model()
     try:
         encoding = tiktoken.encoding_for_model(model)
         return len(encoding.encode(text))
@@ -28,12 +29,14 @@ def get_token_count(text: str, model: str = EMBED_MODEL) -> int:
 
 
 def chunk_texts_for_embeddings(
-    texts: List[str], max_tokens: int = None, model: str = EMBED_MODEL
+    texts: List[str], max_tokens: int = None, model: str = None
 ) -> List[List[str]]:
     """
     Split texts into batches that won't exceed token limits.
     If max_tokens is None, returns texts as single batch (no splitting).
     """
+    model = model or get_embedding_model()
+
     if max_tokens is None:
         return [texts]
 

src/services/search_service.py

@@ -1,7 +1,7 @@
 import copy
 from typing import Any, Dict
 from agentd.tool_decorator import tool
-from config.settings import clients, INDEX_NAME, EMBED_MODEL
+from config.settings import clients, INDEX_NAME, get_embedding_model
 from auth_context import get_auth_context
 from utils.logging_config import get_logger
 
@@ -24,17 +24,18 @@ class SearchService:
         Args:
             query (str): query string to search the corpus
             embedding_model (str): Optional override for embedding model.
-                                  If not provided, uses EMBED_MODEL from config.
+                                  If not provided, uses the current embedding
+                                  model from configuration.
 
         Returns:
             dict (str, Any): {"results": [chunks]} on success
         """
         from utils.embedding_fields import get_embedding_field_name
 
-        # Strategy: Use provided model, or default to EMBED_MODEL
-        # This assumes documents are embedded with EMBED_MODEL by default
+        # Strategy: Use provided model, or default to the configured embedding
+        # model. This assumes documents are embedded with that model by default.
         # Future enhancement: Could auto-detect available models in corpus
-        embedding_model = embedding_model or EMBED_MODEL
+        embedding_model = embedding_model or get_embedding_model()
         embedding_field_name = get_embedding_field_name(embedding_model)
 
         logger.info(
@@ -451,7 +452,8 @@ class SearchService:
         """Public search method for API endpoints
 
         Args:
-            embedding_model: Embedding model to use for search (defaults to EMBED_MODEL)
+            embedding_model: Embedding model to use for search (defaults to the
+                currently configured embedding model)
         """
         # Set auth context if provided (for direct API calls)
         from config.settings import is_no_auth_mode