fix: embedding config not apply during document processing

frontend/src/components/settings/EmbeddingConfigForm.tsx

@@ -17,6 +17,8 @@ import { Label } from '@/components/ui/label';
 import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card';
 import { Badge } from '@/components/ui/badge';
 import { Alert, AlertDescription } from '@/components/ui/alert';
+import { Switch } from '@/components/ui/switch';
+import { Separator } from '@/components/ui/separator';
 import {
   Select,
   SelectContent,
@@ -153,6 +155,15 @@ export function EmbeddingConfigForm() {
     }));
   }, [currentProvider]);
 
+  // Handle active toggle
+  const handleToggle = useCallback((checked: boolean) => {
+    setFormState(prev => ({
+      ...prev,
+      isEnabled: checked,
+      hasChanges: true,
+    }));
+  }, []);
+
   // Handler for testing connection
   const handleTestConnection = useCallback(async () => {
     setFormState(prev => ({
@@ -433,6 +444,23 @@ export function EmbeddingConfigForm() {
           </div>
         )}
 
+        <Separator />
+
+        {/* Set as Active Toggle */}
+        <div className="flex items-center justify-between">
+          <div className="space-y-0.5">
+            <Label htmlFor="is-active">Set as active</Label>
+            <p className="text-sm text-muted-foreground">
+              Use this embedding provider for document processing
+            </p>
+          </div>
+          <Switch
+            id="is-active"
+            checked={formState.isEnabled}
+            onCheckedChange={handleToggle}
+          />
+        </div>
+
         {/* Action Buttons */}
         <div className="flex gap-2 pt-2">
           <Button

ragitect/api/v1/chat.py

@@ -30,8 +30,11 @@
 from ragitect.services.database.repositories.document_repo import DocumentRepository
 from ragitect.services.database.repositories.vector_repo import VectorRepository
 from ragitect.services.database.repositories.workspace_repo import WorkspaceRepository
-from ragitect.services.embedding import create_embeddings_model, embed_text
-from ragitect.services.llm_config_service import get_active_embedding_config
+from ragitect.services.embedding import (
+    create_embeddings_model,
+    embed_text,
+    get_embedding_model_from_config,
+)
 from ragitect.services.llm_factory import create_llm_with_provider
 
 
@@ -166,21 +169,8 @@ async def retrieve_context_with_graph(
     Returns:
         List of chunks with content and metadata
     """
-    # Get embedding configuration and create model
-    embedding_config = await get_active_embedding_config(session)
-
-    if embedding_config:
-        config = EmbeddingConfig(
-            provider=embedding_config.provider_name,
-            model=embedding_config.model_name or "nomic-embed-text",
-            base_url=embedding_config.config_data.get("base_url"),
-            api_key=embedding_config.config_data.get("api_key"),
-            dimension=embedding_config.config_data.get("dimension", 768),
-        )
-    else:
-        config = EmbeddingConfig()
-
-    embedding_model = create_embeddings_model(config)
+    # Get embedding model from active DB config (or defaults)
+    embedding_model = await get_embedding_model_from_config(session)
 
     # Create async embedding function for graph
     async def embed_fn(text: str) -> list[float]:
@@ -373,22 +363,8 @@ async def chat_stream(
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
 
-    # Get active embedding configuration and model
-    embed_config_dto = await get_active_embedding_config(session)
-    if embed_config_dto is None:
-        # Use default config if no active config found
-        embed_config = EmbeddingConfig()
-    else:
-        # Convert DTO to EmbeddingConfig
-        embed_config = EmbeddingConfig(
-            provider=embed_config_dto.provider_name,
-            model=embed_config_dto.model_name or "all-MiniLM-L6-v2",
-            api_key=embed_config_dto.api_key,
-            base_url=embed_config_dto.base_url,
-            dimension=embed_config_dto.dimension,
-        )
-
-    embed_model = create_embeddings_model(embed_config)
+    # Get embedding model from active DB config (or defaults)
+    embed_model = await get_embedding_model_from_config(session)
 
     async def embed_fn(text: str) -> list[float]:
         """Embedding function for runtime dependency injection."""

ragitect/services/document_processing_service.py

@@ -24,7 +24,7 @@
 from ragitect.services.config import load_document_config
 from ragitect.services.database.repositories.document_repo import DocumentRepository
 from ragitect.services.document_processor import process_file_bytes, split_document
-from ragitect.services.embedding import create_embeddings_model, embed_documents
+from ragitect.services.embedding import embed_documents, get_embedding_model_from_config
 
 logger = logging.getLogger(__name__)
 
@@ -135,7 +135,7 @@ async def process_document(self, document_id: UUID) -> None:
             # Generate embeddings if there are chunks
             if chunks:
                 try:
-                    embedding_model = create_embeddings_model()
+                    embedding_model = await get_embedding_model_from_config(self.session)
                     embeddings = await embed_documents(embedding_model, chunks)
                     logger.info(
                         f"Generated {len(embeddings)} embeddings for document {document_id}"

ragitect/services/embedding.py

@@ -7,14 +7,17 @@
 """
 
 import logging
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import httpx
 from langchain_core.embeddings import Embeddings
 from langchain_ollama import OllamaEmbeddings
 
 from ragitect.services.config import EmbeddingConfig
 
+if TYPE_CHECKING:
+    from sqlalchemy.ext.asyncio import AsyncSession
+
 logger = logging.getLogger(__name__)
 
 
@@ -307,3 +310,47 @@ def get_embedding_dimension(config: EmbeddingConfig | None = None) -> int:
     if config is None:
         return 768  # Default for nomic-embed-text
     return config.dimension
+
+
+async def get_embedding_model_from_config(session: "AsyncSession") -> Embeddings:
+    """Get embedding model using active DB config, fallback to env vars, then defaults.
+
+    Priority order:
+    1. Active embedding config from database (UI-configured)
+    2. Environment variables (EMBEDDING_PROVIDER, EMBEDDING_API_KEY, etc.)
+    3. Hardcoded defaults (Ollama)
+
+    This is the DRY helper that encapsulates embedding config lookup, avoiding
+    duplicate patterns across chat.py and document_processing_service.py.
+
+    Args:
+        session: Database session for config lookup
+
+    Returns:
+        Embeddings model configured from DB, env vars, or defaults
+    """
+    # Import inside function to avoid circular imports
+    from ragitect.services.llm_config_service import get_active_embedding_config
+    from ragitect.services.config import load_embedding_config
+
+    embedding_config = await get_active_embedding_config(session)
+    if embedding_config:
+        logger.info(
+            f"Using embedding config from DB: provider={embedding_config.provider_name}, "
+            f"model={embedding_config.model_name}"
+        )
+        config = EmbeddingConfig(
+            provider=embedding_config.provider_name,
+            model=embedding_config.model_name or "qwen3-embedding:0.6b",
+            base_url=embedding_config.base_url,
+            api_key=embedding_config.api_key,
+            dimension=embedding_config.dimension,
+        )
+    else:
+        # Fall back to environment variables
+        config = load_embedding_config()
+        logger.info(
+            f"No active embedding config in DB, using env vars: "
+            f"provider={config.provider}, model={config.model}"
+        )
+    return create_embeddings_model(config)

tests/api/v1/test_chat_streaming.py

@@ -32,23 +32,10 @@ def setup_langgraph_streaming_mocks(mocker):
     Returns:
         dict with mock objects for assertions
     """
-    # Mock embedding config - required for both retrieval and streaming
-    mock_embed_config = mocker.MagicMock()
-    mock_embed_config.provider_name = "ollama"
-    mock_embed_config.model_name = "all-MiniLM-L6-v2"
-    mock_embed_config.api_key = None
-    mock_embed_config.base_url = None
-    mock_embed_config.dimension = 768
-
-    mocker.patch(
-        "ragitect.api.v1.chat.get_active_embedding_config",
-        return_value=mock_embed_config,
-    )
-
-    # Mock embedding model and embed function
+    # Mock embedding model - now using the DRY helper
     mock_embed_model = mocker.MagicMock()
     mocker.patch(
-        "ragitect.api.v1.chat.create_embeddings_model",
+        "ragitect.api.v1.chat.get_embedding_model_from_config",
         return_value=mock_embed_model,
     )
 
@@ -96,7 +83,6 @@ async def mock_embed_fn(model, text: str):
     )
 
     return {
-        "embed_config": mock_embed_config,
         "embed_model": mock_embed_model,
         "vector_repo": mock_vector_repo,
         "llm": mock_llm,

tests/services/test_document_processing_service.py

@@ -104,8 +104,8 @@ async def test_process_document_success(
                 mock_model = MagicMock()
                 mock_model.aembed_documents = AsyncMock(return_value=[[0.1] * 768])
                 with patch(
-                    "ragitect.services.document_processing_service.create_embeddings_model",
-                    return_value=mock_model,
+                    "ragitect.services.document_processing_service.get_embedding_model_from_config",
+                    new=AsyncMock(return_value=mock_model),
                 ):
                     with patch(
                         "ragitect.services.document_processing_service.embed_documents",
@@ -247,8 +247,11 @@ async def test_process_document_integration_with_repo_methods(
                 "ragitect.services.document_processing_service.split_document",
                 return_value=["Chunk"],
             ):
+                mock_model = MagicMock()
+                mock_model.aembed_documents = AsyncMock(return_value=[[0.1] * 768])
                 with patch(
-                    "ragitect.services.document_processing_service.create_embeddings_model"
+                    "ragitect.services.document_processing_service.get_embedding_model_from_config",
+                    new=AsyncMock(return_value=mock_model),
                 ):
                     with patch(
                         "ragitect.services.document_processing_service.embed_documents",