diff --git a/trustgraph-flow/trustgraph/extract/kg/ontology/extract.py b/trustgraph-flow/trustgraph/extract/kg/ontology/extract.py
index 764600dc..7e474855 100644
--- a/trustgraph-flow/trustgraph/extract/kg/ontology/extract.py
+++ b/trustgraph-flow/trustgraph/extract/kg/ontology/extract.py
@@ -93,7 +93,8 @@ class Processor(FlowProcessor):
         """Initialize per-flow OntoRAG components.
 
         Each flow gets its own vector store and embedder to support
-        different embedding models across flows.
+        different embedding models across flows. The vector store dimension
+        is auto-detected from the embeddings service.
 
         Args:
             flow: Flow object for this processing context
@@ -110,15 +111,22 @@ class Processor(FlowProcessor):
         try:
             logger.info(f"Initializing components for flow {flow_id}")
 
-            # Initialize vector store (FAISS only, no fallback)
-            vector_store = InMemoryVectorStore(
-                dimension=1536,  # text-embedding-3-small
-                index_type='flat'
-            )
-
             # Use embeddings client directly (no wrapper needed)
             embeddings_client = flow("embeddings-request")
 
+            # Detect embedding dimension by embedding a test string
+            logger.info("Detecting embedding dimension from embeddings service...")
+            test_embedding_response = await embeddings_client.embed("test")
+            test_embedding = test_embedding_response[0]  # Extract from [[vector]]
+            dimension = len(test_embedding)
+            logger.info(f"Detected embedding dimension: {dimension}")
+
+            # Initialize vector store with detected dimension
+            vector_store = InMemoryVectorStore(
+                dimension=dimension,
+                index_type='flat'
+            )
+
             ontology_embedder = OntologyEmbedder(
                 embedding_service=embeddings_client,
                 vector_store=vector_store
@@ -143,10 +151,11 @@ class Processor(FlowProcessor):
             self.flow_components[flow_id] = {
                 'embedder': ontology_embedder,
                 'vector_store': vector_store,
-                'selector': ontology_selector
+                'selector': ontology_selector,
+                'dimension': dimension
             }
 
-            logger.info(f"Flow {flow_id} components initialized successfully")
+            logger.info(f"Flow {flow_id} components initialized successfully (dimension={dimension})")
             return flow_id
 
         except Exception as e:
diff --git a/trustgraph-flow/trustgraph/extract/kg/ontology/ontology_embedder.py b/trustgraph-flow/trustgraph/extract/kg/ontology/ontology_embedder.py
index 6430a0a7..8eee76b4 100644
--- a/trustgraph-flow/trustgraph/extract/kg/ontology/ontology_embedder.py
+++ b/trustgraph-flow/trustgraph/extract/kg/ontology/ontology_embedder.py
@@ -164,6 +164,9 @@ class OntologyEmbedder:
                 # Convert to numpy array
                 embeddings = np.array(embeddings_list)
 
+                # Log embedding shape for debugging
+                logger.debug(f"Embeddings shape: {embeddings.shape}, expected: ({len(batch)}, {self.vector_store.dimension})")
+
                 # Store in vector store
                 ids = [elem['id'] for elem in batch]
                 metadata_list = [elem['metadata'] for elem in batch]
@@ -174,7 +177,7 @@ class OntologyEmbedder:
                 logger.debug(f"Embedded batch of {len(batch)} elements from ontology {ontology.id}")
 
             except Exception as e:
-                logger.error(f"Failed to embed batch for ontology {ontology.id}: {e}")
+                logger.error(f"Failed to embed batch for ontology {ontology.id}: {e}", exc_info=True)
 
         self.embedded_ontologies.add(ontology.id)
         logger.info(f"Embedded {embedded_count} elements from ontology {ontology.id}")