Fix: Sửa lỗi syntax embeddings + reranker trong bản Docker backend

backend/hue_portal/core/embeddings.py

@@ -70,27 +70,27 @@ class EmbeddingModelManager:
         model_name: Optional[str] = None,
         force_reload: bool = False,
     ) -> Optional[SentenceTransformer]:
-    """
+        """
         Get or load embedding model instance with thread-safe caching.
-    
-    Args:
+        
+        Args:
             model_name: Name of the model to load.
-        force_reload: Force reload model even if cached.
-    
-    Returns:
-        SentenceTransformer instance or None if not available.
-    """
-    if not SENTENCE_TRANSFORMERS_AVAILABLE:
+            force_reload: Force reload model even if cached.
+        
+        Returns:
+            SentenceTransformer instance or None if not available.
+        """
+        if not SENTENCE_TRANSFORMERS_AVAILABLE:
             print(
                 "Warning: sentence-transformers not installed. "
                 "Install with: pip install sentence-transformers"
             )
-        return None
-    
-    resolved_model_name = model_name or DEFAULT_MODEL_NAME
-    if resolved_model_name in AVAILABLE_MODELS:
-        resolved_model_name = AVAILABLE_MODELS[resolved_model_name]
-    
+            return None
+        
+        resolved_model_name = model_name or DEFAULT_MODEL_NAME
+        if resolved_model_name in AVAILABLE_MODELS:
+            resolved_model_name = AVAILABLE_MODELS[resolved_model_name]
+        
         if (
             not force_reload
             and self._model is not None
@@ -110,40 +110,40 @@ class EmbeddingModelManager:
     
     def _load_model(self, resolved_model_name: str) -> Optional[SentenceTransformer]:
         """Internal method to load model (must be called with lock held)."""
-    try:
-        print(f"Loading embedding model: {resolved_model_name}")
-        
-        model_path = Path(resolved_model_name)
-        if model_path.exists() and model_path.is_dir():
-            print(f"Loading local model from: {resolved_model_name}")
+        try:
+            print(f"Loading embedding model: {resolved_model_name}")
+            
+            model_path = Path(resolved_model_name)
+            if model_path.exists() and model_path.is_dir():
+                print(f"Loading local model from: {resolved_model_name}")
                 self._model = SentenceTransformer(str(model_path))
-        else:
-            hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
-            model_kwargs = {}
-            if hf_token:
-                print(f"Using Hugging Face token for model: {resolved_model_name}")
-                model_kwargs["token"] = hf_token
+            else:
+                hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
+                model_kwargs = {}
+                if hf_token:
+                    print(f"Using Hugging Face token for model: {resolved_model_name}")
+                    model_kwargs["token"] = hf_token
                 self._model = SentenceTransformer(resolved_model_name, **model_kwargs)
             
             self._model_name = resolved_model_name
-        
-        try:
+            
+            try:
                 test_embedding = self._model.encode("test", show_progress_bar=False)
-            dim = len(test_embedding)
-            print(f"✅ Successfully loaded model: {resolved_model_name} (dimension: {dim})")
-        except Exception:
-            print(f"✅ Successfully loaded model: {resolved_model_name}")
+                dim = len(test_embedding)
+                print(f"✅ Successfully loaded model: {resolved_model_name} (dimension: {dim})")
+            except Exception:
+                print(f"✅ Successfully loaded model: {resolved_model_name}")
             
             return self._model
         except Exception as exc:
             print(f"❌ Error loading model {resolved_model_name}: {exc}")
-        if resolved_model_name != FALLBACK_MODEL_NAME:
-            print(f"Trying fallback model: {FALLBACK_MODEL_NAME}")
-            try:
+            if resolved_model_name != FALLBACK_MODEL_NAME:
+                print(f"Trying fallback model: {FALLBACK_MODEL_NAME}")
+                try:
                     self._model = SentenceTransformer(FALLBACK_MODEL_NAME)
                     self._model_name = FALLBACK_MODEL_NAME
                     test_embedding = self._model.encode("test", show_progress_bar=False)
-                dim = len(test_embedding)
+                    dim = len(test_embedding)
                     print(
                         f"✅ Successfully loaded fallback model: {FALLBACK_MODEL_NAME} "
                         f"(dimension: {dim})"

backend/hue_portal/core/reranker.py

@@ -149,19 +149,19 @@ def rerank_documents(
         # Rerank using cross-encoder
         print(f"[RERANKER] Reranking {len(pairs)} documents...", flush=True)
         logger.debug("[RERANKER] Reranking %d documents", len(pairs))
-        
+
         # Handle different reranker types
         from FlagEmbedding import FlagReranker
         from sentence_transformers import CrossEncoder
-        
+
         if isinstance(reranker, FlagReranker):
-        # FlagReranker.compute_score returns list of scores for multiple pairs
-        scores = reranker.compute_score(pairs, normalize=True)
-        
-        # Handle both single score (float) and list of scores
-        if isinstance(scores, (int, float)):
-            scored_docs = [(doc_objects[0], float(scores))]
-        elif isinstance(scores, list):
+            # FlagReranker.compute_score returns list of scores for multiple pairs
+            scores = reranker.compute_score(pairs, normalize=True)
+
+            # Handle both single score (float) and list of scores
+            if isinstance(scores, (int, float)):
+                scored_docs = [(doc_objects[0], float(scores))]
+            elif isinstance(scores, list):
                 scored_docs = list(zip(doc_objects, scores))
             else:
                 logger.warning("[RERANKER] Unexpected score type: %s", type(scores))
@@ -169,7 +169,7 @@ def rerank_documents(
         elif isinstance(reranker, CrossEncoder):
             # CrossEncoder.predict returns numpy array
             scores = reranker.predict(pairs)
-            if hasattr(scores, 'tolist'):
+            if hasattr(scores, "tolist"):
                 scores = scores.tolist()
             elif not isinstance(scores, list):
                 scores = [float(scores)] if len(pairs) == 1 else list(scores)