Update rag_pipeline.py

rag_pipeline.py

@@ -15,7 +15,7 @@ class RAGPipeline:
         self.model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
 
         self.chunks = []
-        self.embeddings = []
+        self.embeddings = None
 
         print("[RAG] تم تحميل النماذج بنجاح.")
 
@@ -24,7 +24,7 @@ class RAGPipeline:
         self.embeddings = self.embedder.encode(chunks, convert_to_numpy=True)
 
     def retrieve_passages(self, question, top_k=5):
-        if not self.embeddings or not self.chunks:
+        if self.embeddings is None or len(self.chunks) == 0:
             return []
         question_embedding = self.embedder.encode([question], convert_to_numpy=True)
         similarities = np.dot(self.embeddings, question_embedding.T).squeeze()