Update src/rag_engine.py

src/rag_engine.py

@@ -123,8 +123,12 @@ def search_knowledge_base(query: str, username: str, index_name: str, embed_mode
         logger.error(f"Search failed: {e}")
         return []
 
-def process_and_add_text(text: str, source_name: str, username: str, index_name: str) -> Tuple[bool, str]:
+def process_and_add_text(text: str, source_name: str, username: str, index_name: str, embed_model_name: str = None) -> Tuple[bool, str]:
+    """
+    Ingests raw text with a specific embedding model.
+    """
     if not PINECONE_KEY or not index_name: return False, "Pinecone Configuration Missing."
+    
     try:
         pm = PineconeManager(PINECONE_KEY)
         
@@ -135,12 +139,18 @@ def process_and_add_text(text: str, source_name: str, username: str, index_name:
         user_docs_dir = os.path.join(UPLOAD_DIR, username)
         os.makedirs(user_docs_dir, exist_ok=True)
         backup_path = os.path.join(user_docs_dir, source_name)
+        
         with open(backup_path, "w", encoding='utf-8') as f:
             f.write(text)
 
-        # 3. UPLOAD
-        emb_fn = get_embedding_func()
-        doc = Document(page_content=text, metadata={"source": source_name, "strategy": "flattened", "file_type": "generated"})
+        # 3. UPLOAD (The Fix: Use the passed model name)
+        emb_fn = get_embedding_func(embed_model_name) 
+        
+        doc = Document(
+            page_content=text,
+            metadata={"source": source_name, "strategy": "flattened", "file_type": "generated"}
+        )
+        
         vstore = pm.get_vectorstore(index_name, emb_fn, namespace=username)
         vstore.add_documents([doc], ids=[f"{source_name}_0"])