Update manabUtils.py

manabUtils.py

@@ -44,16 +44,35 @@ def retrieve_chunks_GPC():
     Retreive chunks from HF dataset for GPC
     """    
     embedding_model="sentence-transformers/all-MiniLM-L6-v2"
+    repo_id="manabb/NRLGPC"
     
     try:
-        embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
-        vectorstore = FAISS.load_local(
-            folder_path="hf://manabb/NRLGPC/faiss_gpc_goods_merged",
-            embeddings=embeddings,
-            allow_dangerous_deserialization=True  # Required for .pkl
-        )      
-        retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+      # Step 1: Create embeddings (FIX: was missing)
+      embeddings = HuggingFaceEmbeddings(model_name=embedding_model)
+
+      # Step 2: Download FAISS files from HF Hub
+      faiss_path = hf_hub_download(
+          repo_id=repo_id,
+          filename="faiss_gpc_goods_merged/index.faiss",
+          repo_type="dataset"
+      )
+      pkl_path = hf_hub_download(
+          repo_id=repo_id,
+          filename="faiss_gpc_goods_merged/index.pkl",
+          repo_type="dataset"
+      )
+
+      # Step 3: Load FAISS vectorstore (FIX: pass embeddings object, not string)
+      folder_path = os.path.dirname(faiss_path)
+      vectorstore = FAISS.load_local(
+          folder_path=folder_path,
+          embeddings=embeddings,  # FIXED: was 'embedding_model' string
+          allow_dangerous_deserialization=True
+      )
+
+      # Step 4: Create retriever
+      retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
     except Exception as e:
-        print(f"Error in generate_qa_chain: {e}")
-        return None
+      print(f"Error in generate_qa_chain: {e}")
+      return None
     return retriever