Update app.py

app.py

@@ -45,12 +45,15 @@ text_generator_pipeline = pipeline(
 # Wrap it as a LangChain LLM
 llm = HuggingFacePipeline(pipeline=text_generator_pipeline)
 
+# Re-declare embedding function
+embed_fn = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
 # Create vectorstore and retriever
 vectorstore_faiss = LangChainFAISS(
     index=faiss_index,
     docstore=docstore,
     index_to_docstore_id=id_map,
-    embedding_function=None # Not needed for retrieval only
+    embedding_function=embed_fn # Not needed for retrieval only
 )
 
 # Create a retriever that returns top-k most relevant chunks