JWT-update_4

app.py

@@ -79,6 +79,7 @@ def chat(
     # Use token from request if provided, fallback to sandbox
     access_token = request.access_token if request.access_token else SANDBOX_ACCESS_TOKEN
     financial_context = get_financial_snapshot(access_token)
+    print(f"Financial context preview: {financial_context[:100]}")
 
     answer = get_answer(
         message=request.message,

fiscal.py

@@ -142,9 +142,16 @@ def clear_memory(user_id: str) -> None:
 
 def stream_answer(message: str, user_id: str, financial_context: str = "") -> Generator:
     """Stream answer token by token."""
+    
+    # Same error detection as get_answer
+    if not financial_context or financial_context == "NO_BANK_DATA" or "Unable to fetch" in financial_context or "Bad Request" in financial_context or "access token" in financial_context.lower():
+        context_block = "The user has not connected their bank account yet. If relevant, gently mention they can connect their bank account in the Options menu to get personalized advice based on their real transactions and balances."
+    else:
+        context_block = f"The user has connected their bank account. Here is their live financial data:\n{financial_context}"
+
     filled_template = QA_TEMPLATE.replace(
         "{financial_context}",
-        financial_context or "No bank data available."
+        context_block
     )
 
     prompt = PromptTemplate(
@@ -152,14 +159,10 @@ def stream_answer(message: str, user_id: str, financial_context: str = "") -> Ge
         input_variables=["context", "question"],
     )
 
-    # Get relevant docs manually for streaming
-    docs = retriever.get_relevant_documents(message)
+    docs = retriever.invoke(message)
     context = "\n\n".join([doc.page_content for doc in docs])
-
-    # Build the full prompt
     filled_prompt = prompt.format(context=context, question=message)
 
-    # Stream directly from the LLM
     for chunk in llm.stream(filled_prompt):
         if hasattr(chunk, 'content'):
             yield chunk.content