Update utils.py

utils.py

@@ -82,24 +82,21 @@ metadata_df = pd.merge(metadata_df, metadata_df2, how='inner')
 table_search = EmbeddingsSearch(metadata_df=metadata_df, emb_model=emb_model)
 
 def extract_question_type(llm, query):
+    sys_prompt = """
+        You are an AI assistant that determines if a user provided question can be answered from the given tables. 
+        The metadata of the tables are provided here - {}. \
+        If the question can be answered return yes.
+        If the question is a generic one and cannot be answered using these tables, return no.""".format(metadata_df[['table','metadata']].to_string())
+
     messages = [
-    (
-        "system",
-        """
-        You are an AI assistant that determines if a question seeks any specific information about any item or any table or a generic information of the database. \
-    Answer only "specific information" or "generic information".""",
-    ),
+    ("system", sys_prompt),
     ("human", query),
     ]
+    
     output = llm.invoke(messages)
     pred = output.content
     
-    if 'generic' in pred:
-        return 'generic'
-    elif 'specific' in pred:
-        return 'specific'
-    else:
-        return 'unknown'
+    return pred
 
 def extract_table_name(llm, query):
     messages = [