Update app.py

app.py

@@ -56,7 +56,7 @@ def retrieve_thoughts(query, ):
     chunks_1 = tier_1.groupby(['title', 'url', ]).apply(lambda x: "\n...\n".join(x.sort_values('score').iloc[:3].sort_values('id')['page_content'].values)).values
     score = tier_1.groupby(['title', 'url', ]).apply(lambda x: x.sort_values('score').iloc[:3]['score'].mean()).values
 
-    tier_1_adjusted = tier_1.groupby(['title', 'url', ]).first().reset_index()[[ 'title', 'url']]
+    tier_1_adjusted = tier_1.groupby(['title', 'url', ]).first().reset_index()[[ 'title', 'url', 'author']]
     tier_1_adjusted['content'] = chunks_1
     tier_1_adjusted['score'] = score
 
@@ -77,7 +77,7 @@ def get_references(query):
   thoughts = retrieve_thoughts(query)
   print(thoughts.keys())
   tier_1 = thoughts['tier 1']
-  reference = tier_1[['ref', 'url', 'title']].to_dict('records')
+  reference = tier_1[['ref', 'url', 'title', 'author']].to_dict('records')
   return reference
 def qa_themes(query,):
 
@@ -98,9 +98,9 @@ def qa_themes(query,):
     tier_1 = thoughts['tier 1']
     tier_2 = thoughts['tier 2']
 
-    reference = tier_1[['ref', 'url', 'title']].to_dict('records')
+    reference = tier_1[['ref', 'url', 'title', 'author']].to_dict('records')
 
-    tier_1 = list(tier_1.apply(lambda x: f"[{int(x['ref'])}] title: {x['title']}\n Content: {x.content}", axis = 1).values)
+    tier_1 = list(tier_1.apply(lambda x: f"[{int(x['ref'])}] title: {x['title']}\n author: {x.author}\n content: {x.content}", axis = 1).values)
     tier_2 = list(tier_2.apply(lambda x: f"title: {x['title']}\n Content: {x.content}", axis = 1).values)
     print(f"QUERY: {query}\nTIER 1: {tier_1}\nTIER2: {tier_2}")
 
@@ -164,7 +164,7 @@ def qa_retrieve(query,):
 
     reference = tier_1[['ref', 'url', 'title']].to_dict('records')
 
-    tier_1 = list(tier_1.apply(lambda x: f"[{int(x['ref'])}] title: {x['title']}\n Content: {x.content}", axis = 1).values)
+    tier_1 = list(tier_1.apply(lambda x: f"[{int(x['ref'])}] title: {x['title']}\n author: {x.author}\n content: {x.content}", axis = 1).values)
     tier_2 = list(tier_2.apply(lambda x: f"title: {x['title']}\n Content: {x.content}", axis = 1).values)
     print(f"QUERY: {query}\nTIER 1: {tier_1}\nTIER2: {tier_2}")
 
@@ -226,7 +226,7 @@ def qa_intro(query, cynthesis, tier_1,):
 
   llm = ClaudeLLM()
   llm_4 = ChatOpenAI(
-              temperature=0,
+              temperature=0.5,
               model='gpt-3.5-turbo-16k'
           )
 
@@ -235,7 +235,7 @@ def qa_intro(query, cynthesis, tier_1,):
   prompt = PromptTemplate(
           input_variables=["query", "cynthesis", "articles"],
           template="""
-          Give me an introduction to the following consensus without repeating how it starts. Consider this an abstract. And after finishing the introduction, pick one quote from the sources given below.
+          Give me an introduction to the following consensus without repeating how it starts. Consider what you will write an abstract. And after finishing the introduction, pick one quote from the sources given below.
 
           query: {query}
           Here's the consensus: {cynthesis}