Update app.py

app.py

@@ -183,45 +183,44 @@ def self_knowledge(query: str):
     """
 
 def main(query: str, client: QdrantClient, collection_name: str, llm, dense_model: OptimumEncoder, sparse_model: SparseTextEmbedding, past_messages: str):
-    prompt = build_initial_prompt(query)
-    gen_text = outlines.generate.text(llm)
+    scored_points = query_hybrid_search(query, client, collection_name, dense_model, sparse_model).points
+        
+    docs = [(scored_point.payload['text'], scored_point.payload['metadata']) for scored_point in scored_points]
+    contents, metadatas = [list(t) for t in zip(*docs)]
+        
+    context = "\n".join(contents)
     
-    gen_choice = outlines.generate.choice(llm, choices=['Domain-Specific Question', 'General Question'])
+    gen_text = outlines.generate.text(llm)
+
+    gen_choice = outlines.generate.choice(llm, choices=['Yes', 'No'])
+    prompt = route_llm(context, 'Is the context relevant to the question ?')
     action = gen_choice(prompt, max_tokens=128, sampling_params=SamplingParams(temperature=0))
     print(f'Choice: {action}')
     
-    if action == 'General Question':
-        prompt = open_query_prompt(past_messages, query)
-        answer = gen_text(prompt, max_tokens=300, sampling_params=SamplingParams(temperature=0.3))
+    if action == 'Yes':
+        seen_values = set()
+        result_metadatas = "\n\n".join(
+            f'{value}'
+            for metadata in metadatas
+            for key, value in metadata.items()
+            if (value not in seen_values and not seen_values.add(value))
+        )
+
+        prompt = answer_with_context(context, query)
+        answer = gen_text(prompt, max_tokens=500, sampling_params=SamplingParams(temperature=0.3))
+        answer = f"{answer}\n\n\nSource(s) :\n\n{result_metadatas}"
+            
+        if not st.session_state.documents_only:
+            answer = f'Documents Based :\n\n{answer}'
     else:
-        scored_points = query_hybrid_search(query, client, collection_name, dense_model, sparse_model).points
-        
-        docs = [(scored_point.payload['text'], scored_point.payload['metadata']) for scored_point in scored_points]
-        contents, metadatas = [list(t) for t in zip(*docs)]
-        
-        context = "\n".join(contents)
-        
         torch.cuda.empty_cache()
-        print('HELLO')
-        gen_choice = outlines.generate.choice(llm, choices=['Yes', 'No'])
-        prompt = route_llm(context, 'Is the context relevant to the question ?')
+        gen_choice = outlines.generate.choice(llm, choices=['Domain-Specific Question', 'General Question'])
+        prompt = build_initial_prompt(query)
         action = gen_choice(prompt, max_tokens=128, sampling_params=SamplingParams(temperature=0))
-        print('HELLO3')
-        if action == 'Yes':
-            seen_values = set()
-            result_metadatas = "\n\n".join(
-                f'{value}'
-                for metadata in metadatas
-                for key, value in metadata.items()
-                if (value not in seen_values and not seen_values.add(value))
-            )
-
-            prompt = answer_with_context(context, query)
-            answer = gen_text(prompt, max_tokens=500, sampling_params=SamplingParams(temperature=0.3))
-            answer = f"{answer}\n\n\nSource(s) :\n\n{result_metadatas}"
-            
-            if not st.session_state.documents_only:
-                answer = f'Documents Based :\n\n{answer}'
+        
+        if action == 'General Question':
+            prompt = open_query_prompt(past_messages, query)
+            answer = gen_text(prompt, max_tokens=300, sampling_params=SamplingParams(temperature=0.3))
         else:
             if st.session_state.documents_only:
                 prompt = idk(query)