Update app.py

app.py

@@ -103,13 +103,20 @@ def rag_workflow(query):
     # Assume 'llm' and 'vector_store' are already initialized instances
     rag_chain = RAGChain(llm, vector_store)
 
-    # Step 1: Predict which library usage is relevant
+
+    """
+    Pre-Retrieval-Stage  
+    """
     # predict which python library to search in: (standard) kadiAPY-library or kadiAPY-cli-library 
     code_library_usage_prediction = rag_chain.predict_library_usage(query)
     print(f"Predicted library usage: {code_library_usage_prediction}")
-
     
-    # Step 2: Retrieve contexts (documents and code snippets)   
+    rewritten query = rag_chain.rewrite_query(self, query)
+    print(f"Rewritten query: {code_library_usage_prediction}"))
+
+    """
+    Retrieval-Stage
+    """  
     kadiAPY_doc_documents = rag_chain.retrieve_contexts(query, k = 3, {"usage: doc"})  
     kadiAPY_code_documents =rag_chain.retrieve_contexts(rewritten_query, k = 3, {"usage": library_usage_prediction} )
     
@@ -118,12 +125,18 @@ def rag_workflow(query):
 
 
     
-    
+    """
+    Pre-Generation-Stage
+    Adding each doc's metadata to the retrieved content (docs & code snippets) 
+    """
     formatted_doc_snippets = rag_chain.format_documents(kadiAPY_doc_documents)
     formatted_code_snippets = rag_chain.format_documents(kadiAPY_code_documents)
     #print("FORMATTED Retrieved Document Contexts:", formatted_doc_snippets)
     #print("FORMATTED Retrieved Code Contexts:" , formatted_code_snippets)
-    # Step 4: Generate the final response
+
+    """
+    Generation-Stage
+    """
     response = rag_chain.generate_response(query, formatted_doc_snippets, formatted_code_snippets)
     print("Generated Response:", response)