Update app.py

app.py

@@ -340,8 +340,8 @@ def rag_workflow(query):
     rewritten_query_response = llm.invoke(rewrite_prompt)
     rewritten_query = rewritten_query_response.content.strip()
     
-    print("A", library_usage_prediction)
-    print(rewritten_query)
+    print("Predicted library to search in", library_usage_prediction)
+    print("Rewritten query:", rewritten_query)
 
     kadi_apy_docs = vector_store.similarity_search(query, k=5, filter={"usage": "doc"})
     kadi_apy_sourcecode = vector_store.similarity_search(query, k=5, filter={"usage": library_usage_prediction})
@@ -352,11 +352,16 @@ def rag_workflow(query):
     
     print("H")
     print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
-    #for doc in kadi_apy_sourcecode:
-    #    print(doc.metadata.get("source", "Unknown Type"))
-     #   print("\n")
+    for doc in kadi_apy_sourcecode:
+        print(doc.metadata.get("source", "Unknown Type"))
+        print("\n")
     print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
 
+    print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
+    for doc in kadi_apy_docs:
+        print(doc.metadata.get("source", "Unknown Type"))
+        print("\n")
+    print("::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::")
 
 
     
@@ -421,8 +426,6 @@ def initialize():
     print(f"Total number of code_chunks: {len(code_chunks)}")
     print(f"Total number of doc_chunks: {len(doc_chunks)}")
 
-    #docstore = embed_documents_into_vectorstore(kadiAPY_code_chunks, EMBEDDING_MODEL_NAME, PERSIST_DOC_DIRECTORY)
-    #codestore = embed_documents_into_vectorstore(kadiAPY_doc_chunks, EMBEDDING_MODEL_NAME, PERSIST_CODE_DIRECTORY)
     filename = "test"
     vector_store = embed_documents_into_vectorstore(doc_chunks + code_chunks, EMBEDDING_MODEL_NAME, f"{DATA_DIR}/{filename}")
     llm = setup_llm(LLM_MODEL_NAME, LLM_TEMPERATURE, GROQ_API_KEY)