Update kadiApy_ragchain.py

kadiApy_ragchain.py

@@ -134,7 +134,7 @@ class KadiApyRagchain:
         context = self.vector_store.similarity_search(query = query, k=k, filter=filter)       
         return context
 
-    def generate_response2(self, query, chat_history, doc_context, code_context):
+    def generate_response(self, query, chat_history, doc_context, code_context):
         """
         Generate a response using the retrieved contexts and the LLM.
         """     
@@ -170,37 +170,6 @@ class KadiApyRagchain:
         """
         return self.llm.invoke(prompt).content
 
-    def generate_response(self, query, chat_history, doc_context, code_context):
-        """
-        Generate a response using the retrieved contexts and the LLM.
-        """     
-        
-        # Update the prompt with history included
-        prompt = f"""
-            You are a Python programming assistant specialized in the "Kadi-APY" library. 
-            The "Kadi-APY" library is a Python package designed to facilitate interaction with the REST-like API of a software platform called Kadi4Mat. 
-            Your task is to answer the user's query based on the guidelines, and if needed, combine understanding provided by 
-            "Document Snippets" with the implementation details provided by "Code Snippets." 
-    
-            Guidelines if generating code:
-                - Display the complete code first, followed by a concise explanation in no more than 5 sentences.
-                
-            General Guidelines:
-                - Refer to the chat history if it provides context that could enhance your understanding of the user's query.
-                - Always include the chat history if relevant to the user's query for continuity and clarity in responses.
-                - If the user's query cannot be fulfilled based on the provided snippets, reply with "The API does not support the requested functionality."
-                - If the user's query does not implicate any task, reply with a question asking the user to elaborate.
-    
-            Document Snippets:
-            {doc_context}
-    
-            Code Snippets:
-            {code_context}
-        
-            Query:
-            {query}
-        """
-        return self.llm.invoke(prompt).content
     
     def format_documents(self, documents):
         formatted_docs = []