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_response(self, query, chat_history, doc_context, code_context):
+    def generate_response2(self, query, chat_history, doc_context, code_context):
         """
         Generate a response using the retrieved contexts and the LLM.
         """     
@@ -170,7 +170,37 @@ 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 = []