Update kadiApy_ragchain.py

kadiApy_ragchain.py

@@ -25,9 +25,9 @@ class KadiApyRagchain:
         
         # Retrieve contexts
         print("Start retrieving:")
-        doc_contexts = self.retrieve_contexts(query, k=2, filter={"dataset_category": "kadi_apy_docs"})
-        code_contexts = self.retrieve_contexts(rewritten_query, k=3, filter={"usage": code_library_usage_prediction})
-
+        #doc_contexts = self.retrieve_contexts(query, k=2, filter={"dataset_category": "kadi_apy_docs"})
+        #code_contexts = self.retrieve_contexts(rewritten_query, k=3, filter={"usage": code_library_usage_prediction})
+        context = self.retrieve_contexts(query, k=5)
 
         # Vanilla
         #doc_contexts = self.retrieve_contexts(query, k=3, filter={"dataset_category": "kadi_apy_docs"})
@@ -134,7 +134,44 @@ 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_response(self, query, chat_history, doc_context, code_context):
+    #     """
+    #     Generate a response using the retrieved contexts and the LLM.
+    #     """     
+    #     formatted_history = self.format_history(chat_history)
+        
+    #     # 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.
+            
+    #         Chat History:
+    #         {formatted_history}
+    
+    #         Document Snippets:
+    #         {doc_context}
+    
+    #         Code Snippets:
+    #         {code_context}
+        
+    #         Query:
+    #         {query}
+    #     """
+    #     return self.llm.invoke(prompt).content
+
+
+    def generate_response(self, query, chat_history, context):
         """
         Generate a response using the retrieved contexts and the LLM.
         """     
@@ -144,8 +181,8 @@ class KadiApyRagchain:
         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." 
+            Your task is to answer the user's query based on the guidelines, and if needed, combine understanding provided by "Context"
+            "Context" contains snippets from the source code and/or code examples
     
             Guidelines if generating code:
                 - Display the complete code first, followed by a concise explanation in no more than 5 sentences.  
@@ -159,17 +196,15 @@ class KadiApyRagchain:
             Chat History:
             {formatted_history}
     
-            Document Snippets:
-            {doc_context}
-    
-            Code Snippets:
-            {code_context}
+            Context:
+            {context}
         
             Query:
             {query}
         """
         return self.llm.invoke(prompt).content
 
+
     
     def format_documents(self, documents):
         formatted_docs = []