Update kadiApy_ragchain.py

kadiApy_ragchain.py

@@ -9,7 +9,7 @@ class KadiApyRagchain:
         self.conversation = []  
 
 
-    def process_query(self, query):
+    def process_query(self, query, chat_history):
         """
         Process a user query, handle history, retrieve contexts, and generate a response.
         """
@@ -40,7 +40,7 @@ class KadiApyRagchain:
         formatted_code_contexts = self.format_documents(code_contexts)
         
         # Generate response
-        response = self.generate_response(query, formatted_doc_contexts, formatted_code_contexts)
+        response = self.generate_response(query, chat_history, formatted_doc_contexts, formatted_code_contexts)
         
         # Add the response to the existing query in the conversation history
         #self.add_to_conversation(llm_response=response)
@@ -130,73 +130,74 @@ class KadiApyRagchain:
         context = self.vector_store.similarity_search(query = query, k=k, filter=filter)       
         return context
 
-    def generate_response(self, query, doc_context, code_context):
-        """
-        Generate a response using the retrieved contexts and the LLM.
-        """
+    # def generate_response(self, query, doc_context, code_context):
+    #     """
+    #     Generate a response using the retrieved contexts and the LLM.
+    #     """
 
 
-        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 the combine understanding provided by 
-                     "Document snippets" with the implementation details provided by "Code Snippets."
+    #     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 the 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.
+    #                  Guidelines if generating code:
+    #                     - Display the complete code first, followed by a concise explanation in no more than 5 sentences.
                     
-                     General Guideline:
-                        - If the user's query can not be fullfilled 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.
+    #                  General Guideline:
+    #                     - If the user's query can not be fullfilled 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": These contain documentation excerpts and code examples that explain how to use the "Kadi-APY" library
-                    Document Snippets:
-                    {doc_context}
+    #                 "Document Snippets": These contain documentation excerpts and code examples that explain how to use the "Kadi-APY" library
+    #                 Document Snippets:
+    #                 {doc_context}
 
 
-                    "Code Snippets": These are raw source code fragments from the implementation of the "Kadi-APY" library.
-                    Code Snippets:
-                    {code_context}
+    #                 "Code Snippets": These are raw source code fragments from the implementation of the "Kadi-APY" library.
+    #                 Code Snippets:
+    #                 {code_context}
         
-                    Query:
-                    {query}
-                """
-        return self.llm.invoke(prompt).content
+    #                 Query:
+    #                 {query}
+    #             """
+    #     return self.llm.invoke(prompt).content
 
 
-    # def generate_response(self, query, history, doc_context, code_context):
-    #     """
-    #     Generate a response using the retrieved contexts and the LLM.
-    #     """     
-    #     formatted_history = format_history(history)
+    def generate_response(self, query, chat_history, doc_context, code_context):
+        """
+        Generate a response using the retrieved contexts and the LLM.
+        """     
+        formatted_history = format_history(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." 
+        # 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.
+            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 "Conversation History" if it provides context that could enhance your understanding of the user's query.
-    #             - 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.
+            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.
             
-    #         Conversation History:
-    #         {formatted_history}
+            Chat History:
+            {formatted_history}
     
-    #         Document Snippets:
-    #         {doc_context}
+            Document Snippets:
+            {doc_context}
     
-    #         Code Snippets:
-    #         {code_context}
+            Code Snippets:
+            {code_context}
         
-    #         Query:
-    #         {query}
-    #     """
-    #     return self.llm.invoke(prompt).content
+            Query:
+            {query}
+        """
+        return self.llm.invoke(prompt).content