Update kadiApy_ragchain.py

kadiApy_ragchain.py

@@ -23,20 +23,11 @@ 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})
-        #code_contexts = self.retrieve_contexts(query, k=2)
 
-        #query_formulated_question= self.formulate_question(code_contexts)
-        #print("question:", query_formulated_question)
-        doc_contexts = self.retrieve_contexts(query, k=2, filter={"dataset_category": "kadi_apy_docs"})
         
-        # Vanilla
-        #doc_contexts = self.retrieve_contexts(query, k=3, filter={"dataset_category": "kadi_apy_docs"})
-        #code_contexts = self.retrieve_contexts(query, k=5, filter={"dataset_category": "kadi_apy_source_code"})
+        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"})
 
-        # doc_contexts = self.retrieve_contexts(query, k=3, filter={"dataset_category": "kadi_apy_docs"})
-        # code_contexts = self.retrieve_contexts(rewritten_query, k=5, filter={"dataset_category": "kadi_apy_source_code"})
         
         
         # Format contexts
@@ -92,39 +83,6 @@ class KadiApyRagchain:
             """
         )
         return self.llm.invoke(rewrite_prompt).content
-
-    def ask_rag(self, context):
-        rewrite_prompt = (
-            f""" "Code snippets" consists of methods and/or class declaration from the source code of an API Wrapper Python library. 
-                  Your task is to write a question how to use   
-
-                Guidelines for rewriting the query:
-                    1. Identify the main action the user wants to perform (e.g., "Upload a file to a record," "Get users of a group").
-                    2. Remove conversational elements like greetings or pleasantries (e.g., "Hello Chatbot", "I need you to help me with").
-                    3. Exclude specific variable values (e.g., "ID of my record is '31'") unless essential to the intent.
-                    4. Rephrase the query to match the format and keywords used in the docstrings, focusing on verbs and objects relevant to the action (e.g., "Add a record to a collection").
-                    5. Given the query the user might need more than one action to achieve his goal. In this case the rewritten query has more than one action. 
-
-                    Examples:
-                        - User query: "Create a Python script with a method that facilitates the creation of records. This method should accept an array of identifiers as a parameter and allow metadata to be added to each record."
-                        - Rewritten query: "create records, add metadata to record"
-                        - User query: "Hi, can you help me write Python code to add a record to a collection? The record ID is '45', and the collection ID is '12'."
-                          Rewritten query: "add a record to a collection"
-                        - User query: I need a python script with which i create a new record with the title: "Hello World"  and then link the record to a given collection.
-                          Rewritten query: "create a new record with title" , "link a record to a collection"
-
-                    Based on these examples and guidelines, rewrite the following user query to align more effectively with the keywords used in the docstrings. 
-                    Do not include any addition comments, explanations, or text.
-                    
-                    Original query:
-                    {query}
-            """
-        )
-        return self.llm.invoke(rewrite_prompt).content
-
-
-        
-
     
     def predict_library_usage(self, query):
         """
@@ -188,68 +146,6 @@ class KadiApyRagchain:
         """
         return self.llm.invoke(prompt).content
 
-
-    # def generate_response(self, query, chat_history, 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 "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.  
-                
-    #         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}
-    
-    #         Context:
-    #         {context}
-        
-    #         Query:
-    #         {query}
-    #     """
-    #     return self.llm.invoke(prompt).content
-
-
-
-    
-    def formulate_question(self, code_contexts):
-        """
-        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 formulate the next logical question a programmer would ask themselves to implement the methods provided in "Code-contexts". 
-    
-            "Context" contains snippets from the source code and metadata that provide details about the method.
-    
-            Guidelines for generating questions:
-                - The question should be specific to the programmer's intent of using the class of the methods within a Python script.
-                - Focus on determining the entry point of the class to which the method belongs.
-                - Avoid vague or general questions; be precise about the next actionable steps.
-                - Only generate the question, do not include any additional comments, explanations, or text.
-                
-            Code-contexts:
-            {code_contexts}
-        """
-        return self.llm.invoke(prompt).content
-
-
-    
-
     
     def format_documents(self, documents):
         formatted_docs = []