Update kadiApy_ragchain.py

kadiApy_ragchain.py

@@ -245,25 +245,25 @@ class KadiApyRagchain:
 
     
     def formulate_question(self, source_code):
-    """
-    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 and run the method provided in the "context". 
-    
-            "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 method 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.
-    
-            Context:
-            {context}
         """
-    return self.llm.invoke(prompt).content
+        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 and run the method provided in the "context". 
+        
+                "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 method 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.
+        
+                Context:
+                {context}
+            """
+        return self.llm.invoke(prompt).content