Update kadiApy_ragchain.py

kadiApy_ragchain.py

@@ -248,21 +248,21 @@ class KadiApyRagchain:
         """
         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}
-            """
+        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