added EvalDataset Generation

app.py

@@ -448,9 +448,9 @@ class BSIChatbot:
 
         if stepBackPrompt == True:
             stepBackQuery = self.stepBackPrompt(query)
-            print("DBG stepBackQuery:" + stepBackQuery)
+            #newprint("DBG stepBackQuery:" + stepBackQuery)
             stepBackQuery, stepBackContext = self.retrieval(stepBackQuery, rerankingStep, True)
-            print("DBG stepBackContext:" + stepBackContext)
+            #newprint("DBG stepBackContext:" + stepBackContext)
             sysPrompt = """
             You are an helpful Chatbot for the BSI IT-Grundschutz. Using the information contained in the context,
                 give a comprehensive answer to the question.
@@ -459,7 +459,7 @@ class BSIChatbot:
                 If the answer cannot be deduced from the context, do not give an answer.
                 """
             stepBackAnswer = self.queryRemoteLLM(sysPrompt, stepBackQuery, True)
-            print("DBG stepBackAnswer:" + stepBackAnswer)
+            #newprint("DBG stepBackAnswer:" + stepBackAnswer)
             context += "Übergreifende Frage:" + stepBackQuery + "Übergreifender Context:" + stepBackAnswer
 
         #def queryRemoteLLM(self, systemPrompt, query, summary):
@@ -539,9 +539,9 @@ class BSIChatbot:
                 retrieved_chunks_text.append(
                     f"The Document is: '{chunk.metadata['source']}'\nImage Description is: ':{chunk.page_content}")
         i=1
-        for chunk in retrieved_chunks_text:
-            print(f"Retrieved Chunk number {i}:\n{chunk}")
-            i=i+1
+        #newfor chunk in retrieved_chunks_text:
+            #newprint(f"Retrieved Chunk number {i}:\n{chunk}")
+            #newi=i+1
 
         if rerankingStep==True:
             if rerankingModel == None:
@@ -553,9 +553,9 @@ class BSIChatbot:
             retrieved_chunks_text=[chunk["content"] for chunk in retrieved_chunks_text]
 
             i = 1
-            for chunk in retrieved_chunks_text:
-                print(f"Reranked Chunk number {i}:\n{chunk}")
-                i = i + 1
+            #newfor chunk in retrieved_chunks_text:
+                #newprint(f"Reranked Chunk number {i}:\n{chunk}")
+                #newi = i + 1
 
         context = "\nExtracted documents:\n"
         context += "".join([doc for i, doc in enumerate(retrieved_chunks_text)])
@@ -638,7 +638,7 @@ class BSIChatbot:
                     f"The Document is: '{chunk.metadata['source']}'\nImage Description is: ':{chunk.page_content}")
         i=1
         for chunk in retrieved_chunks_text:
-            print(f"Retrieved Chunk number {i}:\n{chunk}")
+            #newprint(f"Retrieved Chunk number {i}:\n{chunk}")
             i=i+1
 
         if rerankingStep==True:
@@ -649,9 +649,9 @@ class BSIChatbot:
             rerankingModel
             retrieved_chunks_text=rerankingModel.rerank(query, retrieved_chunks_text,k=15)
             #REVERSE Rerank results!
-            print("DBG: Rankorder:")
-            for chunk in reversed(retrieved_chunks_text):
-                print(chunk.rank)
+            #newprint("DBG: Rankorder:")
+            #newfor chunk in reversed(retrieved_chunks_text):
+                #newprint(chunk.rank)
             retrieved_chunks_text=[chunk["content"] for chunk in reversed(retrieved_chunks_text)]
 
             i = 1
@@ -704,7 +704,8 @@ class BSIChatbot:
         for data in data_list:
             data["Context"] = None
             data["Answer"] = None
-            print(data)
+
+        print(data_list)
 
         i=1
         #for data in data_list[:3]: