added EvalDataset Generation

app.py

@@ -273,7 +273,7 @@ class BSIChatbot:
         all_documents = []
 
         #print(vectorstore.index_to_docstore_id)
-        print(vectorstore)
+        #newprint(vectorstore)
         # Iteriere über alle IDs im index_to_docstore_id
         docs = vectorstore.docstore._dict.values()
 
@@ -353,7 +353,7 @@ 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:
@@ -362,18 +362,18 @@ class BSIChatbot:
                 self.initializeRerankingModel()
             print("Starting Reranking Chunks...")
             rerankingModel
-            print("DBG:retrieved_chunks_text")
-            print(type(retrieved_chunks_text))
-            print(retrieved_chunks_text)
+            #newprint("DBG:retrieved_chunks_text")
+            #newprint(type(retrieved_chunks_text))
+            #new print(retrieved_chunks_text)
             retrieved_chunks_text = rerankingModel.rerank(query, retrieved_chunks_text, k=15)
-            print("DBG:retrieved_chunks_text after rerank")
-            print(type(retrieved_chunks_text))
-            print(retrieved_chunks_text)
+            #newprint("DBG:retrieved_chunks_text after rerank")
+            #newprint(type(retrieved_chunks_text))
+            #newprint(retrieved_chunks_text)
             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}")
+                #newprint(f"Reranked Chunk number {i}:\n{chunk}")
                 i = i + 1
 
         context = "\nExtracted documents:\n"