Update app.py

app.py

@@ -202,7 +202,7 @@ def document_retrieval_chroma(llm, prompt):
     #etwas weniger rechenaufwendig:
     #embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cpu"}, encode_kwargs={'normalize_embeddings': False})
 
-    #ChromaDb für OpenAI embedinngs
+    #ChromaDb um die embedings zu speichern
     db = Chroma(embedding_function = embeddings, persist_directory = PATH_WORK + CHROMA_DIR)
     return db
 
@@ -247,7 +247,8 @@ def generate_prompt_with_history(text, history, max_length=2048):
     for x in history[::-1]:
             history_text = x + history_text
             flag = True
-            
+    print ("Prompt: ..........................")
+    print(prompt+history_text)
     if flag:
         return  prompt+history_text
     else:
@@ -336,7 +337,9 @@ def invoke (prompt, history, rag_option, model_option, openai_api_key,  temperat
             db = document_retrieval_mongodb(llm, history_text_und_prompt)
             result = rag_chain(llm, history_text_und_prompt, db)
         else:
+            print("LLM aufrufen via HF: ...........")
             result = llm_chain(llm, history_text_und_prompt)
+            print(result)
 
     except Exception as e:
         raise gr.Error(e)