Update app.py

app.py

@@ -84,10 +84,10 @@ MODEL_NAME  ="gpt-4"
 #repo_id = "meta-llama/Llama-2-13b-chat-hf"   
 #repo_id = "HuggingFaceH4/zephyr-7b-alpha"
 #repo_id = "meta-llama/Llama-2-70b-chat-hf"    
-#repo_id = "tiiuae/falcon-180B-chat"   
+repo_id = "tiiuae/falcon-40b "  
 #repo_id = "Vicuna-33b"
 #repo_id = "alexkueck/ChatBotLI2Klein"
-repo_id ="mistralai/Mistral-7B-v0.1" 
+#repo_id ="mistralai/Mistral-7B-v0.1" 
 
 
 ################################################
@@ -174,9 +174,9 @@ def document_storage_mongodb(splits):
 
 #dokumente in chroma db vektorisiert ablegen können - die Db vorbereiten daüfur
 def document_retrieval_chroma(llm, prompt):      
-    embeddings = OpenAIEmbeddings()
+    #embeddings = OpenAIEmbeddings()
     #Alternative Embedding - für Vektorstore, um Ähnlichkeitsvektoren zu erzeugen
-    #embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})
+    embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})
     db = Chroma(embedding_function = embeddings,
                 persist_directory = PATH_WORK + CHROMA_DIR)