Update app.py

app.py

@@ -171,10 +171,10 @@ def document_loading_splitting():
 #Chroma DB die splits ablegen - vektorisiert...
 def document_storage_chroma(splits):
     #OpenAi embediings
-    #Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(disallowed_special = ()),  persist_directory = PATH_WORK + CHROMA_DIR)  
+    Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(disallowed_special = ()),  persist_directory = PATH_WORK + CHROMA_DIR)  
 
     #HF embeddings
-    Chroma.from_documents(documents = splits, embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cpu"}, encode_kwargs={'normalize_embeddings': False}),  persist_directory = PATH_WORK + CHROMA_DIR)  
+    #Chroma.from_documents(documents = splits, embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cpu"}, encode_kwargs={'normalize_embeddings': False}),  persist_directory = PATH_WORK + CHROMA_DIR)  
     
 #Mongo DB die splits ablegen - vektorisiert...
 def document_storage_mongodb(splits):
@@ -185,11 +185,11 @@ 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 - die ...InstructEmbedding ist sehr rechenaufwendig
     #embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})
     #etwas weniger rechenaufwendig:
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cpu"}, encode_kwargs={'normalize_embeddings': False})
+    #embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cpu"}, encode_kwargs={'normalize_embeddings': False})
 
     #ChromaDb für OpenAI embedinngs
     db = Chroma(embedding_function = embeddings, persist_directory = PATH_WORK + CHROMA_DIR)