Update utils.py

utils.py

@@ -35,7 +35,8 @@ from langchain_community.document_loaders import PyPDFLoader,  UnstructuredWordD
 from langchain.schema import AIMessage, HumanMessage
 from langchain_community.llms import HuggingFaceHub
 from langchain_community.llms import HuggingFaceTextGenInference
-from langchain_community.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
+#from langchain_community.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings
+from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_community.tools import DuckDuckGoSearchRun
 from typing import Dict, TypedDict
 from langchain_core.messages import BaseMessage
@@ -224,9 +225,9 @@ def document_storage_chroma(splits):
 def document_retrieval_chroma(llm, prompt):  
     #HF embeddings -----------------------------------
     #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"})
+    #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 um die embedings zu speichern
     db = Chroma(embedding_function = embeddings, persist_directory = PATH_WORK + CHROMA_DIR)