Update app.py

app.py

@@ -9,7 +9,7 @@ from langchain.schema import Document
 from langchain_community.llms import HuggingFaceHub
 from langchain.chains import RetrievalQA, LLMChain
 from langchain.prompts import PromptTemplate
-from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.vectorstores import Chroma
 from langchain.retrievers import ContextualCompressionRetriever
 from langchain.retrievers.document_compressors import LLMChainExtractor
@@ -69,7 +69,7 @@ def setup_advanced_rag_pipeline(model_name):
     splits = load_and_process_documents("index_training.json")  # Replace with your document path
     
     # Set up InstructorEmbeddings
-    embeddings = HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
     
     # Create vectorstore
     vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)