Update app.py

app.py

@@ -4,6 +4,11 @@ from docx import Document
 from sentence_transformers import SentenceTransformer
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.docstores import InMemoryDocstore
+from langchain.docstores.base import Docstore
+from langchain.vectorstores.faiss import FAISS
+from langchain.vectorstores.faiss import FAISSIndex
+from langchain.vectorstores.faiss import IndexToDocstoreID
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 from nltk.tokenize import sent_tokenize
 import torch
@@ -42,7 +47,6 @@ retriever_tokenizer = AutoTokenizer.from_pretrained(retriever_model_name)
 
 # Initialize FAISS index using LangChain
 hf_embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
-faiss_index = FAISS(embedding_function=hf_embeddings)
 
 # Load or create FAISS index
 index_path = "faiss_index.pkl"
@@ -51,6 +55,10 @@ if os.path.exists(index_path):
         faiss_index = pickle.load(f)
         print("Loaded FAISS index from faiss_index.pkl")
 else:
+    index = FAISSIndex(d=hf_embeddings.model.get_sentence_embedding_dimension())
+    docstore = InMemoryDocstore({})
+    index_to_docstore_id = IndexToDocstoreID({})
+    faiss_index = FAISS(embedding_function=hf_embeddings, index=index, docstore=docstore, index_to_docstore_id=index_to_docstore_id)
     print("Created new FAISS index")
 
 def preprocess_text(text):
@@ -129,5 +137,3 @@ with gr.Blocks() as demo:
         query_button.click(fn=process_and_query, inputs=[query], outputs=query_output)
 
 demo.launch()
-
-