Update app.py

app.py

@@ -7,7 +7,6 @@ from sentence_transformers import SentenceTransformer
 from transformers import AutoTokenizer, AutoModel
 import torch
 from langchain.vectorstores import FAISS
-from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.chains import RetrievalQA
 from langchain.prompts import PromptTemplate
 from langchain.llms.base import LLM
@@ -83,8 +82,8 @@ def create_vector_store(text):
     # Convert embeddings to a numpy array
     embeddings = np.array(embeddings, dtype=np.float32)
 
-    # Create a FAISS vector store with valid embeddings
-    vector_store = FAISS.from_texts(texts=sentences, embedding_function=lambda x: embeddings)
+    # Create a FAISS vector store with sentences and their embeddings
+    vector_store = FAISS.from_embeddings(embeddings=embeddings, texts=sentences)
     return vector_store, sentences
 
 # Streamlit app