Update app.py

app.py

@@ -5,6 +5,9 @@ from dotenv import load_dotenv
 from langchain_community.document_loaders import UnstructuredPDFLoader
 from langchain_community.vectorstores import FAISS
 from langchain_community.embeddings import HuggingFaceEmbeddings
+
+# Define the correct model embedding class
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
 from langchain_text_splitters import CharacterTextSplitter
 from langchain_groq import ChatGroq
 from langchain.memory import ConversationBufferMemory
@@ -18,6 +21,7 @@ import gradio as gr
 # Load environment variables
 load_dotenv()
 os.environ["GROQ_API_KEY"] = "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"
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
 
 # File directories
 UPLOAD_FOLDER = 'uploads/'
@@ -41,7 +45,6 @@ def load_pdf(file_path):
     return documents
 
 def prepare_vectorstore(data):
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
     text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=20, separator="\n")
     texts = data
     vectorstore = FAISS.from_texts(texts, embeddings)
@@ -52,7 +55,6 @@ def prepare_vectorstore(data):
     return vectorstore
 
 def load_vectorstore():
-    embeddings = HuggingFaceEmbeddings()
     vectorstore = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
     return vectorstore