Update app.py

app.py

@@ -3,7 +3,6 @@ import re
 import os
 from langchain.chains import ConversationalRetrievalChain
 from langchain.document_loaders import WebBaseLoader
-from langchain.embeddings import SentenceTransformerEmbedding
 from langchain.vectorstores import Chroma
 from langchain.prompts import load_prompt
 from langchain.chat_models import ChatGroq
@@ -107,25 +106,8 @@ if st.button("Submit Query"):
             text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
             splits = text_splitter.split_documents(docs)
 
-            # Define the embedding class
-            class SentenceTransformerEmbedding:
-                def __init__(self, model_name):
-                    self.model = SentenceTransformer(model_name)
-
-                def embed_documents(self, texts):
-                    embeddings = self.model.encode(texts, convert_to_tensor=True)
-                    if isinstance(embeddings, torch.Tensor):
-                        return embeddings.cpu().detach().numpy().tolist()  # Convert tensor to list
-                    return embeddings
-
-                def embed_query(self, query):
-                    embedding = self.model.encode([query], convert_to_tensor=True)
-                    if isinstance(embedding, torch.Tensor):
-                        return embedding.cpu().detach().numpy().tolist()[0]  # Convert tensor to list
-                    return embedding[0]
-
             # Initialize the embedding model
-            embedding_model = SentenceTransformerEmbedding('all-MiniLM-L6-v2')
+            embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
 
             # Initialize Chroma with the embedding class
             vectorstore = Chroma.from_documents(documents=splits, embedding=embedding_model)