Update app.py

app.py

@@ -84,9 +84,6 @@ import os
 import sys
 from langchain.chains import ConversationalRetrievalChain
 from langchain.document_loaders import DirectoryLoader, TextLoader
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.indexes import VectorstoreIndexCreator
-from langchain.indexes.vectorstore import VectorStoreIndexWrapper
 from langchain.text_splitter import CharacterTextSplitter
 from langchain.vectorstores import Chroma
 import gradio as gr
@@ -115,10 +112,12 @@ for f in os.listdir("multiple_docs"):
 splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)
 docs = splitter.split_documents(docs)
 
-# Convert the document chunks to embedding and save them to the vector store
+# Convert the document chunks to embeddings
 embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-embeddings = HuggingFaceEmbeddings(embedding_model=embedding_model)
-vectorstore = Chroma.from_documents(docs, embedding=embeddings, persist_directory="./data")
+embeddings = [embedding_model.encode(doc.content) for doc in docs]
+
+# Save the embeddings to the vector store
+vectorstore = Chroma.from_embeddings(embeddings=embeddings, documents=docs, persist_directory="./data")
 vectorstore.persist()
 
 # Load the Hugging Face model for text generation
@@ -171,3 +170,4 @@ demo.launch(debug=True)
 
 
 
+