Update app.py

app.py

@@ -112,12 +112,14 @@ 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 embeddings
+# Extract the content from documents and create embeddings
 embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-embeddings = [embedding_model.encode(doc.page_content) for doc in docs]
+texts = [doc.page_content for doc in docs]
+embeddings = embedding_model.encode(texts)
 
-# Save the embeddings to the vector store
-vectorstore = Chroma.from_embeddings(embeddings=embeddings, documents=docs, persist_directory="./data")
+# Create a Chroma vector store and add documents and their embeddings
+vectorstore = Chroma(persist_directory="./data")
+vectorstore.add_texts(texts, embeddings)
 vectorstore.persist()
 
 # Load the Hugging Face model for text generation
@@ -172,3 +174,4 @@ demo.launch(debug=True)
 
 
 
+