Update app.py

app.py

@@ -89,6 +89,7 @@ from langchain.vectorstores import Chroma
 import gradio as gr
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer
+import numpy as np
 
 __import__('pysqlite3')
 sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
@@ -115,7 +116,7 @@ docs = splitter.split_documents(docs)
 # Extract the content from documents and create embeddings
 embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 texts = [doc.page_content for doc in docs]
-embeddings = embedding_model.encode(texts)
+embeddings = embedding_model.encode(texts).tolist()  # Convert numpy arrays to lists
 
 # Create a Chroma vector store and add documents and their embeddings
 vectorstore = Chroma(persist_directory="./data")
@@ -175,3 +176,4 @@ demo.launch(debug=True)
 
 
 
+