Update app.py

app.py

@@ -119,8 +119,8 @@ embeddings = embedding_model.encode(texts).tolist()  # Convert numpy arrays to l
 
 # Create a Chroma vector store and add documents and their embeddings
 vectorstore = Chroma(persist_directory="./data")
-for text, embedding in zip(texts, embeddings):
-    vectorstore.add_texts([text], metadatas=[{"embedding": embedding}])
+for i, (text, embedding) in enumerate(zip(texts, embeddings)):
+    vectorstore.add_texts([text], metadatas=[{"id": i}], embeddings=[embedding])
 vectorstore.persist()
 
 # Load the Hugging Face model for text generation
@@ -179,3 +179,4 @@ demo.launch(debug=True)
 
 
 
+