Update app.py

app.py

@@ -117,8 +117,12 @@ embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 texts = [doc.page_content for doc in docs]
 embeddings = embedding_model.encode(texts).tolist()  # Convert numpy arrays to lists
 
+# Create a wrapper function for the embedding function
+def embedding_function(texts):
+    return embedding_model.encode(texts).tolist()
+
 # Create a Chroma vector store with an embedding function and add documents and their embeddings
-vectorstore = Chroma(persist_directory="./db", embedding_function=embedding_model.encode)
+vectorstore = Chroma(persist_directory="./db", embedding_function=embedding_function)
 vectorstore.add_texts(texts=texts, metadatas=[{"id": i} for i in range(len(texts))], embeddings=embeddings)
 vectorstore.persist()
 
@@ -182,3 +186,4 @@ demo.launch(debug=True)
 
 
 
+