I would like to create a ChromaDB with csv in a folder. In each of the csv, each line is a document (text). I am using Gemini embedding model.

My code do run. But the kernel will die after around 100 documents.

It throws the error code as below:
[error] Disposing session as kernel process died ExitCode: 3221225477, Reason:

The The Chroma DB is created. My code as below:

# Set up the embedding model
embed_model = GeminiEmbedding(
    model_name="models/embedding-001",
    api_key=os.environ["GEMINI_API_KEY"],
)
# Create a ServiceContext
service_context = ServiceContext.from_defaults(
    llm=llm,
    embed_model=embed_model,
    chunk_size=1000,
    chunk_overlap=200,
)

def create_chroma_db_from_csv_folder(folder_path, db_path, collection_name):
    # Initialize Chroma client
    chroma_client = chromadb.PersistentClient(path=db_path)
    
    # Create or get the collection with the correct embedding function
    # chroma_collection = chroma_client.get_or_create_collection(
        # name=collection_name
    # )
    chroma_collection = chroma_client.create_collection(name=collection_name)

    # Create vector store
    vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
    storage_context = StorageContext.from_defaults(vector_store=vector_store)

    # Initialize text splitter
    splitter = SentenceSplitter(chunk_size=1000, chunk_overlap=200)

    # Get all CSV files in the folder
    csv_files = [f for f in os.listdir(folder_path) if f.endswith('.csv')]
    
    for csv_file in csv_files:
                file_path = os.path.join(folder_path, csv_file)

                
                try:
                    df = pd.read_csv(file_path)
                except Exception as e:
                    continue
                
                # Assume the text column is named 'text'. Adjust if it's named differently.
                if 'Body' not in df.columns:
                    continue
                
                # Process each row in the dataframe
                for index, row in tqdm(df.iterrows(), total=df.shape[0], desc=f"Processing {csv_file}"):
                    doc_id = str(uuid.uuid4())  # Generate a unique ID for each document
                    text = row['Body']
                    
                    # Create metadata from other columns
                    metadata = row.drop('Body').to_dict()
                    
                    # Create a Document object
                    document = Document(text=text, metadata=metadata, id_=doc_id)
                    
                    # Insert the document into the index
                    VectorStoreIndex.from_documents(
                        [document],
                        storage_context=storage_context,
                        service_context=service_context
                    )

    print(f"Chroma database created at {db_path}")
    print(f"Total documents in collection: {chroma_collection.count()}")

db_path = "./chroma_db"
collection_name = "csv_documents"
create_chroma_db_from_csv_folder(folder_path, db_path, collection_name)

What I did wrong that cause the kernel to die?