[Question] client.batch.failed_objects

import time
import weaviate
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import StorageContext, Settings
from llama_index.readers.file import PyMuPDFReader
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
import nest_asyncio
nest_asyncio.apply()  # Only needed in Jupyter notebooks
weaviate_client = weaviate.connect_to_local()
weaviate_client.connect()
Settings.llm = OpenAI(temperature=0, model="gpt-4o")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small", dimensions=512)
splitter = SentenceSplitter(chunk_size=512, chunk_overlap=100)
documents = SimpleDirectoryReader("./data1").load_data()
nodes = splitter.get_nodes_from_documents(documents)
print(nodes)
if weaviate_client.collections.exists("TextNode"):
    weaviate_client.collections.delete("TextNode")
schema = {
           "class": "TextNode",
           "properties": [
               {"name": "id_", "dataType": ["string"], },
               {"name": "embedding", "dataType": ["number[]"], },
               {"name": "file_path", "dataType": ["string"], },
               {"name": "file_name", "dataType": ["string"], },
               {"name": "file_type", "dataType": ["string"], },
               {"name": "file_size", "dataType": ["int"], },
               {"name": "creation_date", "dataType": ["string"], },
               {"name": "last_modified_date", "dataType": ["string"], },
               # {"name": "source", "dataType": ["string"], },
               {"name": "text", "dataType": ["text"], },
               {"name": "start_char_idx", "dataType": ["int"], },
               {"name": "end_char_idx", "dataType": ["int"], }
               # {"name": "metadata_str", "dataType": ["string"], },
               # {"name": "content", "dataType": ["text"], },
           ]
       }
weaviate_client.collections.create_from_dict(schema)
try:
    collection = weaviate_client.collections.get("TextNode")
    data_lines = []
    for node in nodes:
        embedding = Settings.embed_model.get_text_embedding(node.text)  # 生成嵌入
        node.embedding = embedding 
        properties = {
            "id": node.id_,
            "embedding": node.embedding,
            "file_path": node.metadata.get("file_path"),
            "file_name": node.metadata.get("file_name"),
            "file_type": node.metadata.get("file_type"),
            "file_size": node.metadata.get("file_size"),
            "creation_date": node.metadata.get("creation_date"),
            "last_modified_date": node.metadata.get("last_modified_date"),
            # "source": node.metadata.get("source"),
            "text": node.text,
            "start_char_idx": node.start_char_idx,
            "end_char_idx": node.end_char_idx,
            # "metadata_str": node.metadata_template,
            # "content": node.text,
        }
        data_lines.append(properties)
    print(data_lines)
    with collection.batch.dynamic() as batch:
        for data_line in data_lines:
            batch.add_object(properties=data_line)
    print("node insert completation！！！！！！！！！！！")
    vector_store = WeaviateVectorStore(weaviate_client=weaviate_client, index_name="TextNode")
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    index = VectorStoreIndex.from_vector_store(vector_store)
    print(index.index_struct)
    print(index.storage_context)

    query_engine = index.as_query_engine()

    while True:
        question = input("User: ")
        if question.strip() == "":
            break
        start_time = time.time()
        response = query_engine.query(question)
        end_time = time.time()
        print(f"Time taken: {end_time - start_time} seconds")
        print(f"AI: {response}")
finally:
    weaviate_client.close()

Error message is:
{‘message’: ‘Failed to send 1 objects in a batch of 1. Please inspect client.batch.failed_objects or collection.batch.failed_objects for the failed objects.’}

How should I solve it?Thank you