An error occurred: Error code: 400 - {'object': 'error', 'message': '**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**\n\n(The expanded size of the tensor (513) must match the existing size (512) at non-singleton dimension 1. Target sizes: [2, 513]. Tensor sizes: [1, 512])', 'code': 50001}

please help me !

import os
import asyncio
from lightrag import LightRAG, QueryParam
from lightrag.llm import openai_complete_if_cache, openai_embedding
from lightrag.utils import EmbeddingFunc
import numpy as np

WORKING_DIR = "./dickens"

if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)

async def llm_model_func(
    prompt, system_prompt=None, history_messages=[], **kwargs
) -> str:
    return await openai_complete_if_cache(
        "/root/.cache/modelscope/hub/Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4",
        prompt,
        system_prompt=system_prompt,
        history_messages=history_messages,
        api_key=os.getenv("EMPTY"),
        base_url="http://0.0.0.0:8001/v1",
        **kwargs,
    )

async def embedding_func(texts: list[str]) -> np.ndarray:
    return await openai_embedding(
        texts,
        model="bge-large-zh-v1.5",
        api_key=os.getenv("EMPTY"),
        base_url="http://0.0.0.0:8000/v1",
    )

async def get_embedding_dim():
    test_text = ["This is a test sentence."]
    embedding = await embedding_func(test_text)
    embedding_dim = embedding.shape[1]
    return embedding_dim

# function test
async def test_funcs():
    result = await llm_model_func("How are you?")
    print("llm_model_func: ", result)

    result = await embedding_func(["How are you?"])
    print("embedding_func: ", result)

async def main():
    try:
        embedding_dimension = await get_embedding_dim()
        print(f"Detected embedding dimension: {embedding_dimension}")

        rag = LightRAG(
            working_dir=WORKING_DIR,
            llm_model_func=llm_model_func,
            embedding_func=EmbeddingFunc(
                embedding_dim=embedding_dimension,
                max_token_size=512,
                func=embedding_func,
            ),
        )

        with open("/data/project/raag/caiwu.txt", "r", encoding="utf-8") as f:
            await rag.ainsert(f.read())

        # Perform hybrid search
        print(
            await rag.aquery(
                "What are the top themes in this story?",
                param=QueryParam(mode="hybrid"),
            )
        )
    except Exception as e:
        print(f"An error occurred: {e}")

if __name__ == "__main__":
    asyncio.run(main())

An error occurred: Error code: 400 - {'object': 'error', 'message': '**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**\n\n(The expanded size of the tensor (513) must match the existing size (512) at non-singleton dimension 1.  Target sizes: [2, 513].  Tensor sizes: [1, 512])', 'code': 50001}

HKUDS / LightRAG