llm protocol_error ai_generated partial

Error: streaming response chunk order mismatch - expected index 5 but got 7

ID: llm/streaming-chunk-order-mismatch

Also available as: JSON · Markdown · 中文
75%Fix Rate
82%Confidence
1Evidence
2024-05-10First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
openai==1.23.0 active — — —
gpt-4-turbo-2024-04-09 active — — —
gpt-4o-2024-05-13 active — — —

Root Cause

When using SSE streaming with parallel processing or load balancing, chunks may arrive out of order due to network latency or server-side concurrency, causing the client to reassemble the response incorrectly.

generic

中文

在使用 SSE 流式传输进行并行处理或负载均衡时，由于网络延迟或服务器端并发，块可能乱序到达，导致客户端错误地重新组装响应。

Official Documentation

https://platform.openai.com/docs/guides/streaming

Workarounds

  1. 85% success Implement a buffer that reorders chunks based on sequence numbers before assembly: `buffer[chunk.index] = chunk; while buffer[next_index] is not None: yield buffer[next_index]; next_index++` 
    Implement a buffer that reorders chunks based on sequence numbers before assembly: `buffer[chunk.index] = chunk; while buffer[next_index] is not None: yield buffer[next_index]; next_index++`
  2. 70% success Use a single-threaded SSE client or ensure the API endpoint is not behind a load balancer that reorders requests. 
    Use a single-threaded SSE client or ensure the API endpoint is not behind a load balancer that reorders requests.
  3. 75% success If using a proxy or CDN, bypass it for streaming endpoints to reduce reordering risks. 
    If using a proxy or CDN, bypass it for streaming endpoints to reduce reordering risks.

中文步骤

  1. 实现一个缓冲区，根据序列号对块进行重新排序后组装：`buffer[chunk.index] = chunk; while buffer[next_index] is not None: yield buffer[next_index]; next_index++`
  2. 使用单线程 SSE 客户端，或确保 API 端点不在会重新排序请求的负载均衡器后面。
  3. 如果使用代理或 CDN，请绕过流式端点以减少重新排序的风险。

Dead Ends

Common approaches that don't work:

  1. 60% fail

    Timeout doesn't fix ordering; out-of-order chunks will still be out of order regardless of wait time.

  2. 30% fail

    This works as a workaround but defeats the purpose of streaming for real-time applications.