Reliability on Mengboy 技术笔记

OpenAI Responses + Go Stream Recovery: Delta Persistence, Resume Tokens, and Duplicate Chunk Dedup

Mon, 23 Mar 2026 01:13:09 +0000

In production, the painful part is not “streaming is slow.” It’s “streaming breaks and then duplicates output after reconnect.”
This guide gives you a practical recovery loop: delta persistence + resume token + idempotent dedup, so reconnection does not replay garbage.

OpenAI Responses + Go 的流式中断恢复：delta 持久化、resume token 与重复片段去重

Mon, 23 Mar 2026 01:13:09 +0000

生产里最难受的不是“流式返回慢”，而是“流式返回断了还重复”，用户看到半句、重连后又从中间重喷一遍。
这篇给一套可落地的恢复闭环：delta 持久化 + resume token + 幂等去重，目标是“断线可续，重放不重字”。

Go + OpenAI Responses: Connection Pooling and Timeout Budgets from HTTP/2 Reuse to Error-Budget Control

Fri, 06 Mar 2026 01:13:12 +0000

When Go services call the OpenAI Responses API in production, the real failures are rarely about model quality. Most incidents come from transport instability: weak connection pooling, conflicting timeout layers, and retry storms.

This guide gives you a practical baseline: HTTP/2 reuse, layered timeout budgets, bounded retries, and error-budget driven operations.

Go 调 OpenAI Responses 的连接池与超时预算：HTTP/2 复用到错误预算闭环

Fri, 06 Mar 2026 01:13:12 +0000

线上 Go 服务调用 OpenAI Responses 时，最容易踩的坑不是“模型不准”，而是链路抖动：连接池不稳、超时预算乱配、重试叠加把自己打挂。

这篇给一套可落地的基线配置：HTTP/2 连接复用、分层超时、错误预算和退避重试，目标是把 5xx 与超时比例压到可控范围，并且能快速定位瓶颈。