Troubleshooting 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 Structured Outputs + Go：Schema 演进、坏样本兜底与灰度回滚

Wed, 11 Mar 2026 01:08:00 +0000

Structured Outputs 最容易翻车的地方，不是“模型不听话”，而是你把 schema 当成了永远不变的圣旨。

线上一旦进入版本演进期，最常见的事故就是：字段新增后老消费端崩、枚举值扩展后校验误杀、坏样本把整条链路拖死，最后只能半夜回滚，像在给自己写惊悚片。

OpenAI Responses Structured Outputs with Go: Schema Evolution, Bad-Case Fallbacks, and Gradual Rollback

Wed, 11 Mar 2026 01:08:00 +0000

The hardest part of Structured Outputs is not getting JSON once. It is surviving schema changes without turning production into a small fire with excellent logs and terrible business results.

Once a Go service starts evolving prompts and response contracts, the usual failure modes show up fast: a new required field breaks older consumers, an enum expands and strict validation kills valid requests, or one bad sample drags the whole chain into retries and rollback panic.

OpenAI Responses + Go 工具调用重试风暴治理：幂等键、退避抖动与熔断阈值

Wed, 04 Mar 2026 01:10:40 +0000

线上最可怕的不是一次失败，而是失败后被重试放大。

在 OpenAI Responses + Go 的工具调用链路里，如果没有幂等键、退避抖动和熔断阈值，10 个请求很快就能打成 1000 个下游调用，账单和延迟一起爆炸。

OpenAI Responses + Go: Taming Retry Storms with Idempotency Keys, Jittered Backoff, and Circuit Breakers

Wed, 04 Mar 2026 01:10:40 +0000

The most expensive outage is not a single failure — it is a failure amplified by retries.

In an OpenAI Responses + Go tool-calling stack, missing idempotency, jittered backoff, and breaker thresholds can turn 10 failing requests into 1000 downstream calls in minutes.

Go Memory Leak Triage in Production: pprof + FlameGraph Step by Step

Sat, 14 Feb 2026 21:20:00 +0800

If your Go service RSS keeps climbing, drops after restart, then climbs again, you likely have a memory retention problem (or an actual leak pattern).

Do not start with random code edits. Run a clean evidence chain: metrics trend check → pprof snapshots → FlameGraph comparison → object growth path → regression validation.