Most streaming failures are not about “can it stream”, but “does it stay stable under load”: broken chunks, stuck clients, timeout cascades, and retry storms. ...
线上最容易把流式输出做坏的，不是“能不能流出来”，而是流量一上来就抖：token 断片、客户端卡死、超时雪崩、重试风暴。 ...
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. ...
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”。 ...
Production streaming fails in two predictable ways: users wait while the stream silently drops, and your logs say “timeout” without telling you where it actually broke. This guide gives you a practical Go pattern for OpenAI Responses API streaming with strict timeout boundaries, safe retries, and useful telemetry. ...
线上流式生成最怕两件事：用户在等，你的连接先断；日志里报错一堆，你却不知道是哪一层炸了。 这篇给你一个能直接落地的 Go 工程模板：把 OpenAI Responses API 的流式调用做成可超时、可重试、可观测的生产级链路。 ...