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”。 ...
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. ...
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。 ...