eaiadminandClaude Code
b4c8ea38d1
回退链从「一条主路由 + 一串替补」改成两级：先把同能力（有说话人分离）的路由 试完，全挂才降级到不分离的路由。降级是**本次事实**而不是配置事实，写进第 2 步 产物（capability_degraded / capability_zh），第 3 步与第 5/6 步据此判为不可用。 - transcribe.go：Result 加 CapabilityDegraded，判据 chain[0] 能分离而实际这条 不能；与 HasSpeakers 分开记（后者可能是「配了却没输出」那种异常） - audio_handlers.go：闸门从「读路由声明的能力」改成「读稿子里实际有没有标签」 （audioTranscriptSpeakerKeys），与第 3 步共用同一句 SpeakerKeysOf；本次没分离 → 哨兵错误 errAudioSpeakersUnavailableThisRun，不再放行去写一份看不出残缺的纪要 - 闸门**不看** capability_degraded：改动前落库的老产物没有这个字段，看它就 fail-open - 第 1 步「转写要求」提前把降级的后果说清；第 2 步产物带完整措辞与「⚠」日志 - 前端两处（SpecialistPanel.vue / audioSkill.js）判断顺序改为先读实际结果 has_speakers、再退回声明 speakers —— 顺序反了会在降级那一次照旧显示第 3 步 - ai_config.json：补 3 条云端无分离路由与各自的回退链 验证：三处变异（产物 key 拼错、标记写死 true、闸门 fail-closed）都验过会红； 新增两个用例文件走真实 gin 路由 + 真实鉴权中间件，断言拦下来的**理由**而不只是 「拦下来了」；go test ./... 全绿、gofmt 干净、前端构建通过。 同期把本地 ASR 装成 systemd 常驻服务（deploy/install_asr_local.sh 七步全过， 开机自启，实测 26.7 分钟录音 → 3.1 分钟）。装的过程挖出两个只在服务化时才暴露的坑： - E12 转写堵住事件循环 → 探活超时 → 本地被判不健康 → auto 静默退云端、音频出网， 全程没有任何报错。修法 run_in_threadpool（deploy/asr/serve.py） - E13 服务账号的 ~ 不可写，pyannote 写不了 ~/.pyannote/database.yml，每次转写 500。 修法 asr.env 加 HOME=<cache 目录>（该目录在 unit 的 ReadWritePaths 里） 顺带收口一处交付缺口：服务源码原先只有 ~/asr-poc 一份，而 DELIVERY.md 的清理计划 要 rm -rf 它 —— 那会让唯一副本变成 /opt 下 root 所有、不在任何版本库里的文件。 现在 deploy/asr/ 是唯一事实源，装机脚本与文档同步改。 已知偏离 / 未做（记在案）： - 界面那句「本次没有说话人分离，后续步骤不可用」只验到后端接口层，没有造出真实 降级场景渲染出来看过 - deploy/asr/ 的引入改变了装机来源：原型目录 $SRC_DIR 从此只提供 venv 与模型， 服务代码一律从仓库取 Co-Authored-By: Claude Code <noreply@anthropic.com>
312 lines
14 KiB
JSON
312 lines
14 KiB
JSON
{
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"agent_routes": {
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"embed_gen": "embed_route_llamacpp_nomic",
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"image_gen": "image_route_openrouter_gpt_image_2",
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"video_gen": "video_route_openrouter_veo_3",
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"path_coach": "chat_route_lmuai_deepseek_v4_flash",
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"title_gen": "chat_route_lmuai_deepseek_v4_flash",
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"audio_transcribe": "audio_route_auto",
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"audio_transcribe_llm": "chat_route_lmuai_deepseek_v4_flash_doc"
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},
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"audio_routes": {
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"audio_route_local_whisper": {
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"base_url": "http://127.0.0.1:8090/v1",
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"description": "语音转写 · 本机 faster-whisper large-v3 + pyannote 3.1（音频不出本机）。由 deploy/eai_agentplatform-asr.service 常驻在 127.0.0.1:8090；服务不在时 audio_route_auto 会改走 fallback_routes 里的云端路由，并在转写产物里标注「音频已出本机」。timeout 定在 840s 而不是一小时：前端 api/audioSkill.js 给这一步的上限是 15 分钟，后端必须在它之前自己收手（否则用户看到的是 axios 超时，而后端还在跑），同时 840s 里还留得下云端那 600s 的回退预算。回退链分两级：先把同能力（有说话人分离）的路由试完，最后才降级到不分离的路由；一旦降级，转写产物里 capability_degraded=true，第 3 步「识别说话人身份」与第 5/6 步「整理/纪要」判为不可用，只交付逐字稿",
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"endpoint": "/audio/transcriptions",
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"model": "large-v3",
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"provider": "local_asr",
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"short_model_name": "whisper large-v3",
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"short_route_name": "本地",
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"supports_speakers": true,
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"timeout_seconds": 840
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},
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"audio_route_siliconflow_diarize": {
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"description": "语音转写 · SiliconFlow / XingChen ASR Diarize（云端，音频会出本机；本地服务不可用时的回退，也可由管理员手动指定）。回退链分两级：先把同能力（有说话人分离）的路由试完，最后才降级到不分离的路由；一旦降级，转写产物里 capability_degraded=true，第 3 步「识别说话人身份」与第 5/6 步「整理/纪要」判为不可用，只交付逐字稿",
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"endpoint": "/audio/transcriptions",
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"model": "XingChenAGI/XingChenASR-Diarize-V3.0",
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"provider": "siliconflow",
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"short_model_name": "XingChen ASR Diarize",
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"short_route_name": "SiliconFlow",
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"supports_speakers": true,
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"timeout_seconds": 600
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},
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"audio_route_siliconflow_qwen3": {
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"description": "语音转写 · SiliconFlow / Qwen3-ASR（云端，音频会出公网；无说话人分离）。实测最快（45 秒片段约 1.4 秒），是降级兜底的首选",
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"endpoint": "/audio/transcriptions",
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"model": "Qwen/Qwen3-ASR-1.7B",
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"provider": "siliconflow",
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"short_model_name": "Qwen3 ASR",
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"short_route_name": "SiliconFlow",
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"supports_speakers": false,
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"timeout_seconds": 600
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},
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"audio_route_openrouter_whisper": {
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"description": "语音转写 · OpenRouter / OpenAI whisper-1（云端，音频会出公网；无说话人分离）。实测是几条云端路线里最慢的（45 秒片段约 9 秒），排在同能力链的末位",
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"endpoint": "/audio/transcriptions",
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"model": "openai/whisper-1",
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"provider": "openrouter",
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"base_url": "https://openrouter.ai/api/v1",
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"short_model_name": "whisper-1",
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"short_route_name": "OpenRouter",
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"supports_speakers": false,
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"timeout_seconds": 600
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},
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"audio_route_openrouter_gpt4o_transcribe": {
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"description": "语音转写 · OpenRouter / OpenAI gpt-4o-transcribe（云端，音频会出公网；无说话人分离）。实测比 whisper-1 快一倍且断句更整，无分离路由里的首选",
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"endpoint": "/audio/transcriptions",
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"model": "openai/gpt-4o-transcribe",
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"provider": "openrouter",
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"base_url": "https://openrouter.ai/api/v1",
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"short_model_name": "gpt-4o-transcribe",
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"short_route_name": "OpenRouter",
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"supports_speakers": false,
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"timeout_seconds": 600
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},
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"audio_route_siliconflow_asr_ultra": {
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"description": "语音转写 · SiliconFlow / XingChen ASR V3.2 Ultra（云端，音频会出公网；无说话人分离）。同门的 V3.2（非 Ultra）实测同样可用、约快一倍，但效果略差，没有单独建路由",
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"endpoint": "/audio/transcriptions",
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"model": "XingChenAGI/XingChenASR-V3.2-Ultra",
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"provider": "siliconflow",
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"short_model_name": "XingChen V3.2 Ultra",
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"short_route_name": "SiliconFlow",
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"supports_speakers": false,
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"timeout_seconds": 600
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}
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},
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"chat_routes": {
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"chat_route_lmuai_deepseek_v4_flash": {
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"description": "LMUAI / DeepSeek Flash。max_tokens 从 4096 提到 32768（2026-09-26）：4096 是当初只按「聊天」估的，但这条路由是 default_route，用户在对话框里选过模型后这个选择会被记进 localStorage 并**一路带到技能内部的加工步骤**（全仓库只有语音转写的第 3、4 步消费 ai_route_id，见 skills/api/audio_handlers.go 的 resolveAudioChatRoute）。于是它会顶掉技能自带的 _doc 路由，让「整理段落」在推理模型思考完之前就把预算耗光、正文 0 字。上限是天花板不是配额，模型写完就停，抬高不会让聊天变慢变贵（实测见 _doc 那条的说明）",
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"endpoint": "/chat/completions",
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"max_tokens": 32768,
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"model": "deepseek-v4-flash",
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"provider": "lmuai",
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"short_model_name": "DeepSeek V4 Flash",
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"short_route_name": "LMUAI",
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"temperature": 0.7
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},
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"chat_route_lmuai_deepseek_v4_flash_doc": {
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"description": "LMUAI / DeepSeek Flash（长文输出档，max_tokens=32768）· 供逐字稿整理这类要产出一整篇文档的技能使用。为什么不复用上面那条 4096 的：这是推理模型，思考与正文共用 max_tokens，而思考的长度跟输入几乎不成比例（实测 1200 字的输入，思考照样写到 7169 字）。4096 下正文一个字都写不出来（六次实测只有一次成功）。再说为什么是 32768 而不是 8192：纪要步骤最后要把各段结果**归并**成一份，那是全流程最长的一次输入（实测 8628 字），8192 下 finish_reason=length、正文 0 字，16384 才出稿、32768 又快又稳。上限提高**不会**让逐块调用变慢或变贵——上限是天花板不是配额，模型写完就停：实测同一批 11 段，8192 与 32768 两档的用时（18.6s / 19.2s）与 completion token（均 4928 / 5094）基本一致。调小这个值之前请先看 audio_transcribe/prompts.go 里的实测表",
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"endpoint": "/chat/completions",
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"max_tokens": 32768,
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"model": "deepseek-v4-flash",
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"provider": "lmuai",
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"short_model_name": "DeepSeek V4 Flash（长文档）",
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"short_route_name": "LMUAI",
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"temperature": 0.7
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},
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"chat_route_ollama_qwen": {
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"description": "本地对话 · llama.cpp / Qwen3.5 4B",
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"endpoint": "/chat/completions",
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"max_tokens": 512,
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"model": "Qwen3.5-4B-Q4_K_M.gguf",
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"provider": "llamacpp",
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"short_model_name": "Qwen3.5 4B",
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"short_route_name": "本地对话",
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"temperature": 0.7,
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"timeout_seconds": 90
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},
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"chat_route_ollama_qwen_fast": {
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"description": "本地快聊 · llama.cpp / Qwen3.5 4B",
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"endpoint": "/chat/completions",
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"max_tokens": 512,
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"model": "Qwen3.5-4B-Q4_K_M.gguf",
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"provider": "llamacpp",
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"short_model_name": "Qwen3.5 4B",
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"short_route_name": "本地快聊",
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"temperature": 0.3
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},
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"chat_route_openrouter_deepseek_v3": {
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"description": "OpenRouter / DeepSeek V3.2",
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"endpoint": "/chat/completions",
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"max_tokens": 4096,
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"model": "deepseek/deepseek-v3.2",
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"provider": "openrouter",
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"short_model_name": "DeepSeek V3.2",
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"short_route_name": "OpenRouter",
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"temperature": 0.6
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},
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"chat_route_openrouter_deepseek_v4_flash": {
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"description": "OpenRouter / DeepSeek V4 Flash 0731",
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"endpoint": "/chat/completions",
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"max_tokens": 4096,
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"model": "deepseek/deepseek-v4-flash-0731",
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"provider": "openrouter",
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"short_model_name": "DeepSeek V4 Flash 0731",
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"short_route_name": "OpenRouter",
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"temperature": 0.7
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},
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"chat_route_openrouter_gpt_5": {
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"description": "OpenRouter / GPT 5.4",
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"endpoint": "/chat/completions",
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"max_tokens": 8192,
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"model": "openai/gpt-5.4",
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"provider": "openrouter",
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"short_model_name": "GPT 5.4",
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"short_route_name": "OpenRouter",
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"temperature": 0.7
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},
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"chat_route_openrouter_grok_4": {
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"description": "OpenRouter / Grok 4.20",
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"endpoint": "/chat/completions",
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"max_tokens": 4096,
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"model": "x-ai/grok-4.20",
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"provider": "openrouter",
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"short_model_name": "Grok 4.20",
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"short_route_name": "OpenRouter",
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"temperature": 0.6
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},
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"chat_route_siliconflow_deepseek_v4_flash": {
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"description": "SiliconFlow / DeepSeek V4 Flash",
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"endpoint": "/chat/completions",
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"max_tokens": 4096,
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"model": "deepseek-ai/DeepSeek-V4-Flash",
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"provider": "siliconflow",
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"short_model_name": "DeepSeek V4 Flash",
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"short_route_name": "SiliconFlow",
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"temperature": 0.7
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}
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},
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"default_audio_route": "audio_route_local_whisper",
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"default_embed_route": "embed_route_llamacpp_nomic",
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"default_route": "chat_route_lmuai_deepseek_v4_flash",
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"description": "eai_agentplatform AI 路由配置 — 按场景分类定义多条路由，支持 provider/模型切换",
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"embed_routes": {
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"embed_route_llamacpp_nomic": {
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"base_url": "http://127.0.0.1:8081/v1",
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"description": "本地向量 · llama.cpp / Nomic Embed v1.5",
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"endpoint": "/embeddings",
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"model": "nomic-embed-text-v1.5.Q4_K_M.gguf",
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"provider": "llamacpp",
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"short_model_name": "Nomic Embed v1.5",
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"short_route_name": "本地向量",
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"timeout_seconds": 30
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},
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"embed_route_ollama_bge_m3": {
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"description": "本地向量 · Ollama / BGE-M3",
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"endpoint": "/embeddings",
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"model": "bge-m3",
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"provider": "ollama",
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"short_model_name": "BGE-M3",
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"short_route_name": "本地向量"
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},
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"embed_route_openrouter_text_v3": {
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"description": "OpenRouter / text-embedding-3-small",
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"endpoint": "/embeddings",
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"model": "openai/text-embedding-3-small",
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"provider": "openrouter",
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"short_model_name": "text-embedding-3-small",
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"short_route_name": "OpenRouter"
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}
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},
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"fallback_routes": {
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"chat_route_lmuai_deepseek_flash": [
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"chat_route_ollama_qwen_fast",
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"chat_route_ollama_qwen"
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],
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"chat_route_openrouter_deepseek_v3": [
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"chat_route_openrouter_grok_4",
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"chat_route_ollama_qwen"
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],
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"chat_route_openrouter_deepseek_v4_flash": [
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"chat_route_openrouter_deepseek_v3",
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"chat_route_openrouter_grok_4",
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"chat_route_ollama_qwen"
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],
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"chat_route_openrouter_grok_4": [
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"chat_route_ollama_qwen"
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],
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"chat_route_siliconflow_deepseek_v4_flash": [
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"chat_route_ollama_qwen_fast",
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"chat_route_ollama_qwen"
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],
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"audio_route_local_whisper": [
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"audio_route_siliconflow_diarize",
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"audio_route_siliconflow_qwen3",
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"audio_route_siliconflow_asr_ultra",
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"audio_route_openrouter_gpt4o_transcribe",
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"audio_route_openrouter_whisper"
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],
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"embed_route_llamacpp_nomic": [
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"embed_route_openrouter_text_v3"
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],
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"embed_route_ollama_bge_m3": [
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"embed_route_openrouter_text_v3"
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],
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"embed_route_openrouter_text_v3": [
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"embed_route_ollama_bge_m3"
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],
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"audio_route_openrouter_gpt4o_transcribe": [
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"audio_route_siliconflow_asr_ultra",
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"audio_route_openrouter_whisper",
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"audio_route_siliconflow_qwen3"
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],
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"audio_route_siliconflow_asr_ultra": [
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"audio_route_openrouter_gpt4o_transcribe",
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"audio_route_openrouter_whisper",
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"audio_route_siliconflow_qwen3"
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],
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"audio_route_openrouter_whisper": [
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"audio_route_openrouter_gpt4o_transcribe",
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"audio_route_siliconflow_asr_ultra",
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"audio_route_siliconflow_qwen3"
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],
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"audio_route_siliconflow_qwen3": [
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"audio_route_openrouter_gpt4o_transcribe",
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"audio_route_siliconflow_asr_ultra",
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"audio_route_openrouter_whisper"
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],
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"audio_route_siliconflow_diarize": [
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"audio_route_local_whisper",
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"audio_route_siliconflow_qwen3",
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"audio_route_siliconflow_asr_ultra",
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"audio_route_openrouter_gpt4o_transcribe",
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"audio_route_openrouter_whisper"
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]
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},
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"image_routes": {
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"image_route_ollama_llava": {
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"description": "本地图像 · Ollama / LLaVA",
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"endpoint": "/chat/completions",
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"max_tokens": 2048,
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"model": "llava",
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"provider": "ollama",
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"short_model_name": "LLaVA",
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"short_route_name": "本地图像",
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"temperature": 0.7
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},
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"image_route_openrouter_flux": {
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"description": "OpenRouter / FLUX 1.1 Pro",
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"endpoint": "/images/generations",
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"max_tokens": 1024,
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"model": "black-forest-labs/flux-1.1-pro",
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"provider": "openrouter",
|
||
"short_model_name": "FLUX 1.1 Pro",
|
||
"short_route_name": "OpenRouter",
|
||
"temperature": 1
|
||
},
|
||
"image_route_openrouter_gpt_image_2": {
|
||
"description": "OpenRouter / GPT 5.4 Image 2",
|
||
"endpoint": "/images/generations",
|
||
"max_tokens": 4096,
|
||
"model": "openai/gpt-5.4-image-2",
|
||
"provider": "openrouter",
|
||
"short_model_name": "GPT 5.4 Image 2",
|
||
"short_route_name": "OpenRouter",
|
||
"temperature": 1
|
||
}
|
||
},
|
||
"video_routes": {
|
||
"video_route_openrouter_veo_3": {
|
||
"description": "OpenRouter / Google Veo 3",
|
||
"endpoint": "/videos/generations",
|
||
"model": "google/veo-3",
|
||
"max_tokens": 4096,
|
||
"provider": "openrouter",
|
||
"short_model_name": "Veo 3",
|
||
"short_route_name": "OpenRouter",
|
||
"temperature": 0.7
|
||
}
|
||
},
|
||
"version": "2.1.0"
|
||
}
|