19 Mar, 2026
9 commits
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- Text and image embedding are now split into separate services/processes, while still keeping a single replica as requested. The split lives in [embeddings/server.py](/data/saas-search/embeddings/server.py#L112), [config/services_config.py](/data/saas-search/config/services_config.py#L68), [providers/embedding.py](/data/saas-search/providers/embedding.py#L27), and the start scripts [scripts/start_embedding_service.sh](/data/saas-search/scripts/start_embedding_service.sh#L36), [scripts/start_embedding_text_service.sh](/data/saas-search/scripts/start_embedding_text_service.sh), [scripts/start_embedding_image_service.sh](/data/saas-search/scripts/start_embedding_image_service.sh). - Independent admission control is in place now: text and image have separate inflight limits, and image can be kept much stricter than text. The request handling, reject path, `/health`, and `/ready` are in [embeddings/server.py](/data/saas-search/embeddings/server.py#L613), [embeddings/server.py](/data/saas-search/embeddings/server.py#L786), and [embeddings/server.py](/data/saas-search/embeddings/server.py#L1028). - I checked the Redis embedding cache. It did exist, but there was a real flaw: cache keys did not distinguish `normalize=true` from `normalize=false`. I fixed that in [embeddings/cache_keys.py](/data/saas-search/embeddings/cache_keys.py#L6), and both text and image now use the same normalize-aware keying. I also added service-side BF16 cache hits that short-circuit before the model lane, so repeated requests no longer get throttled behind image inference. **What This Means** - Image pressure no longer blocks text, because they are on different ports/processes. - Repeated text/image requests now return from Redis without consuming model capacity. - Over-capacity requests are rejected quickly instead of sitting blocked. - I did not add a load balancer or multi-replica HA, per your GPU constraint. I also did not build Grafana/Prometheus dashboards in this pass, but `/health` now exposes the metrics needed to wire them. **Validation** - Tests passed: `.venv/bin/python -m pytest -q tests/test_embedding_pipeline.py tests/test_embedding_service_limits.py` -> `10 passed` - Stress test tool updates are in [scripts/perf_api_benchmark.py](/data/saas-search/scripts/perf_api_benchmark.py#L155) - Fresh benchmark on split text service `6105`: 535 requests / 3s, 100% success, `174.56 rps`, avg `88.48 ms` - Fresh benchmark on split image service `6108`: 1213 requests / 3s, 100% success, `403.32 rps`, avg `9.64 ms` - Live health after the run showed cache hits and non-zero cache-hit latency accounting: - text `avg_latency_ms=4.251` - image `avg_latency_ms=1.462`
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The instability is very likely real overload, but `lsof -i :6005 | wc -l = 75` alone does not prove it. What does matter is the live shape of the service: it is a single `uvicorn` worker on port `6005`, and the code had one shared process handling both text and image requests, with image work serialized behind a single lock. Under bursty image traffic, requests could pile up and sit blocked with almost no useful tracing, which matches the “only blocking observed” symptom. now adds persistent log files, request IDs, per-request request/response/failure logs, text microbatch dispatch logs, health stats with active/rejected counts, and explicit overload admission control. New knobs are `TEXT_MAX_INFLIGHT`, `IMAGE_MAX_INFLIGHT`, and `EMBEDDING_OVERLOAD_STATUS_CODE`. Startup output now shows those limits and log paths in [scripts/start_embedding_service.sh](/data/saas-search/scripts/start_embedding_service.sh#L80). I also added focused tests in [tests/test_embedding_service_limits.py](/data/saas-search/tests/test_embedding_service_limits.py#L1). What this means operationally: - Text and image are still in one process, so this is not the final architecture. - But image spikes will now be rejected quickly once the image lane is full instead of sitting around and consuming the worker pool. - Logs will now show each request, each rejection, each microbatch dispatch, backend time, response time, and request ID. Verification: - Passed: `.venv/bin/python -m pytest -q tests/test_embedding_service_limits.py` - I also ran a wider test command, but 3 failures came from pre-existing drift in [tests/test_embedding_pipeline.py](/data/saas-search/tests/test_embedding_pipeline.py#L95), where the tests still monkeypatch `embeddings.text_encoder.redis.Redis` even though [embeddings/text_encoder.py](/data/saas-search/embeddings/text_encoder.py#L1) no longer imports `redis` that way. 已把 CLIP_AS_SERVICE 的默认模型切到 ViT-L-14，并把这套配置收口成可变更的统一入口了。现在默认值在 embeddings/config.py (line 29) 的 CLIP_AS_SERVICE_MODEL_NAME，当前为 CN-CLIP/ViT-L-14；scripts/start_cnclip_service.sh (line 37) 会自动读取这个配置，不再把默认模型写死在脚本里，同时支持 CNCLIP_MODEL_NAME 和 --model-name 临时覆盖。scripts/start_embedding_service.sh (line 29) 和 embeddings/server.py (line 425) 也补了模型信息输出，方便排查实际连接的配置。 文档也一起更新了，重点在 docs/CNCLIP_SERVICE说明文档.md (line 62) 和 embeddings/README.md (line 58)：现在说明的是“以配置为准、可覆盖”的机制，而不是写死某个模型名；相关总结文档和内部说明也同步改成了配置驱动表述。
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推理”，不再是先按原始输入条数切块。也就是说，如果 100 条请求分句后变成 150 个 segments，batch_size=64 时会按 64 + 64 + 22 三批推理，推理完再按原始分句计划合并并还原成 100 条返回。这个改动在 local_seq2seq.py (line 241) 和 local_ctranslate2.py (line 391)。 日志这边也补上了两层你要的关键信息： 分句摘要日志：Translation segmentation summary，会打印输入条数、非空条数、发生分句的输入数、总 segments 数、当前 batch_size、每条输入分成多少段的统计，见 local_seq2seq.py (line 216) 和 local_ctranslate2.py (line 366)。 每个预测批次日志：Translation inference batch，会打印第几批、总批数、该批 segment 数、长度统计、首条预览。CTranslate2 另外还会打印 Translation model batch detail，补充 token 长度和 max_decoding_length，见 local_ctranslate2.py (line 294)。 我也补了测试，覆盖了“分句后再 batching”和“日志中有分句摘要与每批推理日志”，在 test_translation_local_backends.py (line 358)。
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改动： 新增分句与预算工具：translation/text_splitter.py 接入 HF 本地后端：translation/backends/local_seq2seq.py (line 157) 接入 CT2 本地后端：translation/backends/local_ctranslate2.py (line 301) 补了测试：tests/test_translation_local_backends.py 我先把代码里实际限制梳理了一遍，关键配置在 config/config.yaml (line 133)： nllb-200-distilled-600m: max_input_length=256，max_new_tokens=64，并且是 ct2_decoding_length_mode=source + extra=8。现在按这个配置计算出的保守输入预算是 56 token。 opus-mt-zh-en: max_input_length=256，max_new_tokens=256。现在保守输入预算是 248 token。 opus-mt-en-zh: 同上，也是 248 token。 这版分句策略是： 先按强边界切：。！？!?；;…、换行、英文句号 不够再按弱边界切：，,、：:()（）[]【】/| 再不够才按空白切 最后才做 token 预算下的硬切 超长时会“分句翻译后再回拼”，中文目标语言默认无空格回拼，英文等默认按空格回拼，尽量别切太碎 验证： python3 -m compileall translation tests/test_translation_local_backends.py 已通过
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中采用了最优T4配置：ct2_inter_threads=2、ct2_max_queued_batches=16、ct2_batch_type=examples。该设置使NLLB获得了显著更优的在线式性能，同时大致保持了大批次吞吐量不变。我没有将相同配置应用于两个Marian模型，因为聚焦式报告显示了复杂的权衡：opus-mt-zh-en 在保守默认配置下更为均衡，而 opus-mt-en-zh 虽然获得了吞吐量提升，但在 c=8 时尾延迟波动较大。 我还将部署/配置经验记录在 /data/saas-search/translation/README.md 中，并在 /data/saas-search/docs/TODO.txt 中标记了优化结果。关键实践要点现已记录如下：使用CT2 + float16，保持单worker，将NLLB的 inter_threads 设为2、max_queued_batches 设为16，在T4上避免使用 inter_threads=4（因为这会损害高批次吞吐量），除非区分在线/离线配置，否则保持Marian模型的默认配置保守。
18 Mar, 2026
8 commits
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Implemented CTranslate2 for the three local translation models and switched the existing local_nllb / local_marian factories over to it. The new runtime lives in local_ctranslate2.py, including HF->CT2 auto-conversion, float16 compute type mapping, Marian direction handling, and NLLB target-prefix decoding. The service wiring is in service.py (line 113), and the three model configs now point at explicit ctranslate2-float16 dirs in config.yaml (line 133). I also updated the setup path so this is usable end-to-end: ctranslate2>=4.7.0 was added to requirements_translator_service.txt and requirements.txt, the download script now supports pre-conversion in download_translation_models.py (line 27), and the docs/config examples were refreshed in translation/README.md. I installed ctranslate2 into .venv-translator, pre-converted all three models, and the CT2 artifacts are now already on disk: models/translation/facebook/nllb-200-distilled-600M/ctranslate2-float16 models/translation/Helsinki-NLP/opus-mt-zh-en/ctranslate2-float16 models/translation/Helsinki-NLP/opus-mt-en-zh/ctranslate2-float16 Verification was solid. python3 -m compileall passed, direct TranslationService smoke tests ran successfully in .venv-translator, and the focused NLLB benchmark on the local GPU showed a clear win: batch_size=16: HF 0.347s/batch, 46.1 items/s vs CT2 0.130s/batch, 123.0 items/s batch_size=1: HF 0.396s/request vs CT2 0.126s/request One caveat: translation quality on some very short phrases, especially opus-mt-en-zh, still looks a bit rough in smoke tests, so I’d run your real quality set before fully cutting over. If you want, I can take the next step and update the benchmark script/report so you have a fresh full CT2 performance report for all three models.
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batch×并发矩阵”彻底分开展示。 改动在这几处： scripts/benchmark_translation_local_models.py：新增 --suite extended，支持 batch_size=1,4,8,16,32,64、concurrency=1,2,4,8,16,64、以及 batch_size * concurrency <= 128 的组合矩阵；并且单场景模式现在只加载目标模型，load_seconds 更干净，也支持 --disable-cache。 translation/README.md：把性能章节拆成了 batch_sweep、concurrency_sweep、batch x concurrency matrix 三块，补了这次复测的参数、复现命令和摘要表。 perf_reports/20260318/translation_local_models/README.md：新增本轮补测摘要。 完整结果在 translation_local_models_extended_221846.md 和 translation_local_models_extended_221846.json。 这次补测的核心结论很明确： 在线单条请求应该看 concurrency_sweep，也就是固定 batch_size=1 的表。 离线批量吞吐应该看 batch_sweep，4 个方向的最高 raw throughput 都出现在 batch_size=64，但更均衡的默认值仍更像 batch_size=16。 当前本地 seq2seq backend 有单模型锁，提升客户端并发几乎不涨吞吐，主要是把排队时间变成更高的 p95；所以并发更像“延迟预算”问题，不是“扩容吞吐”手段。 本轮在线单条里最快的是 opus-mt-zh-en；最慢、且并发放大最明显的是 nllb-200-distilled-600m en->zh。
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核心改动在 rerank_client.py (line 99)：fuse_scores_and_resort 现在按 rerank * knn * text 的平滑乘法公式计算，优先从 hit["matched_queries"] 里取 base_query 和 knn_query，并把 _text_score / _knn_score 一并写回调试字段。为了让 KNN 也有名字，我给 top-level knn 加了 name: "knn_query"，见 es_query_builder.py (line 273)。搜索执行时会在 rerank 窗口内打开 include_named_queries_score，并在显式排序时加上 track_scores，见 searcher.py (line 400) 和 es_client.py (line 224)。
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2. 优化缓存，缓存粒度为商品级，每次只对batch中未cache的重新计算；key使用每个商品输入的hash
17 Mar, 2026
12 commits
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多个独立翻译能力”重构。现在业务侧不再把翻译当 provider 选型，QueryParser 和 indexer 统一通过 6006 的 translator service client 调用；真正的能力选择、启用开关、model + scene 路由，都收口到服务端和新的 translation/ 目录里了。 这次的核心改动在 config/services_config.py、providers/translation.py、api/translator_app.py、config/config.yaml 和新的 translation/service.py。配置从旧的 services.translation.provider/providers 改成了 service_url + default_model + default_scene + capabilities，每个能力可独立 enabled；服务端新增了统一的 backend 管理与懒加载，真实实现集中到 translation/backends/qwen_mt.py、translation/backends/llm.py、translation/backends/deepl.py，旧的 query/qwen_mt_translate.py、query/llm_translate.py、query/deepl_provider.py 只保留兼容导出。接口上，/translate 现在标准支持 scene，context 作为兼容别名继续可用，健康检查会返回默认模型、默认场景和已启用能力。
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2. 抽象出可复用的 embedding Redis 缓存类（图文共用） 详细： 1. embedding 缓存改为 BF16 存 Redis（读回恢复 FP32） 关键行为（按你给的流程落地） 写入前：FP32 embedding →（normalize_embeddings=True 时）L2 normalize → 转 BF16 → bytes（2字节/维，大端） → redis.setex 读取后：redis.get bytes → BF16 → 恢复 FP32（np.float32 向量） 变更点 新增 embeddings/bf16.py 提供 float32_to_bf16 / bf16_to_float32 encode_embedding_for_redis()：FP32 → BF16 → bytes decode_embedding_from_redis()：bytes → BF16 → FP32 l2_normalize_fp32()：按需归一化 修改 embeddings/text_encoder.py Redis value 从 pickle.dumps(np.ndarray) 改为 BF16 bytes 缓存 key 改为包含 normalize 标记：{prefix}:{n0|n1}:{query}（避免 normalize 开关不同却共用缓存） 修改 tests/test_embedding_pipeline.py cache hit 用例改为写入 BF16 bytes，并使用新 key：embedding:n1:cached-text 修改 docs/缓存与Redis使用说明.md embedding 缓存的 Key/Value 格式更新为 BF16 bytes + n0/n1 修改 scripts/redis/redis_cache_health_check.py embedding pattern 不再硬编码 embedding:*，改为读取 REDIS_CONFIG["embedding_cache_prefix"] value 预览从 pickle 解码改为 BF16 解码后展示 dim/bytes/dtype 自检 在激活环境后跑过 BF16 编解码往返 sanity check：bytes 长度、维度恢复正常；归一化向量读回后范数接近 1（会有 BF16 量化误差）。 2. 抽象出可复用的 embedding Redis 缓存类（图文共用） 新增 embeddings/redis_embedding_cache.py：RedisEmbeddingCache 统一 Redis 初始化（读 REDIS_CONFIG） 统一 BF16 bytes 编解码（复用 embeddings/bf16.py） 统一过期策略：写入 setex(expire_time)，命中读取后 expire(expire_time) 滑动过期刷新 TTL 统一异常/坏数据处理：解码失败或向量非 1D/为空/含 NaN/Inf 会删除该 key 并当作 miss 已接入复用 文本 embeddings/text_encoder.py 用 self.cache = RedisEmbeddingCache(key_prefix=..., namespace="") key 仍是：{prefix}:{query} 图片 embeddings/image_encoder.py 用 self.cache = RedisEmbeddingCache(key_prefix=..., namespace="image") key 仍是：{prefix}:image:{url_or_path} -
- Rename indexer/product_annotator.py to indexer/product_enrich.py and remove CSV-based CLI entrypoint, keeping only in-memory analyze_products API - Introduce dedicated product_enrich logging with separate verbose log file for full LLM requests/responses - Change indexer and /indexer/enrich-content API wiring to use indexer.product_enrich instead of indexer.product_annotator, updating tests and docs accordingly - Switch translate_prompts to share SUPPORTED_INDEX_LANGUAGES from tenant_config_loader and reuse that mapping for language code → display name - Remove hard SUPPORTED_LANGS constraint from LLM content-enrichment flow, driving languages directly from tenant/indexer configuration - Redesign LLM prompt generation to support multi-round, multi-language tables: first round in English, subsequent rounds translate the entire table (headers + cells) into target languages using English instructions
16 Mar, 2026
4 commits
14 Mar, 2026
1 commit
13 Mar, 2026
6 commits
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2. 翻译限速 对应处理（qwen-mt限速）