25 Mar, 2026
7 commits
24 Mar, 2026
3 commits
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加了一个过滤/降权词典，query中有独立分词匹配到指定的触发词，将过滤带某些分词的商品（比如fitted/修身，过滤宽松、loose、relaxed、baggy,slouchy等商品） 2. reranker的query使用翻译后的
23 Mar, 2026
5 commits
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combined_fields+best_field+phrase_boost
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子句都变成了一个带有以下结构的命名布尔查询： must：combined_fields should：加权后的 best_fields 和 phrase 子句 主要改动位于 search/es_query_builder.py，但此次调整沿用了现有语言路由设计，并未引入一次性分支。额外的 should 子句权重现在通过 config/schema.py、config/loader.py、search/searcher.py 以及 config/config.yaml 进行配置驱动，从而保持结构的集中管理。
22 Mar, 2026
4 commits
21 Mar, 2026
2 commits
20 Mar, 2026
4 commits
19 Mar, 2026
7 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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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
4 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.
17 Mar, 2026
4 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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- 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