deepopen-com/deepopen

选用指南

AI 根据 README 整理 · 2026/10/14 13:26:08

使用方式

通过 Python 库使用，支持路由模式和单模型模式，可自定义类型决策规则。

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2. 推荐路由模式（开箱即用） 通过内置Router入口点，自动完成语言检测和模型调度，预加载所有检查点后即可实现亚毫秒级路由切换： ```python import deepopen from deepopen import Router 将所有检查点预加载到内存，实现35毫秒以内的极速推理 router = Router(preload=True, device="cuda") 定义任意语言的输入状态 state = { "from": "user@acme.com", "subject": "Duplicate charge on invoice 4411", "body": "Hi, we were billed twice for March. Please refund the duplicate today or we will cancel our plan." } 定义自定义类型决策规则 questions = { "department": { "type": "choice", "instructions": "Which department should handle this request?", "criteria": {

If you only need a single checkpoint for a dedicated pipeline, you can load models directly: ```python import deepopen # 1. Load a specific checkpoint directly from the hub agent = deepopen.load("convaiinnovations/deepopen") # English root agent_ml = deepopen.load("convaiinnovations/deepopen", subfolder="multilingual") # 100+ languages agent_td = deepopen.load("convaiinnovations/deepopen", subfolder="typed-decisions") # 2. Run all questions in ONE single forward pass (~35 ms on GPU) result = agent.predict(state, questions) answers = result["answers"]

部署要求

需要安装 Python 包，推荐使用 GPU（如 T4）以获得最佳性能，但也可在 CPU 上运行。

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2. 全链路智能路由机制：在模型前向推理前，通过纯Python实现的语言检测模块，在<0.5毫秒内识别输入内容的脚本类型和所属语言，自动匹配最优检查点，彻底避免了英文模型在非拉丁语种下“高置信度错误”的致命问题——此前纯英文检查点在高棉语任务中准确率为0，却给出95.2%的错误置信度，仅靠置信度阈值完全无法规避风险。 3. 严格校准的概率输出：依托RLCD强化学习框架训练，所有输出的置信度分数具备严格的统计意义，经过域温度校准后，ECE（预期校准误差）低至0.081，远优于同类方案，可直接用于生产环境的自动置信门控流程，高置信度请求直接自动处理，低置信度请求自动流转人工审核。 4. 极致的性能表现：在特斯拉T4显卡上实测，单请求推理仅需32.8毫秒，10题批量处理仅72.3毫秒，单T4显卡最高支持每秒103-332个问题的吞吐量，实测速度是TypeSafe Jev的7.8倍。 5. 完全开源零成本部署：采用Apache 2.0开源许可，所有权重完全开放，支持本地自托管，无需调用任何付费API，不存在按Token计费的额外成本，对比TypeSafe Jev每百万Token 0.042美元的定价，长期大规模部署可节省近100%的推理成本。 快速上手部署流程 1. 安装依赖包 直接通过PyPI一键安装最新版本： ```bash pip install deepopen ```

A cold checkpoint build costs seconds; language detection costs microseconds. At the default `max_loaded=1`, traffic that alternates languages rebuilds a model on *every* request (measured at a 7.4 s median reload on CPU and 10.3 s on T4). For a server or production app, preload: ```python # Every checkpoint resident in memory; language flips cost detection only (<1 ms) router = Router(preload=True) router = Router(preload=True, device="cuda") # Or preload only the specific checkpoints you serve: router.preload(["english", "multilingual"])

已知限制

在高基数标签场景（如超过20个选项）下性能下降，但可通过调整 head_max_len 参数扩展支持能力。

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* **High-cardinality label spaces (>20 options at default settings):** On Banking77, Jev scores 0.870 (on 72 labels) while deepopen scores 0.425 (on 77 labels at default 256-token head budget). This is an architectural token-budget constraint: options share a fixed `head_max_len` budget (192 tokens on English, 256 on multilingual), so 77 options receive only ~3 to 4 tokens per label, causing text to become indistinguishable. Jev supports up to 255 options out-of-the-box. While `deepopen-multilingual` supports 1,024 context (and up to 8,192 in the encoder) and you can raise