Orcheo让对话式搜索开发更简单，一键搭建完整系统
Orcheo: A Modular Full-Stack Platform for Conversational Search
- 模块化设计，单文件组件可复用，提升研究可共享性
- 内置45+现成组件，快速搭建从理解到生成的完整流程
- 支持原型到生产部署，适合研究者与工程师协作使用
对话式搜索（CS）需要集成查询重写、排序和响应生成的复杂工程流程。当前研究面临两大障碍：缺乏统一框架以高效共享成果，以及难以部署端到端原型用于用户评估。我们提出Orcheo，一个开源平台，旨在填补这一空白。Orcheo具备三大优势：(i) 模块化架构通过单文件节点模块促进组件复用，提升研究的可共享性和可复现性；(ii) 生产就绪的基础设施通过双执行模式、安全凭证管理与执行监控，实现从原型到系统的无缝衔接，并内置AI辅助编码功能，降低学习门槛；(iii) 提供45+开箱即用的组件，覆盖查询理解、排序与响应生成，可快速构建完整的对话式搜索流水线。我们描述了框架架构，并通过案例研究验证其模块化与易用性。Orcheo已以MIT许可证开源，项目地址为https://github.com/AI-Colleagues/orcheo。
原文摘要 · Abstract (English)
Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation. CS researchers currently face two barriers: the lack of a unified framework for efficiently sharing contributions with the community, and the difficulty of deploying end-to-end prototypes needed for user evaluation. We introduce Orcheo, an open-source platform designed to bridge this gap. Orcheo offers three key advantages: (i) A modular architecture promotes component reuse through single-file node modules, facilitating sharing and reproducibility in CS research; (ii) Production-ready infrastructure bridges the prototype-to-system gap via dual execution modes, secure credential management, and execution telemetry, with built-in AI coding support that lowers the learning curve; (iii) Starter-kit assets include 45+ off-the-shelf components for query understanding, ranking, and response generation, enabling the rapid bootstrapping of complete CS pipelines. We describe the framework architecture and validate Orcheo's utility through case studies that highlight modularity and ease of use. Orcheo is released as open source under the MIT License at https://github.com/AI-Colleagues/orcheo.
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