📄 中文摘要
CDDBS 采用六阶段的后台管道来解决实际系统中常见的问题。大多数大型语言模型（LLM）教程仅展示如何调用 API 并打印响应，而真实系统需要从外部源获取数据、构建约束输出格式的提示、解析不总是遵循指令的响应、将结果持久化到数据库，并优雅地处理各种失败模式，且不阻塞用户。在用户请求分析媒体 outlet 时，CDDBS 首先需要获取内容，利用 SerpAPI 的 Google News 引擎来抓取最新文章。文章详细介绍了每个阶段的实际代码。
构建 CDDBS — 第二部分：分析管道内部
出处: Building CDDBS — Part 2: Inside the Analysis Pipeline
发布: 2026年3月17日
📄 English Summary
Building CDDBS — Part 2: Inside the Analysis Pipeline
CDDBS employs a six-stage background pipeline to address common issues faced in real systems. Most tutorials on large language models (LLMs) only demonstrate how to call an API and print the response, while real systems require fetching data from external sources, constructing prompts that constrain output formats, parsing responses that do not always follow instructions, persisting results to a database, and gracefully handling various failure modes without blocking the user. When a user requests an analysis of a media outlet, CDDBS first needs to fetch content, utilizing SerpAPI's Google News engine to retrieve recent articles. The article provides actual code for each stage of the pipeline.