Netflix Graph Search 的 AI 演进：从结构化查询到自然语言

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By Alex Hutter and Bartosz Balukiewicz

由 Alex Hutter 和 Bartosz Balukiewicz 撰写

Our previous blog posts (part 1, part 2, part 3) detailed how Netflix’s Graph Search platform addresses the challenges of searching across federated data sets within Netflix’s enterprise ecosystem. Although highly scalable and easy to configure, it still relies on a structured query language for input. Natural language based search has been possible for some time, but the level of effort required was high. The emergence of readily-available AI, specifically Large Language Models (LLMs), has created new opportunities to integrate AI search features, with a smaller investment and improved accuracy.

我们之前的博客文章（第一部分，第二部分，第三部分）详细介绍了 Netflix 的 Graph Search 平台如何应对在 Netflix 企业生态系统中跨 federated 数据集搜索的挑战。虽然高度可扩展且易于配置，但它仍依赖结构化查询语言作为输入。基于自然语言的搜索已经可能有一段时间了，但所需努力程度很高。随时可用的 AI 的出现，特别是 Large Language Models (LLMs)，创造了以较小投资和更高准确性集成 AI 搜索功能的新机会。

While Text-to-Query and Text-to-SQL are established problems, the complexity of distributed Graph Search data in the GraphQL ecosystem necessitates innovative solutions. This is the first in a three-part series where we will detail our journey: how we implemented these solutions, evaluated their performance, and ultimately evolved them into a self-managed platform.

虽然 Text-to-Query...