实用的文本到SQL的数据分析
Co-authors: Co-authored byAlbert Chen, Co-authored byManas Bundele, Co-authored byGaurav Ahlawat, Co-authored byPatrick Stetz, Co-authored byZhitao (James) W., Co-authored byQiang Fei, Co-authored byDonghoon (Don) Jung, Co-authored byAudrey Chu, Co-authored byBharadwaj Jayaraman, Co-authored byAyushi Panth, Co-authored byYatin Arora, Co-authored bySourav Jain, Co-authored byRenjith Varma, Co-authored byAlex Ilin, Co-authored byIuliia Melnychuk 🇺🇦, Co-authored byChelsea C., Co-authored byJoyan Sil, and Co-authored byXiaofeng Wang
合著者：合著者Albert Chen，合著者Manas Bundele，合著者Gaurav Ahlawat，合著者Patrick Stetz，合著者Zhitao (James) W.，合著者Qiang Fei，合著者Donghoon (Don) Jung，合著者Audrey Chu，合著者Bharadwaj Jayaraman，合著者Ayushi Panth，合著者Yatin Arora，合著者Sourav Jain，合著者Renjith Varma，合著者Alex Ilin，合著者Iuliia Melnychuk 🇺🇦，合著者Chelsea C.，合著者Joyan Sil，以及合著者Xiaofeng Wang
In most tech companies, data experts spend a significant amount of their time helping colleagues find data they need – time that could be spent on complex analysis and strategic initiatives. This bottleneck not only frustrates data teams but also creates delays for business partners waiting for crucial insights.
在大多数科技公司，数据专家花费大量时间帮助同事找到他们需要的数据——这些时间本可以用于复杂分析和战略计划。这一瓶颈不仅使数据团队感到沮丧，还为等待关键见解的业务合作伙伴造成延误。
Generative AI presents an opportunity to improve this workflow. As part of our data democratization efforts at LinkedIn, we've developed SQL Bot, an AI-powered assistant integrated within our DARWIN data science platform. This internal tool transforms natural language questions into SQL: it finds the right tables, writes queries, fixes errors, and enables employees across functions to independently access the data insights they need under the appropriate permissions. Behind the scenes, SQL Bot is a multi-agent system built on top of LangChain and LangGraph.
生成式 AI 为改善这一工作流程提供了机会。作为我们在 LinkedIn 的数据民主化努力的一部分，我们开发了 SQL Bot，这是一种集成在我们 DARWIN 数据科学平台中的 AI 驱动助手。这个内部工具将自然语言问题转化为 SQL：它找到正确的表，编写查询，修复错误，并使各职能的员工在适当的权限下独立访问所需的数据洞察。在后台，SQL Bot 是一个建立在 LangChain 和 LangGraph 之上...