Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/31/zh: Difference between revisions

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'''Handling multi-modal data''': Enterprise data is multi-modal. Handling structured, unstructured, and multi-modal data is crucial for a versatile RAG pipeline. From our experience, if the structure of the document is consistent and known apriori (like those found in EDGAR databases for SEC filings data in financial earnings domain that Scout bot was handling), implementing section-level splitting, using the section titles and subheadings and incorporating those in the context of chunks improves retrieval relevancy. We also found solutions like Unstructured.io, which specialize in extracting and structuring content from PDFs, helpful in parsing and chunking unstructured documents with context.

处理多模态数据：企业数据是多模态的。处理结构化、非结构化和多模态数据对于多功能的RAG管道至关重要。根据我们的经验，如果文档的结构是一致且已知的（例如在金融收益领域中Scout bot处理的SEC文件数据的EDGAR数据库中找到的那些），实施基于章节的分割，使用章节标题和副标题，并将其纳入块的上下文中，可以提高检索的相关性。我们还发现像Unstructured.io这样的解决方案在从PDF中提取和结构化内容方面很有帮助，这对于解析和分块具有上下文的非结构化文档非常有用。