Translations:FACTS About Building Retrieval Augmented Generation-based Chatbots/31/en

    From Marovi AI
    Revision as of 09:55, 17 February 2025 by FuzzyBot (talk | contribs) (Importing a new version from external source)
    (diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

    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.