多模态 / NAVIN RESEARCH
Multimodal RAG for Text, Images, and Tables
Index each modality with provenance, retrieve at the right granularity, and cite original evidence.
Index each modality with provenance, retrieve at the right granularity, and cite original evidence.
简要回答: Do not flatten every modality into one lossy text summary. Store extracted text, page or image regions, captions, table structure, and source metadata. Retrieve candidates across modalities, rerank for the question, and provide the original evidence to the model.
实施步骤
- Choose chunks based on document structure and visual boundaries.
- Link derived text and embeddings to immutable source locations.
- Retrieve separately by modality, then rerank jointly.
- Return citations that a user can open at the relevant page or region.
验证清单
- Evaluate retrieval recall before generation quality.
- Test questions answerable only from charts or tables.
- Check that citations support the exact claim.
常见错误
- OCR-only indexing of layout-heavy documents.
- Embedding whole files as one chunk.
- Citing a document title without a page or region.
实际决策
先交付能够通过上述检查的最小设计。记录测试中使用的模型、提示词、工具、数据集与运行时版本，让后续变化可以被比较而不是被猜测。依赖供应商的限制、模型名称、价格或预览功能之前，请重新检查链接文档；这些细节变化往往快于本文的工程原则。
