--- title: "MM-RAG (Multimodal RAG) — 多模态RAG" source: "https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG" wiki: "systems-analysis.info/int" article: "MM-RAG_(Multimodal_RAG)_—_多模态RAG" language: "zh" categories: - "Category:Chinese" - "Category:Large language models" - "Category:Prompt engineering" revision_id: 4066 wiki_created_at: 2026-09-06T23:30:00Z wiki_modified_at: 2026-09-06T23:30:00Z downloaded_at: 2026-09-07T23:00:41Z --- # MM-RAG (Multimodal RAG) — 多模态RAG **MM-RAG** (英文：*Multimodal Retrieval-Augmented Generation*，多模态检索增强生成) 是经典RAG范式的扩展。在这种范式中，LLM不仅使用文本，还利用视觉数据（图像、图表、表格、图形）来生成回答。多模态检索能够查找并关联不同形式的证据，通过依赖外部来源，并将信息精确定位到页面片段和区域（*bounding boxes*，边界框），从而降低了产生幻觉的风险^{[\[1\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-lewis2020-1)[\[2\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-visrag2024-2)}。 MM-RAG 对于大部分信息以非文本形式（如页面布局、图表、表格结构）呈现的文档尤其有用。在这类情况下，经典的纯文本 RAG 常常会丢失重要的上下文元素^{[\[3\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-docvqa2021-3)[\[4\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-textvqa2019-4)}。 ## 背景与待解决的问题经典 RAG 处理的是文本段落，无法识别视觉结构（如元素布局、图片标题、图表坐标轴）。MM-RAG 填补了这些空白：它提取结构化元素（文本、表格、带坐标的图像），将其索引到向量空间中，并结合来自不同模态的证据^{[\[5\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-layoutlm2020-5)[\[6\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-clip2021-6)}。 ## MM-RAG 架构 MM-RAG 的工作流在经典 RAG 的基础上增加了视觉数据处理和模态对齐的步骤：**数据注入 (Ingestion) → 索引 (Indexing) → 多模态检索 (Multimodal Retrieval) → 融合与重排 (Fusion & Reranking) → 可追溯生成 (Generation with Tracing)**。 1. **数据采集与预处理 (Ingestion)**：输入 PDF、扫描件或图像。系统执行 OCR 和页面**布局分析**，以识别并划分区域：段落、标题、表格、图像及其坐标。常用工具包括 LayoutLM 系列模型和 LayoutParser 工具库；验证和训练通常依赖于 PubLayNet 和 DocLayNet 数据集^{[\[7\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-publaynet2019-7)[\[8\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-doclaynet2022-8)[\[9\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-layoutparser2021-9)}。 2. **区域分割**：提取视觉对象（图表、表格、插图、标题）。为提高鲁棒性，可采用无需 OCR 的模型（如 Donut）或 OCR+VLM 的组合流水线^{[\[10\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-donut2021-10)}。 3. **索引 (Vector Index)**：将文本块和视觉元素（图像或其描述）转换为向量表示，并存入向量数据库。为了建立统一的*文本↔图像*空间，通常使用 CLIP 或 SigLIP；在生产环境中，多模态/多向量索引（一个对象对应多个向量）更为便捷^{[\[6\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-clip2021-6)[\[11\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-siglip2023-11)[\[12\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-milvus-mv-12)}。 4. **多模态检索与重排**：结合文本和视觉搜索；将候选结果（段落、表格、图像/区域）合并，并使用更“重”的模型（如交叉编码器/LLM 重排器）进行重排，以提高准确性^{[\[13\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-cohere-rerank-13)}。 5. **上下文打包与生成**：将筛选出的片段输入 LLM/VLM。如果模型是多模态的（如 GPT-4V/4o），图像可以直接输入；如果使用纯文本 LLM，图像则需要预先转换为详细描述^{[\[14\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-gpt4v-14)[\[15\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-gpt4o-15)}。 6. **追溯与引用**：生成的回答附带可点击的引用，不仅链接到文档/页面，还能精确定位到具体区域（坐标）。这提升了*grounding*（信息溯源）水平和用户信任度^{[\[2\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-visrag2024-2)}。 ## 质量评估与指标 MM-RAG 的有效性从提取、检索和生成三个层面进行评估。 - **视觉数据提取质量**：在 DocLayNet/PubLayNet 等数据集上评估 OCR 准确率（WER/CER）和布局分析质量（mAP/Precision/Recall）^{[\[8\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-doclaynet2022-8)[\[7\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-publaynet2019-7)}。 - **检索质量**：采用标准信息检索指标：Recall@K、Precision@K、MRR；对于多模态，需分别评估各模态及组合后的效果。 - **端到端回答质量**：使用自动化指标 *faithfulness*/*groundedness* 以及人工评估。实践中常使用 RAGAS/TruLens/DeepEval 等框架^{[\[16\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-ragas-16)}。 - **基准测试 (Benchmarks)**： - **DocVQA**：针对文档图像的问答^{[\[3\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-docvqa2021-3)}。 - **TextVQA**：需要读取图像中文本才能回答的问题^{[\[4\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-textvqa2019-4)}。 - **InfographicVQA**：针对信息图表的问答^{[\[17\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-infographicvqa2021-17)}。 - **ChartQA**：针对图表的问答，需要逻辑推理^{[\[18\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-chartqa2022-18)}。 - **MMDocRAG**：用于文档问答（DocQA）的多模态 RAG 基准测试，包含多页文档和跨模态证据链^{[\[19\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-mmdocrag2025-19)}。 ## 组件对比表 | 组件 | 实现方案 | 优点 | 缺点 / 风险 | 选择建议 | |------------|--------------------------------------------------------|---------------------------------------------------------------------------------|------------------------------------------------------------|-------------------------------------------------------------------------------| | OCR | Tesseract / PaddleOCR / 云服务 API | 本地部署方案保护隐私且可控；云服务则提供开箱即用的高精度。 | 复杂布局可能导致识别错误；API 方案有成本和合规性要求。 | 涉密数据采用本地 OCR；追求最高精度则使用云服务（若合规）。 | | 布局分析 | 规则 / 机器学习模型 (LayoutLM, LayoutParser) | 规则对标准化模板简单有效；机器学习对多样化布局鲁棒性强。 | 规则在处理新布局时容易失效；机器学习则需要计算资源和数据。 | 标准化表单使用规则；多样化文档库则采用机器学习。 | | 图像向量化 | CLIP / SigLIP / 无需 OCR 的描述生成 (Donut/Pix2Struct) | CLIP/SigLIP 提供统一的*文本↔图像*潜在空间；无需 OCR 的方法摆脱了对 OCR 的依赖。 | CLIP 无法读取图像内的文本；而文本描述可能会歪曲原意。 | CLIP/SigLIP 用于基础多模态搜索；对于质量较差的扫描件，可采用无需 OCR 的方法。 | | 结果融合 | 按分数排序 / 按模态分配配额 / LLM 重排器 | 重排器能显著提高上下文选择的准确性。 | 增加延迟和成本。 | 高精度场景；简单方法适用于概念验证 (PoC)。 | | 存储/索引 | 单向量 / 多向量 (文本+图像) / 混合索引 (BM25+向量) | 多向量索引能覆盖同一对象的不同表示；混合搜索则能有效处理关键词和代码。 | 方案和更新流程更复杂。 | 适用于混合数据和有严格服务等级协议 (SLA) 的生产系统。 | MM-RAG 关键组件与方法对比 ## 实践说明 - **混合搜索** (BM25 + 向量)：事实上已成为提高特定术语/代码检索召回率和准确率的标准方法^{[\[20\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-weaviate-hybrid-20)}。 - **重排**：使用交叉编码器/LLM进行重排，可以在生成前过滤掉“垃圾”候选，从而节省 token^{[\[13\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-cohere-rerank-13)}。 - **现代 VLM 检索器**（例如 ColPali）通过直接索引页面图像，在视觉内容丰富的文档上展现出优势^{[\[21\]](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_note-colpali2024-21)}。 ## 参见 - Retrieval-Augmented Generation - 向量数据库 - Embedding - GraphRAG ## 参考文献 - Lewis, P. et al. (2020). *Retrieval‑Augmented Generation for Knowledge‑Intensive NLP Tasks*. NeurIPS. arXiv:2005.11401. - Gao, L. et al. (2023). *Precise Zero‑Shot Dense Retrieval without Relevance Labels (HyDE)*. ACL 2023. arXiv:2212.10496. - Mei, L., Mo, S., Yang, Z., Chen, C. (2025). *A Survey of Multimodal Retrieval‑Augmented Generation*. arXiv:2504.08748. - Abootorabi, M.M. et al. (2025). *Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval‑Augmented Generation*. Findings of ACL 2025. ACL Anthology. - Yu, S. et al. (2024). *VisRAG: Vision‑based Retrieval‑augmented Generation on Multi‑modality Documents*. arXiv:2410.10594. - Cho, J. et al. (2024). *M3DocRAG: Multi‑modal Retrieval is What You Need for Multi‑document QA*. arXiv:2411.04952. - Tanaka, R. et al. (2025). *VDocRAG: Retrieval‑Augmented Generation over Visually‑Rich Documents*. CVPR 2025. arXiv:2504.09795 • CVF Open Access. - Dong, K. et al. (2025). *MMDocRAG: Benchmarking Retrieval‑Augmented Multimodal Generation for Document Question Answering*. arXiv:2505.16470. - Wasserman, N. et al. (2025). *REAL‑MM‑RAG: A Real‑World Multi‑Modal Retrieval Benchmark*. arXiv:2502.12342. - Faysse, M. et al. (2024). *ColPali: Efficient Document Retrieval with Vision Language Models*. arXiv:2407.01449. - Radford, A. et al. (2021). *Learning Transferable Visual Models From Natural Language Supervision (CLIP)*. ICML 2021. arXiv:2103.00020. - Tschannen, M. et al. (2025). *SigLIP 2: Multilingual Vision‑Language Encoders with Improved Semantic Understanding, Localization, and Dense Features*. arXiv:2502.14786. - Xu, Y. et al. (2020). *LayoutLM: Pre‑training of Text and Layout for Document Image Understanding*. KDD 2020. DOI • arXiv:1912.13318. - Huang, Y. et al. (2022). *LayoutLMv3: Pre‑training for Document AI with Unified Text and Image Masking*. arXiv:2204.08387. - Zhong, X., Tang, J., Jimeno‑Yepes, A.J. (2019). *PubLayNet: Largest Dataset Ever for Document Layout Analysis*. arXiv:1908.07836. - Pfitzmann, B. et al. (2022). *DocLayNet: A Large Human‑Annotated Dataset for Document‑Layout Analysis*. arXiv:2206.01062. - Kim, G. et al. (2021). *OCR‑free Document Understanding Transformer (Donut)*. arXiv:2111.15664. - Shen, Z. et al. (2021). *LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis*. arXiv:2103.15348. - Singh, A. et al. (2019). *Towards VQA Models That Can Read (TextVQA)*. CVPR 2019. arXiv:1904.08920. - Mathew, M. et al. (2021/2022). *DocVQA / InfographicVQA: Datasets for VQA on Document Images and Infographics*. WACV 2021 / WACV 2022. CVF • arXiv:2104.12756. - Masry, A. et al. (2022). *ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning*. Findings of ACL 2022. ACL • arXiv:2203.10244. - Liu, F. et al. (2022). *DePlot: One‑shot Visual Language Reasoning by Plot‑to‑Table Translation*. arXiv:2212.10505. - Wang, P. et al. (2024). *Qwen2‑VL: Enhancing Vision‑Language Model’s Capabilities in OCR and Chart QA*. arXiv:2409.12191. - Es, S. et al. (2023). *RAGAS: Automated Evaluation of Retrieval‑Augmented Generation*. arXiv:2309.15217. ## 注释 1. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-lewis2020_1-0) Lewis, P., Perez, E., et al. (2020). *Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks*. NeurIPS. arXiv:2005.11401. 2. ↑ ^{[2.0](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-visrag2024_2-0)} ^{[2.1](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-visrag2024_2-1)} Yu, S. et al. (2024). *VisRAG: Vision-based Retrieval-Augmented Generation on Multi-modality Documents*. arXiv:2407.06437. 3. ↑ ^{[3.0](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-docvqa2021_3-0)} ^{[3.1](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-docvqa2021_3-1)} Mathew, M. et al. (2021). *DocVQA: A Dataset for VQA on Document Images*. WACV. arXiv:2007.00398. 4. ↑ ^{[4.0](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-textvqa2019_4-0)} ^{[4.1](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-textvqa2019_4-1)} Singh, A. et al. (2019). *TextVQA: Towards VQA Models That Can Read*. CVPR. arXiv:1904.08920. 5. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-layoutlm2020_5-0) Xu, Y. et al. (2020). *LayoutLM: Pre-training of Text and Layout for Document Image Understanding*. KDD. DOI. 6. ↑ ^{[6.0](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-clip2021_6-0)} ^{[6.1](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-clip2021_6-1)} Radford, A. et al. (2021). *Learning Transferable Visual Models From Natural Language Supervision (CLIP)*. ICML. arXiv:2103.00020. 7. ↑ ^{[7.0](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-publaynet2019_7-0)} ^{[7.1](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-publaynet2019_7-1)} Zhong, X., Tang, J., Yepes, A. J. (2019). *PubLayNet: Largest Dataset for Document Layout Analysis*. ICDAR. arXiv:1908.07836. 8. ↑ ^{[8.0](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-doclaynet2022_8-0)} ^{[8.1](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-doclaynet2022_8-1)} Pfitzmann, B. et al. (2022). *DocLayNet: A Large Human‑Annotated Dataset for Document‑Layout Analysis*. KDD. DOI / arXiv:2206.01062. 9. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-layoutparser2021_9-0) Shen, Z. et al. (2021). *LayoutParser: A Unified Toolkit for DL‑based Document Image Analysis*. arXiv:2103.15348. 10. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-donut2021_10-0) Kim, G. et al. (2021). *Donut: OCR‑free Document Understanding Transformer*. arXiv:2111.15664. 11. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-siglip2023_11-0) Zhai, X. et al. (2023). *Sigmoid Loss for Language‑Image Pre‑Training (SigLIP)*. ICCV. arXiv:2303.15343. 12. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-milvus-mv_12-0) Milvus Docs. *Multi‑Vector Hybrid Search*. milvus.io/docs/multi-vector-search.md. 13. ↑ ^{[13.0](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-cohere-rerank_13-0)} ^{[13.1](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-cohere-rerank_13-1)} Cohere Docs. *Rerank API*. docs.cohere.com/reference/rerank. 14. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-gpt4v_14-0) OpenAI. *GPT‑4V(ision) System Card*. (2023). PDF. 15. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-gpt4o_15-0) OpenAI. *Hello GPT‑4o*. (2024). openai.com/index/hello-gpt-4o/. 16. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-ragas_16-0) Es, S. et al. (2023). *RAGAS: Automated Evaluation of Retrieval Augmented Generation*. arXiv:2309.15217. 17. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-infographicvqa2021_17-0) Mathew, M. et al. (2021). *InfographicVQA: Understanding Infographics via Question Answering*. ICDAR. arXiv:2104.12756. 18. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-chartqa2022_18-0) Masry, A. et al. (2022). *ChartQA: A Benchmark for Question Answering about Charts*. ACL (Findings). arXiv:2103.16435. 19. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-mmdocrag2025_19-0) Dong, K. et al. (2025). *Benchmarking Retrieval‑Augmented Multimodal Generation for Document QA (MMDocRAG)*. arXiv:2505.16470. 20. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-weaviate-hybrid_20-0) Weaviate Docs. *Hybrid search*. docs.weaviate.io/.../hybrid-search. 21. [↑](https://systems-analysis.info/int/MM-RAG_(Multimodal_RAG)_%E2%80%94_%E5%A4%9A%E6%A8%A1%E6%80%81RAG#cite_ref-colpali2024_21-0) Faysse, M. et al. (2024). *ColPali: Efficient Document Retrieval with Vision‑Language Models*. arXiv:2407.01449.