| التخزين/الفهرس |
متجه واحد / متجه متعدد (نص+صورة) / هجين (BM25+متجه) |
المتجه المتعدد يغطي تمثيلات مختلفة لنفس الكائن؛ الهجين ينقذ الكلمات الرئيسية/الرموز. |
تعقيد في البنية والتحديثات. |
أنظمة إنتاجية ببيانات مختلطة واتفاقيات مستوى خدمة (SLA) صارمة. |
}
ملاحظات عملية
- البحث الهجين (BM25 + متجه) هو المعيار الفعلي لزيادة الشمولية والدقة للمصطلحات/الرموز المتخصصة[20].
- إعادة الترتيب بواسطة مشفّر متقاطع/LLM توفر في استهلاك التوكنز عن طريق استبعاد المرشحين "غير المرغوب فيهم" قبل مرحلة التوليد[13].
- مسترجعات VLM الحديثة (مثل ColPali) تظهر تفوقًا في المستندات الغنية بصريًا بفضل الفهرسة المباشرة لصفحات الصور[21].
انظر أيضًا
- Retrieval-Augmented Generation
- قاعدة بيانات متجهية
- Embedding
- GraphRAG
المراجع
- 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.
ملاحظات
- ↑ Lewis, P., Perez, E., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS. arXiv:2005.11401.
- ↑ 2.0 2.1 Yu, S. et al. (2024). VisRAG: Vision-based Retrieval-Augmented Generation on Multi-modality Documents. arXiv:2407.06437.
- ↑ 3.0 3.1 Mathew, M. et al. (2021). DocVQA: A Dataset for VQA on Document Images. WACV. arXiv:2007.00398.
- ↑ 4.0 4.1 Singh, A. et al. (2019). TextVQA: Towards VQA Models That Can Read. CVPR. arXiv:1904.08920.
- ↑ Xu, Y. et al. (2020). LayoutLM: Pre-training of Text and Layout for Document Image Understanding. KDD. DOI.
- ↑ 6.0 6.1 Radford, A. et al. (2021). Learning Transferable Visual Models From Natural Language Supervision (CLIP). ICML. arXiv:2103.00020.
- ↑ 7.0 7.1 Zhong, X., Tang, J., Yepes, A. J. (2019). PubLayNet: Largest Dataset for Document Layout Analysis. ICDAR. arXiv:1908.07836.
- ↑ 8.0 8.1 Pfitzmann, B. et al. (2022). DocLayNet: A Large Human‑Annotated Dataset for Document‑Layout Analysis. KDD. DOI / arXiv:2206.01062.
- ↑ Shen, Z. et al. (2021). LayoutParser: A Unified Toolkit for DL‑based Document Image Analysis. arXiv:2103.15348.
- ↑ Kim, G. et al. (2021). Donut: OCR‑free Document Understanding Transformer. arXiv:2111.15664.
- ↑ Zhai, X. et al. (2023). Sigmoid Loss for Language‑Image Pre‑Training (SigLIP). ICCV. arXiv:2303.15343.
- ↑ Milvus Docs. Multi‑Vector Hybrid Search. milvus.io/docs/multi-vector-search.md.
- ↑ 13.0 13.1 Cohere Docs. Rerank API. docs.cohere.com/reference/rerank.
- ↑ OpenAI. GPT‑4V(ision) System Card. (2023). PDF.
- ↑ OpenAI. Hello GPT‑4o. (2024). openai.com/index/hello-gpt-4o/.
- ↑ Es, S. et al. (2023). RAGAS: Automated Evaluation of Retrieval Augmented Generation. arXiv:2309.15217.
- ↑ Mathew, M. et al. (2021). InfographicVQA: Understanding Infographics via Question Answering. ICDAR. arXiv:2104.12756.
- ↑ Masry, A. et al. (2022). ChartQA: A Benchmark for Question Answering about Charts. ACL (Findings). arXiv:2103.16435.
- ↑ Dong, K. et al. (2025). Benchmarking Retrieval‑Augmented Multimodal Generation for Document QA (MMDocRAG). arXiv:2505.16470.
- ↑ Weaviate Docs. Hybrid search. docs.weaviate.io/.../hybrid-search.
- ↑ Faysse, M. et al. (2024). ColPali: Efficient Document Retrieval with Vision‑Language Models. arXiv:2407.01449.
|