Extracting content, and retaining all the semantic information presented in texts, tables, charts, graphs, and other visual elements in a manner that is most suitable for consumption by LLMs, especially when used in conjunction with RAG is still a hard problem.
There are many compnanies, research teams and open source projects attempting to solve this at the document level and as a pipeline.
Some of the ones that are the top of my head right now (2025-08) are:
Open Source Projects
- Docling by IBM Zurich Research, and SmolDocling; see (Team 2024)
- Dolphin by ByteDance; see (Feng et al. 2025)
- roe-ai/vectorless by AI SaaS -- "PDF chatbot that uses no vector embeddings or traditional RAG. Instead, it leverages Large Language Models for intelligent document selection and page relevance detection, providing a completely stateless and privacy-first experience."
Supporting Libraries
- pypdf2
- GitHub - pypdfium2-team/pypdfium2: Python bindings to PDFium, reasonably cross-platform. (mentioned in docling paper, as well as used in datalab-to/marker)
- #todo (one used in tensorlake)
Companies
References
- Team (2024). Docling Technical Report.
- Feng, Wei, Fei, Shi, Han, Liao, Lu, Wu, Liu, Lin, others (2025). Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting.