Table-to-text extraction & RAG Architecture
The process of converting structured data from tables into text formats to facilitate rag (Retrieval-Augmented Generation) and nlp workflows. This concept also encompasses the broader system design required to transform generic Large Language Models into specialized expert systems through retrieval mechanisms.
Models & Trends
- Nanonets OCR Small: A powerful, open-source OCR model featuring 3B parameters.
- Efficiency Trend: A shift toward smaller, highly efficient models (e.g., 3B parameter range) optimized for specific extraction tasks, moving away from larger architectures like Llama OCR and Mistral OCR.
- Application: Targeted at improving the accuracy of rag pipelines by converting complex table structures into machine-readable text.
RAG Pipeline for Expert Systems
- Conceptual Shift: Utilizing RAG to augment base LLMs with external knowledge, effectively creating “expert systems” capable of answering domain-specific queries with higher accuracy and reduced hallucination.
- Implementation Framework: Step-by-step coding approaches using Python to integrate retrieval mechanisms, enabling the LLM to access up-to-date or proprietary information not present in its pre-training data Building a RAG Pipeline for LLM Expert Systems: A Practical Guide.
References
- Nanonets OCR for tables to text for RAG
- Building a RAG Pipeline for LLM Expert Systems: A Practical Guide
Backlinks
- 2026 04 14 Nanonets OCR for tables to text for RAG
Source Notes
- 2026-04-07: LiteParse - The Local Document P