---
title: "Docling"
description: "1 posts about Docling, summarised, each linking to the original."
---

# Docling
> 1 posts about Docling, summarised, each linking to the original.

## Articles

### [Build an AI matching engine with Docling, Granite & OpenShift AI](https://yomu.fyi/post/build-an-ai-matching-engine-with-docling-granite-openshift-ai.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Peter Samouelian, Swati Kale, Francesco Pace
- Published: Oct 12, 2026
- Type: Tutorial

PeopleMesh uses Docling and Granite4:3B LLM on Red Hat OpenShift to extract structured data from PDF resumes and match people across multiple dimensions. Docling converts documents into a DoclingDocument with semantic labels using an AI model trained on 150,000 annotated samples to recover reading order from PDF’s print-oriented encoding, then exports to Markdown for LLM-driven JSON extraction via a system prompt. For matching, PeopleMesh decomposes a user query with an LLM to extract intent, entity type, country, and language, then applies structured filtering followed by semantic vector search via cosine similarity on distilled query embeddings. To mitigate embedding dilution from concatenated concepts, PeopleMesh uses a weighted scoring algorithm that reranks top 100 candidates using 7 information channels including nice-to-have skills and penalty multipliers for missing must-have and negative skills. The scoring provides relative ordering only, not normalized probabilities, and default weights are adjustable via the SearchOptions Java record.