Build an AI matching engine with Docling, Granite & OpenShift AI

Summary

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.

Takeaways

1. 1

   Docling recovers reading order from PDFs by using an AI model trained on 150K samples to identify bounding boxes and semantic labels, then maps PDF Tm coordinates to these boxes.

2. 2

   PeopleMesh mitigates embedding dilution through a weighted scoring algorithm that combines cosine similarity with 7 additional information channels and penalty multipliers rather than chunking inputs.

3. 3

   The matching pipeline first decomposes a user query using an LLM to extract intent, entity type, country, and language, then applies structured filtering before semantic vector search.

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