feat(rag): add ViDoRe V3 agentic RAG evaluation with reasoning-based page selectors

[ceberam]

Summary

Introduces an end-to-end evaluation pipeline for a novel chunkless, reasoning-driven RAG approach on the
ViDoRe V3 benchmark. Rather than splitting documents into fixed-size chunks and relying on vector similarity, the approach preserves the DoclingDocument structure, enriches it with AI-generated summaries, and uses an LLM to navigate the document hierarchy and select the most relevant pages per query.

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What's new

Enricher — dual summarisation styles

DoclingEnrichingAgent._summarize_pages gains a style parameter ("sentences" / "keyphrases"). When "keyphrases" is chosen, page-level enrichment stores keyphrase lists in meta.keywords instead of prose summaries in meta.summary. Resume logic and the skip-if-already-enriched guard both respect the chosen style.

Two page-selector strategies

• ReasoningBasedPageSelector — evaluates pages in iterative, sliding-window batches; works with both page-level and element-level enrichment. Includes multi-document support via select_relevant_documents, per-document select_pages, and cross-document rerank_across_documents.
• TreeGuidedPageSelector — traverses the heading hierarchy top-down, drilling into the most promising sections at each step; requires element-level enrichment.

Both selectors are exported from docling_agent.agents.

perfs/ evaluation harness

A four-step pipeline script and YAML config template cover:

1. PDF → DoclingDocument conversion
2. Heading-level normalisation via DoclingEditingAgent
3. Document enrichment (element-level or page-level, sentences or keyphrases)
4. NDCG@10 scoring against ViDoRe ground truth using ranx

The pipeline is fully resumable — Step 3 checkpoints after every page and Step 4 flushes results after every query. The perfs/ README is updated to document both the existing extraction-quality evaluator and the new RAG evaluator side by side.

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