STRUCTURED PDF EXTRACTOR FOR RAG CLAIMS ~300 PAGES/S ON CPU
A new C-based PDF extractor with Python bindings outputs structured JSON (geometry, typography, headings) and claims ~300 pages/second on CPU—about 30x faster t...
A new C-based PDF extractor with Python bindings outputs structured JSON (geometry, typography, headings) and claims ~300 pages/second on CPU—about 30x faster than pymupdf4llm. It targets high-volume RAG pipelines with layout-aware chunking; no OCR or image extraction yet, and external benchmarks are not provided.
Faster, CPU-only parsing can remove ingestion bottlenecks in large RAG/document pipelines.
Structured layout metadata enables smarter chunking and potentially higher retrieval quality than plain text splits.
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terminal
Benchmark end-to-end indexing latency and retrieval accuracy vs existing parsers (pymupdf4llm/docling) on your corpora.
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terminal
Test scanned/image-heavy PDFs and define OCR fallback routing, plus thresholds for switching parsers.
Legacy codebase integration strategies...
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Map its JSON schema to your current chunker and filters; validate header/footer removal and bbox-based boundaries.
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Introduce parser selection logic and keep your existing OCR-capable tool as a fallback for edge cases.
Fresh architecture paradigms...
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Design layout-aware chunking around geometry/typography fields with clear retrieval quality metrics and A/B gates.
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Scale with CPU worker pools, containerize the parser, and persist raw JSON to allow re-chunking without re-parsing.