Do not send Docling output straight to embeddings just because conversion finished. First check its status and errors, compare representative extracted pages with their originals, confirm the output format preserves the structure your application needs, and inspect the chunks that will actually be indexed. Docling documents conversion statuses and format trade-offs, but it does not publish a universal accuracy threshold for accepting documents.
What to check before indexing
Conversion status and errors
Start with the conversion result rather than assuming that a returned artifact is complete. Docling’s REST response can report success, partial_success, skipped, or failure, along with errors, processing time, and optional timing details. The Python converter returns a ConversionResult containing the document and conversion metadata when conversion succeeds. Review the status and error details before accepting the document; decide in advance whether partial results should be inspected, retried, or rejected. See the REST API reference and Docling usage documentation.
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Content fidelity against the source
Inspect representative documents from each meaningful source and layout class in your corpus. Compare extracted passages and important values with the original pages, looking for missing, duplicated, garbled, or incorrectly ordered content. Include scans, multi-column pages, tables, and figure-bearing documents when those formats occur and their contents matter to retrieval. This is a corpus-specific engineering check, not a Docling-published acceptance standard or accuracy score.
Structure and serialization
Choose the serialized format according to what later stages need, especially for tables. Docling’s serialization documentation says JSON preserves the full TableData model losslessly, including span fields; HTML represents merged cells with native rowspan and colspan; Markdown flattens merged cells because Markdown tables have no span syntax. With table-heavy sources, inspect structured output or compare the resulting table with the original rather than assuming a Markdown rendering retains merged-header meaning. See Docling serialization.
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Pipeline and OCR assumptions
Record which pipeline and options produced the artifact. The documented native PDF pipeline reads text and embedded bitmap images already reported by docling-parse, but runs no layout, OCR, or table-structure model. Its result can be plain text items in parser order without reading order, headings, or tables. That output may be unsuitable where those structures are essential. OCR language and table extraction settings are configurable, so check scanned and table-bearing documents using the same configuration you intend to run in production. See Docling pipeline options and CLI options.
Chunk content and provenance
Docling supports JSONL chunk output for RAG and offers hybrid or hierarchical chunking, token limits, and a tokenizer option. Examine the chunks actually produced, not only the pre-chunked document. Check that boundaries preserve enough section context, sizes fit the limits of your embedding and retrieval systems, and source and page identity can be traced. Look for content that disappeared or was duplicated during splitting. The appropriate limits and context policy depend on your application; the documentation does not define one universally optimal setting. See Docling chunking documentation.
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Figures and page images
If figures or page images contain information your users need, check both the image export mode and the references in the output. The CLI supports placeholder, embedded, and referenced image modes for formats that can carry images. A placeholder marks an image position; it does not include the image content.
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A practical validation workflow
- Record the conversion configuration. For each artifact, capture input identity, Docling version, pipeline, OCR language and mode, table setting, and output format. These settings are exposed through Docling’s converter and CLI; matching them makes validation results reproducible.
- Apply a status gate. Review the conversion status and error details before indexing. Define how your ingestion process handles partial success, skipped documents, and failures instead of treating every returned result as acceptable.
- Sample each relevant document class. Compare key passages, headings, table cells, and page references with the source across the layouts and source types that matter to your corpus. Set your own acceptance criteria: official documentation does not establish a universal Docling accuracy threshold.
- Inspect the serialized artifact. Confirm that the chosen format retains the structures and location information your application needs. For tables with merged cells, pay particular attention to the documented differences between JSON, HTML, and Markdown serialization.
- Inspect chunk output before embedding. Check chunk size, context, traceable source or page metadata, and whether important information was lost or repeated during splitting. Tune token limits against your actual embedding and retrieval system.
- Keep test cases for later changes. Retain rejected or corrected examples as regression fixtures, then rerun them when pipeline settings change. This is a recommended engineering practice, not a Docling feature guarantee.
Choose settings by what retrieval must preserve
When comparing formats or processing settings, assess them against the same concrete requirements: fidelity for your source material, retention of structure and location metadata, coherent chunks, access to required images, and clear handling of conversion status and errors. Docling documents differences among formats and pipelines, but the best choice depends on your corpus and downstream application. For version-sensitive behavior, use documentation that matches the Docling version you have installed; the REST API reference describes docling-serve v1.21.0, and deployments or defaults may differ. See the REST API reference and Docling documentation.
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