This configuration is based on FastGPT v4.17.0, and is adapted for the technical document parsing scenario of tooling, fixture and gauge manufacturing.
What this category of data looks like
Daily documents for this category include tooling requirement specifications, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources include product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Updates are performed per project milestones, each product change, and regular calibration. Document fields include datum tolerance (unit: mm), clamping force (unit: kN), calibration cycle (unit: day), and others. Some drawing-type documents embed geometric dimensioning and tolerancing information.
What constraints do these characteristics impose on the document parsing and chunking link
Documents in this category mix text, vector drawings, and metrology data. Parsing must retain the integrity of dimension units, associated fields, and drawing annotations. Multi-version change histories must be split by project batches to avoid mixing content across versions. Associated content between coordinate measuring machine data and acceptance reports must maintain contextual coherence, and chunking must not separate datums and verification results. Regular calibration records must be split along timelines to facilitate traceability of accuracy changes during cross-review. At the same time, structural differences across document types are significant, so chunking boundaries must be adjusted in a targeted manner to avoid combining drawing annotations and operation instructions.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the length of dimension annotations and operation steps in documents of this category, and avoids splitting geometric tolerance descriptions |
chunkSplitter | Split by paragraph + heading | Preserves contextual association of core content such as positioning datums and clamping schemes |
customPdfParse | Enable vector graphic text extraction | Fully extract annotation text and dimension information from fixture and gauge drawings |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time required for large tooling drawings and multi-page calibration records |
chunkSettingMode | Manual mode | Adjust chunking rules for the special structure of documents in this category, and avoids separating associated content via automatic chunking |
Three common mistakes
- When parsing large tooling drawings, the log reports slow operation xxxxms. The cause is failure to adjust the
PARSE_FILE_TIMEOUT_SECONDSparameter. The default timeout duration is insufficient to complete full parsing of multi-page drawings. - After uploading a DOCX document with embedded images, an invalid file prompt is returned. The cause is failure to enable image-associated parsing in
customPdfParse, leading to failure to correctly identify image links. - Chunking results separate datum tolerance and clamping schemes into different chunks. The cause is use of the default
chunkSplitterrule, without chunking by heading + paragraph mode.
How to confirm the configuration is properly set
- A typical fixture drawing document is uploaded, and parsed chunks are checked to confirm retention of the association between datum annotations and clamping schemes.
- The parsing log is reviewed to confirm no timeout errors occur, verifying that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration takes effect. - A gauge operation manual with embedded images is uploaded, and image text is confirmed to be properly extracted and included in chunked content.
- The
chunkSettingModeis switched to manual mode, and chunking rules are verified to follow the heading + paragraph logic.
The values provided here are common starting points, and should be measured against the reader's own samples.