What the data for this category looks like
Data for this category primarily comes from ERP/MES systems, VIN and configuration systems, supplier batch ledgers, tightening tools, EOL inspection lines, and quality traceability systems. Update frequencies include per-vehicle, real-time, and shift-based updates. Daily documents include production orders, VIN lists, assembly work instructions, tightening data, offline inspection reports, rework records, and consistency release documents. Data fields include VIN codes, part numbers, torque parameters, inspection results, and more, with matching physical units. Torque is measured in newton meters, time in seconds.
Constraints on Document Parsing and Chunking From These Characteristics
Configuration, tightening, inspection and other data linked to a single vehicle must be centered on the VIN for association. When chunking, do not mix unrelated data across different VINs into the same chunk. Redundant information will be returned during retrieval if this occurs. Multi-column structured documents, such as tightening data tables and VIN lists, have multi-field associations. Parsing must retain the relationships between columns. Chunking must match the complete data of a single record or single workstation. Large volumes of documents are updated in real time or per shift. Chunk size must balance retrieval accuracy and storage synchronization efficiency. Oversized chunks will cause parsing timeouts or unreasonable retrieval recall ranges.
The following configurations are based on FastGPT v4.17.0 system settings.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to field associations in multi-column structured documents, avoids breaking data logic when splitting single chunks, and balances retrieval recall and storage overhead |
customPdfParse | Enable mineru API | Adapts to multi-format PDF documents such as assembly work instructions and offline inspection reports, retains column association relationships for charts and tables |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Addresses large-volume documents such as single-batch tightening data and VIN lists, avoids parsing timeout interruptions |
chunkSettingMode | Customize by document type | Configure chunking rules separately for structured documents such as VIN lists and unstructured documents such as work instructions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to shift-collected multi-station inspection report collections, avoids interception due to oversized files during upload |
Three Common Mistakes
- After uploading a multi-column Excel document, retrieval results only match the first two columns of data. The cause is that the parsing mode adapted to multi-column structured documents is not enabled. The default chunking logic only extracts the first two columns of fields.
- Cannot call mineru API when enabling PDF enhanced parsing. The cause is that the mineru API key is not correctly bound in the system configuration, or the
customPdfParseparameter is not enabled. - The parameter is empty after the conditional judgment component processes parsed boolean-type data. The cause is that the original data type association is not retained during chunking, and parsed fields are not correctly mapped to boolean format, causing the component to fail to recognize valid parameters.
How to Verify the Configuration Is Correct
- Upload a multi-column tightening data Excel document, check whether the parsed chunks include all column-associated data.
- Upload an assembly work instruction PDF with tables, confirm that the parsed text retains the row and column structure of the table, with no field misalignment.
- Upload a batch VIN list document of approximately 100 MB, check that the parsing process does not trigger a timeout error, and the number of chunks meets expectations.
- Enter a single VIN code during test retrieval, confirm that the returned chunks only include relevant data associated with that VIN, with no redundant cross-VIN content.
The values provided are common starting points and should be measured against the reader's own samples.