What this category of data looks like
Manufacturing enterprise equipment departments, workshop maintenance teams, and public utility departments generate daily data including equipment ledgers, inspection checklists, maintenance plans, work orders, failure analysis, spare parts lists, and special equipment inspection records. Data sources include EAM/CMMS systems, equipment sensor data, operator inspection records, maintenance history, spare parts inventory, and statutory inspection information. Update frequencies cover shifts, daily, weekly, monthly cycles, and temporary updates when faults occur. Most documents are structured tables or work orders with fixed fields. Fields include equipment ID, inspection cycle, fault code, spare parts model, and other relevant fields. Units follow equipment-related measurement standards such as millimeters, hours, and counts.
Constraints for document parsing and chunking
Most documents in this category are structured tables and work orders with fixed fields. When creating chunks, retain the association between fields. Do not split a single inspection record or the complete context of a failure analysis. Data is updated frequently and includes statutory inspection documents. The parsing process must balance efficiency and metadata retention. Documents include attachments in multiple formats, such as CSVs exported from sensor data and PDFs with complex tables. Parsing logic must adapt to these varied file formats. Large monthly maintenance plan documents uploaded in batches require reasonable configuration of parsing timeouts and upload limits to prevent task interruptions.
Configuration Settings
These settings apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the field and paragraph lengths of inspection checklists and work orders, and retains the complete context of a single inspection record or failure analysis |
chunkSplitter | Split by paragraph and table | Matches the structured layout of equipment documents, and avoids splitting cross-page inspection items or work order content spanning multiple fields |
customPdfParse | Enabled | Adapts to complex PDF formats of equipment ledgers and special equipment inspection records, and retains original table and font hierarchy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses large monthly maintenance plan PDFs uploaded in batches, and avoids task failure due to large file parsing timeouts |
UPLOAD_FILE_MAX_SIZE | 100 MB | Compatible with full lifecycle document packages for a single device, including CSV attachments exported from sensor data |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types (inspection checklists/failure reports) to meet business scenario requirements |
Three Common Configuration Mistakes
- Phenomenon: Uploading a PDF file results in a parsing failure, with a document parsing error log returned. Cause: The
customPdfParseparameter is not enabled. The default parsing engine cannot recognize the complex table layout of equipment ledgers. - Phenomenon: Uploaded original files cannot be directly passed to the large model for analysis in the node. Only plain text results are received. Cause: Custom parsing rules are not configured. The system converts files to plain text by default before passing them to the large model.
- Phenomenon: An HTTP node calling Feishu multi-dimensional tables returns a permission error. Cause: Feishu application authorization parameters are not correctly configured in the FastGPT node. The request cannot pass the permission check of the Feishu workplace.
How to Verify Correct Configuration
- Upload a single equipment inspection checklist PDF. View the parsed chunk results and confirm that the fields and values of each inspection item are not split.
- Call the document parsing API. Check the returned
customPdfParseparameter status to verify the integrity of PDF table parsing. - Upload a full lifecycle document package for a single device that is under 100 MB. Confirm that the parsing task completes within 600 seconds with no timeout errors.
- Configure an HTTP node to call Feishu APIs. Use the Feishu workplace debugging tool to reproduce the request. Compare the request headers and parameters of the FastGPT node to confirm consistency.
The values provided are common starting points and should be measured against the reader's own samples.