What data looks like for this category
Production ledgers and report data for electric motor and generator manufacturing originate from winding, insulation, final assembly, and testing departments of electric motor factories and power equipment enterprises. Daily documents include winding process cards, insulation treatment records, dynamic balance reports, voltage withstand insulation test records, and type and factory inspection reports. Data sources cover electromagnetic design data, copper wire and insulating material batch records, outputs from winding equipment, balancing machines, electrical test benches, and ERP/MES system data. Updates occur once per unit or batch, with synchronized updates for each form change. Documents include structured fields such as winding turns, impregnation temperature, test voltage, and temperature rise values, paired with fixed units.
Constraints on document parsing and chunking
Documents in this category mix structured tables and long-form text descriptions. A single test report may include multi-page parameter lists, and mixed-format upload scenarios are common. Ledger data for each piece of equipment must retain core parameters without loss. Chunking processes must not break table associations or field integrity. High-frequency, small-batch uploads require parsing workflows that can quickly adapt to different document formats. Large PDF reports may cause parsing timeouts. The system must also correctly extract merged cells and multi-column parameters from Excel ledgers.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Documents for electric motor manufacturing often contain long parameter lines and table sections. This range preserves the integrity of core fields such as winding turns and test voltage |
chunkSplitter | Split by paragraph and table boundaries | Documents in this category mix process descriptions and structured test data, to avoid splitting that breaks table associations |
customPdfParse | Enabled | Motor test reports mostly use standardized PDF formats. Enabling custom parsing allows accurate extraction of tables and parameter blocks |
UPLOAD_FILE_MAX_SIZE | 20 MB | Type test reports for a single batch of motors can reach 10–15 MB, reserving reasonable upload space |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large-size PDF test reports takes a long time, to avoid timeout interruptions |
chunkSettingMode | Manual configuration | Documents in this category have large structural differences. Manual configuration can adapt to different splitting needs for process cards and test reports |
Three common configuration errors
- Issue: Uploading an Excel motor manufacturing ledger returns some empty fields. The cause is that custom table parsing is not enabled. The default parsing logic incorrectly splits merged cells and cross-paragraph parameters, leading to loss of core fields.
- Issue: Parsing Word process files larger than 10 MB times out and fails. The cause is that the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The default short timeout duration cannot complete parsing of large files. - Issue: Searching for winding turn parameters returns only half of the numerical value. The cause is that
chunkSizeis set too small. This splits a single complete turn parameter into two chunks, breaking the association between the parameter and its unit.
How to verify correct configuration
- Upload a single 12 MB type test report. Check the parsing progress in the FastGPT v4.17.0 task management interface. Confirm the task completes within the set timeout period.
- Extract parsed chunked text. Check that core parameters such as winding turns and test voltage appear fully within a single chunk, with no split breaks.
- Upload mixed-format test documents: PDF process cards, Excel insulation ledgers, Word dynamic balance reports. Confirm that tables and paragraphs of each format are correctly split, with no format errors.
- Enter specific field keywords for retrieval. Confirm that search results include complete parameter context and associated descriptions.
The values provided are common starting points and should be evaluated against the reader's own samples.