What the data for this category looks like
Marketing data for automated equipment primarily comes from equipment technical manuals, official specification sheets, and configuration descriptions on product promotional pages. Data updates are triggered by new model iterations or core parameter adjustments, with no fixed cycle. Individual documents typically combine structured tables and paragraphs, including fields such as complete model numbers, rated power, operating speed, installation dimensions, and applicable working conditions. Most use industrial standard units like millimeters, kilowatts, and revolutions per minute. Some documents also include detailed illustrations and text explanations for component disassembly.
What constraints these characteristics impose on model integration and configuration
Automated equipment has numerous structured parameter fields with strict unit requirements. Configure preset field mapping rules during model integration to avoid parameter matching errors. Set up scheduled synchronization tasks for data sources with no fixed update cycle to ensure the latest equipment parameters are used during model calls. Split text blocks and image-text indexes during the parsing process for documents combining text and images, to prevent missed parameter extractions. Add unified unit conversion logic during model preprocessing to handle the diversity of industrial standard units, ensuring consistent output results.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Automated equipment technical documents often include long tables and multi-page images, leading to long parsing times |
maxContext | 8000–12000 characters | The context length of a single set of equipment parameters is large, so complete parameter groups must be retained to ensure accurate model understanding |
recall_count | Top 6–8 entries | Equipment marketing content needs to cover multi-dimensional parameters including complete machines, components, and working conditions. Too many entries will increase context redundancy |
similarity_threshold | 0.75–0.85 | High precision is required for equipment parameters, so low-match irrelevant documents must be filtered out |
chunk_size | 1000–1500 characters | The structured block length of equipment parameters is appropriate, facilitating accurate recall and parameter extraction by the model |
UPLOAD_FILE_MAX_SIZE | 500 MB | Technical manuals for large industrial equipment may include multiple chapters, resulting in large single-file sizes |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Unable to select an integrated automated equipment-specific model in workflow configuration. Cause: The data source was not specified as the equipment marketing document library during model integration, only a general knowledge base was bound.
- Symptom: Mixed units appear in equipment parameters returned by the model. Cause: No unified unit conversion preprocessing rule was configured, and the model was directly called for parameter extraction.
- Symptom: Search response times out. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and model calls were triggered before long document parsing completed.
How to confirm configuration is complete
- Upload a typical automated equipment technical manual, use the parsing preview function to check if extracted text blocks fully retain parameter fields and corresponding units.
- Trigger a model call, verify that returned results include core marketing-related equipment parameters, adjust the
similarity_thresholdto match business precision requirements. - Run a test workflow, confirm that the model can correctly associate equipment parameters with marketing scenario requirements, adjust the
recall_countto balance response speed and recall completeness. - View model call logs, confirm no abnormal errors occur during file upload, parsing, and model call stages, fine-tune corresponding parameters based on actual call conditions.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.