What the Data for This Category Looks Like
This category’s data covers business documents from air conditioner, refrigerator, washing machine and small appliance manufacturers. These include configured BOMs, assembly work instructions, refrigerant charging records, electrical safety test reports, performance spot check reports and rework records. Data sources include ERP/MES systems, component batch ledgers, tightening and charging equipment logs, safety tester data, performance laboratory reports and after-sales fault archives. Data update frequency follows per-unit product, shift or spot check schedules. Individual records include fields such as component numbers, process parameters, test results and batch information. Some fields have clear units attached.
Constraints for Vector Models and Indexes
The multi-source heterogeneous data, high-frequency update rhythm and strong process correlation of this category create multiple constraints for vector models and indexes. Multi-source data has large format differences. Unified embedding processing must be adapted for both structured ledgers and unstructured documents. High-frequency updates require indexes to support incremental synchronization. This avoids excessive resource usage from full index reconstruction. Segmented data for individual products must not be mixed across product models. Indexes must use product model and production batch for dimension isolation. This prevents mismatched process parameters from appearing in retrieval results. Strong compliance requirements for safety and performance test data require retaining field-level associations during vector embedding. This avoids loss of key verification information.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the single-section content length of assembly work instructions and test reports, avoids splitting across process logic |
| Overlap Length | 100–150 characters | Retains contextual association between adjacent process steps, prevents logical breaks in assembly workflows |
| maxToken | 4096 token | Matches the maximum single-page content length of home appliance test reports, ensures complete embedding of test parameters |
| defaultToken | 512 token | Adapts to embedding requirements for short fields such as component numbers and batch numbers, reduces redundant calculations |
| Similarity Threshold | Calibrated based on actual testing | Must be adjusted based on matching accuracy of safety tests and process parameters, avoids mismatching data from the same model but different batches |
| Maximum Single Search Results | Top 10 entries | Controls the scale of retrieval results, adapts to rapid verification scenarios during mixed-model assembly lines |
Three Common Mistakes
- After upgrading the platform version, importing a CSV file with the same configuration causes chunking errors. The interface displays the prompt "Field parsing failed: Unclosed newline character exists". The cause is that the new version adjusted CSV field parsing rules, and does not support process parameter fields that contained newline characters in older versions.
- Knowledge base index tasks get stuck at 90% progress for an extended period, and no complete index file is generated. The cause is that incremental index mode is not enabled. Full synchronization of high-frequency updated after-sales fault data causes the single task to time out without completion.
- Retrieval results include assembly work instructions for different appliance models. This creates misassembly risks during audits. The cause is that index dimension filtering rules are not set based on product model, and process data from different batches is not isolated.
How to Verify Proper Configuration
- Upload a single home appliance configured BOM document. Check if the chunked fragments completely retain component numbers and assembly sequence information. Adjust chunk length and overlap length to meet business requirements.
- Import test data containing parameters for different models. Run a retrieval test. Verify that results only return content related to the target model. Confirm that dimension filtering rules take effect.
- Submit batch rework record data for indexing. Check if the task progress completes within a reasonable time frame. Adjust incremental synchronization parameters to avoid timeouts.
- View vector embedding logs. Confirm that unit information for structured fields such as torque and charging volume is fully retained in the embedded vectors. No fields are lost.
The values provided here are common starting points. They should be evaluated against the reader's own samples.