What Data Looks Like for This Category
Data for this category comes from manufacturing enterprise ERP systems, process route documents, equipment capacity ledgers, staff shift schedules, work-in-progress inventory records, and supplier arrival notices. Daily processed documents include sales orders, master production schedules, material requirements plans, weekly scheduling sheets, work orders, material shortage lists, and delivery change notices. Fields cover order numbers, material codes, capacity values, shift time periods, delivery dates, and material quantities. Units include pieces, hours, days, kilograms, and others. Data update frequency varies with business rhythm: daily capacity and inventory updates, weekly scheduling plan adjustments, and real-time updates for order changes, material shortages, or equipment failures.
What Constraints Do These Characteristics Impose on Vector Models and Indexes
Multi-source, high-frequency updated data requires indexes to support incremental updates, avoiding performance overhead from full reconstruction. Document types include structured tables such as scheduling sheets and unstructured text such as work orders. Mixed content chunking logic must be adapted to prevent splitting key process-related information. Fields include multiple formats including numbers, dates, and text. Vector models must support semantic encoding of mixed fields to ensure matching accuracy during inspection and review. The business scenario has high requirements for the relevance of recalled content. The volume of irrelevant data recalled must be limited to avoid interfering with review judgments.
Configuration Settings
This configuration is based on FastGPT v4.17.0, with specifics as follows:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the paragraph density of production planning documents, avoids splitting key process information in work orders and scheduling sheets |
| Overlap Length | 50–80 characters | Retains cross-chunk process connection information, prevents missing associated logic during inspection and review |
| Similarity Threshold | 0.75–0.85 | Filters low-match redundant scheduling data, ensures recalled content is strongly relevant to review requirements |
| Single Search Limit | Top 6 entries | Adapts to the multi-constraint scenario of shop scheduling, avoids recalling too many irrelevant work orders or material data |
maxToken | 4096 | Adapts to the overall length of production planning documents, ensures complete transmission of scheduling constraints and inspection rules |
defaultToken | 1024 | Balances the detail of single recalled content and contextual coherence, adapts to the review requirements of work orders and material shortage lists |
Three Common Mistakes
- Abnormally increased number of document chunks after indexing. The interface shows a discrepancy between the original chunk count and the final chunk count. Cause: The automatic chunk optimization switch was not turned off. The system repeatedly splits cell content for the table structure of scheduling sheets.
- Vector model batch training cannot be triggered. Only single-file parameter adjustment and upload are supported. Cause: The batch training switch for batch indexing was not turned on. The system only processes vector generation tasks for single files by default.
- Existing queryable scheduling data cannot be recalled after upgrading the version. Cause: The chunking rules of the new version vector model have been updated. The chunking logic of the original index does not match the new rules. The index must be regenerated.
How to Confirm the Configuration Is Correct
- Upload a typical weekly scheduling sheet. Check the chunk preview interface to confirm that key process rows are not incorrectly split.
- Submit an inspection and review query. Check that the number of segments in the recalled results matches expectations, with no duplicate content.
- Trigger an incremental index. Verify whether newly added delivery change notices are automatically included in the index library.
- Check the vector model configuration logs. Confirm that the values of
maxTokenanddefaultTokenmatch the preset settings.
The values provided are common starting points, and should be evaluated against available samples.