What the Data for This Category Looks Like
Data comes from bus operation dispatch systems, on-board GPS terminals, IC card fare collection equipment, and station management systems. Systems generate single records per operating shift. Daily aggregation creates full daily route ledger documents. Ledger documents include fields such as route number, shift number, departure time, arrival time, passenger trips, driving mileage, number of stops, and energy consumption value. Time units are hours, minutes, and seconds. Mileage uses kilometers as the unit. Passenger volume uses trips as the unit. Energy consumption uses liters or kilowatt-hours as the unit.
Constraints Imposed on Vector Models and Indexing
Single shift ledger records have small individual size but large batch volume. Daily aggregated data grows steadily. Index construction must support incremental updates to match real-time shift update rhythms, avoiding repeated full index calculations. Multiple fields carry different business semantics and measurement units. Vector models must adapt to semantic encoding of structured fields, avoiding encoding bias from different units that could reduce retrieval accuracy. Ledger data has clear relationships, such as shifts on the same route or driven by the same operator. Indexes must support field-based filtered retrieval to quickly locate target ranges of operational data.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Bus transit ledgers have many fields per single record. Chunk length covers full operating information for one shift to avoid semantic fragmentation |
| Overlap Length | 100–150 characters | Retain overlapping content between adjacent chunks to maintain semantic coherence for a single shift ledger |
| Similarity Threshold | 0.75–0.85 | Differentiate operational differences between shifts on the same route, avoid accidental recall of unrelated ledgers |
| Single Search Limit | Top 10 results | Ledger aggregation needs to cover core shift data. 10 results cover major operational scenarios |
maxToken | 8192 | Adapt to the total length of daily aggregated bus transit ledgers, meet vector encoding requirements for long texts |
defaultToken | Use platform default configuration or custom access key | Used to identify embedding model call permissions, adapt to access requirements for different models |
| Maximum Context | 3000–4000 characters | Recalled ledger data needs to support context requirements for report generation, avoid exceeding model processing limits |
Three Common Errors
- Phenomenon: After calling the batch upload interface, the interface displays "Indexing" for an extended period with no progress updates. Cause: Incremental index trigger rules are not configured, and full index loading of historical ledger data exceeding processing capacity causes index construction to stall.
- Phenomenon: Calls to embedding-related functions return permission errors, prompting that the specified model is not authorized for use. Cause: Valid permissions for
defaultTokenare not configured, or an unauthorized model identifier is used. - Phenomenon: Recalled ledger data has low matching degree with retrieval requirements, with large result deviations. Cause: Similarity threshold is set incorrectly. A threshold that is too high results in too few recall results, while a threshold that is too low introduces irrelevant data.
How to Confirm Proper Configuration
- Upload a single day's ledger data, check the platform's index logs to confirm vector encoding is completed with no error messages.
- Enter business-aligned search keywords, such as "Route 10 daily passenger status", verify that the recalled result fields match the actual content of the ledger data.
- Adjust the similarity threshold and initiate a new search, confirm that the number of recalled results changes as expected with the threshold.
- Check the platform's index statistics panel to confirm that the number of indexed documents matches the number of uploaded ledgers.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.