What Data for This Category Looks Like
Data for this category originates from multiple business systems run by transportation bureaus, highway development centers, and other relevant entities. These systems include highway maintenance management platforms, bridge and tunnel monitoring systems, road patrol systems, and traffic flow monitoring tools. Daily generated documents cover maintenance plans, patrol logs, defect resolution tickets, road construction permits, road damage compensation receipts, and more. Some documents contain fields such as timestamps, road section mile markers, defect categories, and handling statuses. Units of measurement include kilometers, square meters, cubic meters, and others. Data is updated on a daily basis.
What Constraints These Characteristics Impose on Vector Models and Indexing
Multi-source, heterogeneous data sources require indexes to support unified vectorization and associated retrieval across systems. Daily data updates demand index configurations that support incremental update logic. This avoids the resource consumption and time delays caused by full reindexing. Document types vary widely: short documents like individual patrol logs are only tens of characters long, while long documents like road construction permits can span thousands of characters. Chunking strategies must balance semantic completeness for short texts and local contextual relevance for long texts. Fields with spatial identifiers such as road section mile markers must be paired with text vectors to improve precise retrieval matching. This prevents documents from unrelated road sections from being included in results.
How to Configure Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to mixed scenarios of short texts such as patrol logs and defect handling tickets, and long texts such as road construction permits in this category. Prevents semantic breaks or loss of local relevance |
| Overlap Length | 100–150 characters | Compensates for semantic gaps at chunk boundaries. Ensures contextual coherence across chunks, and adapts to the process-based descriptions in highway maintenance documents |
| Similarity Threshold | 0.72–0.80 | Matches the semantic similarity characteristics of road property-related documents. Filters low-relevance patrol records or maintenance plans, and improves retrieval accuracy |
| Single Search Limit | Top 8 entries | Balances retrieval recall efficiency and sufficient context for document drafting. Prevents redundant information from interfering with generation |
maxToken | 4000–6000 token | Adapts to vectorization of long documents such as road construction permits and post-retrieval context splicing. Ensures information completeness for document drafting |
defaultToken | 1500–2000 token | Adapts to vectorization needs for short documents such as daily patrol logs and defect handling tickets. Prevents waste of computing resources |
Three Common Errors
- Symptom: A "training order creation failed" error is returned when calling the knowledge base interface, with a 400 status code. Cause: The creation logic for full indexes and incremental indexes is not differentiated. Incremental updates for daily updated data are mistakenly submitted as full training orders, and the update rhythm of the data is not matched.
- Symptom: After uploading a road construction permit document that includes images, the retrieval result does not return associated image information, and the retrieval return fields are empty. Cause: Multimodal vectorization configuration is not enabled. Only the text content within the document is vectorized, and the OCR recognition results of the images and spatial identifiers such as road section mile markers are not associated.
- Symptom: The specified Embedding model or large language model cannot be found in the model selection interface. Cause: The API key and service endpoint of the corresponding model are not entered in the configuration center, or FastGPT v4.17.0 or a later version is not used to adapt to the latest model interfaces.
How to Verify Proper Configuration
- View the knowledge base index update log. Confirm that daily incremental update tasks have triggered at the set frequency, with no failed records.
- Enter a keyword string that includes a road section mile marker and defect type. Check whether the document types and fields returned by retrieval match the daily document structure of this category.
- Test vectorization tasks for documents of different lengths. Confirm that the
maxTokenanddefaultTokenconfigurations do not trigger text truncation errors. - Check whether the multimodal configuration item is enabled. After uploading a document with images, confirm that the retrieval result includes text and spatial information associated with the images.
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.