What data for this category looks like
Daily documents for this category typically include maintenance plans, patrol logs, defect disposal forms, road construction permits, road damage compensation forms and similar items. Data sources are highway maintenance management systems, bridge and tunnel monitoring systems, road patrol systems and traffic flow monitoring data. Update frequency is daily.
Document structures combine structured forms and unstructured text. Core fields include patrol time period, road section identifier, defect type, construction scope and similar items. Units include standard business units such as kilometers, yuan, year/month/day.
What constraints do these characteristics impose on the vector model and indexing link
Multi-source systems have different data formats. Unified preprocessing must be completed before connecting to vector models. Otherwise, field mismatch issues will occur.
Daily updated data volume requires incremental indexing logic. This avoids resource waste and delays caused by full indexing.
Document lengths vary widely across different types. Chunking rules must be adjusted for specific cases. This prevents policy clauses from being truncated or work order content from being overly lengthy.
Policy Q&A requires high recall accuracy. Otherwise, it may affect the rationality of road law enforcement and maintenance decisions.
Configuration Recommendations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to common paragraph lengths of patrol logs, maintenance plans and other documents, avoids overly scattered or redundant single-block content |
| Overlap Length | 100–200 characters | Retains cross-block policy-related information, prevents complete policy clauses from being truncated by chunking |
| Similarity Threshold | 0.75–0.85 | Balances recall accuracy and coverage for policy Q&A, meets the rigor requirements of road law enforcement |
| Single Search Limit | Top 10 entries | Matches the information volume of daily policy consultations, avoids excessive irrelevant content interfering with judgment |
defaultToken | 100000 | Adapts to token requirements for multi-system batch indexing, prevents errors caused by exceeding token limits during batch processing |
maxToken | 4096 | Matches input length limits of mainstream embedding models, ensures complete conversion of policy text to vectors |
Three Common Errors
- The interface displays the message "This token does not have permission to use the model". The cause is that the embedding model key and channel are not correctly configured in FastGPT, and unauthorized call methods are used incorrectly.
- Direct calls to the bce-embedding-v1 model fail. The cause is that the native embedding model channel is not configured, and relying on a transit channel causes abnormal call links.
- Recall results contain a large number of irrelevant maintenance work order data. The cause is that chunk length is set incorrectly, splitting complete policy clauses into multiple small chunks, causing similarity matching to confuse policy and work order content.
How to Confirm Configuration is Complete
- View FastGPT's embedding model call logs, confirm there are no permission errors or connection timeout prompts.
- Manually upload a road construction permit document, check if the chunked content completely retains the core clauses of the permit.
- Enter a policy-related query, verify that the number of recall results matches the similarity threshold settings.
- Trigger the daily incremental indexing task, confirm the task completes normally with no data omission prompts.
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.