What the data for this category looks like
Service subjects for this category include emergency management departments, industry competent departments, safety management departments of production and business entities, and park management committees. Data sources include safety production supervision systems, enterprise hazard self-inspection systems, law enforcement inspection records, and accident statistics systems. Daily documents cover hazard inspection lists, hazard governance ledgers, rectification notices, review records, and major hazard rectification supervision notices with public posting. Data is updated daily. Core fields include hazard level, rectification responsible entity, rectification deadline, review results, and other related fields. No custom special units are used. All information is generated around each link node of the full-process hazard control.
What constraints these characteristics impose on the vector models and indexing link
High-frequency daily updated data requires incremental update mode for indexes, to avoid extra time consumption caused by full reconstruction. Significant structural differences exist across multiple document types. Differentiated chunking rules must be designed for different documents such as inspection lists and rectification notices, to avoid splitting core business logic and associated fields. Fields focus on core control nodes such as hazard level, rectification responsibility, and review results. Vector indexes must prioritize association with such high-weight information, to improve consultation matching accuracy. Multi-source data must first complete format unification. Inconsistent data formats will reduce the accuracy of recall matching.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to the length of core single-segment information in documents such as hazard inspection lists and rectification notices, avoids splitting rectification logic and associated fields |
| Overlap Length | 100–150 characters | Retains associated information of adjacent chunks, ensures cross-chunk information such as hazard rectification deadlines and responsible entities is not split and lost |
| Similarity Threshold | 0.75–0.85, calibrated via actual testing | Meets semantic similarity requirements for hazard-related consultations, balances recall accuracy and filtering efficiency for irrelevant results |
| Single Search Limit | Top 6 entries | Adapts to the concise and efficient needs of work consultations, avoids returning excessive redundant historical ledger information that interferes with core responses |
| maxToken | 4000 | Carries context information from multiple rectification notices and review records, ensures responses cover the complete hazard rectification process |
| defaultToken | 200 | Controls the vector output length of a single chunk, optimizes index storage usage and retrieval response speed |
Three Common Misconfigurations
- Returns a large number of irrelevant historical accident records during search, with similarity matching not meeting expectations. The cause is that no dedicated chunking rule is set for hazard governance ledgers, and long documents are split into overly short chunks, leading to broken semantic associations.
- After calling the pushdata API to upload chunk-mode data, the interface continuously displays the indexing status. The cause is that no incremental update trigger rule is configured, full index reconstruction takes too long and exceeds the default limit, or the chunk length exceeds the system processing upper limit.
- After accessing the bce-embedding-v1 model, the number of retrieval results is always 0. The cause is that the vector dimension output by the model is not aligned with the index dimension configuration of FastGPT, making vector matching impossible.
How to Confirm Configurations Are Set Correctly
- Upload a single hazard inspection list document, check if the chunked content completely retains core fields such as hazard level and inspection time, with no obvious content splitting.
- Initiate a typical consultation such as "How to rectify if a workshop fails to inspect hazards as required", verify whether the similarity of returned results falls within the preset matching range, and whether the proportion of irrelevant results meets expectations.
- View the storage usage on the index management page, confirm that the vector storage size of a single chunk conforms to the value range set by the defaultToken configuration, with no abnormal overflow.
- Test the incremental upload function: after uploading a new rectification notice, check whether the retrieval results include the latest rectification information, to verify that the update logic takes effect.
The values provided here are common starting points, and should be evaluated against sample data from the actual deployment environment.
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.