What Data for This Category Looks Like
This category involves entities including ecological environment law enforcement detachments, monitoring stations, pollutant discharge units, and third-party testing institutions. Daily generated documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. Data sources cover online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports. Document structures include structured operating condition parameters, semi-structured law enforcement process records, and unstructured rectification requirements. Fields include law enforcement officer ID, sampling location, pollutant concentration, illegal facts, and others. Update frequency is daily.
Constraints for Vector Models and Indexes
Multi-source heterogeneous data sources require vector models to support semantic conversion for different text formats and structured fields. Daily updated data volume requires the index system to support incremental synchronization, avoiding performance loss from full reconstruction. Significant field differences across documents require vector generation to retain semantic associations for key business information such as law enforcement officer ID and sampling location. Review results for law enforcement and monitoring documents directly relate to illegal determination, so index recall accuracy must meet the rigor requirements of the business scenario, and no key associated evidence may be omitted.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the average paragraph length of daily law enforcement documents, avoiding semantic chunk breaks |
| Overlap Length | 100–150 characters | Ensures semantic continuity between adjacent chunks, preventing key information from being split |
maxToken | 8192 token | Adapts to the context length limits of most domestic vector models, covering semantic extraction for complete business documents |
| Similarity Threshold | 0.72–0.85 | Calibrated based on actual testing, balancing recall accuracy and coverage |
| Single Recall Limit | Top 10 entries | Matches the typical number of associated documents for law enforcement case files, avoiding interference from redundant information |
defaultToken | Use system default | Meets vector generation requirements for basic text, no additional adjustments needed |
Three Common Misconfigurations
- Phenomenon: Calls to third-party vector models are rejected, while curl test requests function normally. Cause: Request header parameters for the vector model are not correctly configured in FastGPT, leading to failed authentication.
- Phenomenon: Documents cannot be added to or deleted from the vector library directly via an external management system. Cause: FastGPT's vector library open interface permission is not enabled, and only local operation permission for the root user is retained.
- Phenomenon: Duplicate case file entries appear after merging knowledge bases. Cause: Deduplication configuration during index merging is not enabled, and matching deduplication is not performed using document unique identifiers.
How to Verify Correct Configuration
- Upload a single on-site inspection record, and confirm that the vector-generated chunk results cover the full text with no semantic breaks.
- Initiate a related business query, and verify that recalled documents include key evidence such as corresponding monitoring reports and law enforcement records.
- Test adding a third-party test report, and confirm that the vector library can synchronize updates and include the new document in recall scope.
- Check that the
maxTokenanddefaultTokenconfigurations meet the context requirements of the vector model.
The values provided are common starting points and should be evaluated 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.