Reference only; verify against the target FastGPT release and deployment documentation.
What data falls under this category?
Data for this category primarily comes from local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. It is maintained by entities such as big data bureaus and government service data administration bureaus. Daily documents include public data resource catalogs, data open lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Fields cover data item names, open conditions, desensitization requirements, interface addresses, update cycles, and more. The update frequency is once per month.
What constraints do these characteristics impose on the "vector models and indexing" workflow?
The monthly update frequency necessitates configuring scheduled incremental indexing tasks to avoid excessive resource usage from full indexing. Documents include structured data items, interface descriptions, and unstructured rule text. Distinct chunking strategies must be used for different fields to ensure accuracy of vector generation. Sensitive fields such as open conditions and desensitization rules must be preprocessed before indexing to prevent sensitive information from being exposed via vector storage. Data formats vary across multiple sources. Unified field mapping must be completed before vector generation to avoid indexing chaos.
How to set configurations?
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Matches the typical length of individual public data item descriptions and interface documents |
| Overlap Length | 100–150 characters | Ensures contextual continuity between adjacent data chunks |
| Similarity Threshold | 0.75–0.85 | Filters low-match results to ensure accuracy of policy basis results |
| Single Recall Count | Top 10 results | Policy and regulatory basis content is usually concentrated in a small number of relevant documents |
maxToken | 8000 token | Supports contextual content carrying multiple data item descriptions and policy clauses |
defaultToken | 200 token | Controls the output length of individual traceable text |
Three Common Misconfigurations
- Symptom: Search results cannot be linked to specific public data catalog documents, and empty source fields are returned. Cause: Document source identification fields are not retained during chunking, or source metadata is not loaded during indexing.
- Symptom: Vector generation tasks frequently time out. Cause: Incremental indexing is not configured according to the monthly update frequency, and full indexing is performed directly on the full public data catalog.
- Symptom: Returned results include unsensitized sensitive data items. Cause: Desensitization rule fields are not preprocessed before indexing, and raw sensitive data is directly used to generate vectors.
How to Confirm Configurations Are Correctly Set
- Upload a public data item description document, initiate a corresponding policy basis query, and check whether the returned results include document source information.
- Manually trigger an indexing task, and verify whether the task duration falls within a reasonable range with no timeout errors.
- Enter a query containing sensitive data items, and check whether the returned results do not include unsensitized raw data.
- Wait for the monthly update cycle to end, and verify whether the index automatically completes incremental updates.
The values provided here are common starting points. Adjustments should be made based on the user’s own samples and testing.
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.