What Data for This Category Looks Like
This category of data mainly comes from the Random Check, One-Public Supervision Platform, Enterprise Credit Information Publicity System, industry supervision systems, and law enforcement officer directory. Daily generated documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. The data update cycle is usually monthly. Core fields include the unified social credit code of supervised entities, inspection item codes, law enforcement officer numbers, and rectification time limits. Some on-site inspection records contain unstructured content with handwritten annotations. Document formats cover both structured tables and free text.
Constraints on Vector Models and Indexing From These Characteristics
Multi-source heterogeneous data sources require the indexing process to support multiple document formats. This avoids semantic separation between structured fields and unstructured text. The monthly update rhythm means indexing does not need real-time triggering. Use batch scheduled processing to reduce resource consumption. Unified coding rules for core fields require the vector model to adapt to both structured coding and natural text semantic understanding. This ensures matching accuracy between inspection targets and items. Unstructured content in on-site inspection records requires a chunking strategy that balances semantic integrity and field relevance. This prevents truncation of key inspection details.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | This category of documents includes structured lists and unstructured inspection records. A chunk length of 800–1200 characters balances field integrity and semantic coherence |
maxToken | 8192 | The average semantic length of core documents for this category matches the token limit of mainstream vector models, avoiding truncation of key inspection item descriptions |
| Reranking Model | Enabled | The semantic complexity of on-site inspection records is high. Reranking improves the sorting accuracy of highly relevant recall results |
defaultToken | Calibrated based on actual measurements | Different vector models have different token billing rules. Match the actual token quota supported by the locally deployed model |
| Single Search Limit | Top 10 entries | Matching results for supervision inspections must fit within the range law enforcement officers can quickly review. Too many results increase review burden |
| Chunk Overlap Length | 100–150 characters | The field relevance of inspection item lists is strong. Overlapping chunks prevent semantic breaks across chunks |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Configuration Mistakes
- Symptom: When deploying FastGPT v4.17.0 locally, no image indexing model option appears when creating a knowledge base and uploading documents. Cause: This function relies on a specific image parsing plugin. The relevant option will not display if the corresponding plugin is not installed.
- Symptom: Embedding model configuration using OneAPI fails to connect, triggering a connection timeout error. Cause: The data source interface of the random check supervision platform may have access restrictions. Without configuring intranet penetration or a whitelist, FastGPT cannot normally call the vector model service forwarded by OneAPI.
- Symptom: After enabling the reranking model, the online recall test does not return reranked results. Cause: The reranking trigger switch is not enabled in the current knowledge base's indexing settings. Only configuring the global reranking switch does not affect the indexing process of the current knowledge base.
How to Confirm Configuration Is Complete
- Enter the knowledge base management page, view the indexing configuration panel, and confirm the adapted vector model and reranking model (if required) are selected.
- Upload a single inspection plan document, trigger manual indexing, and check the indexing log for records of successful chunking and successful vector generation.
- Launch a recall test, enter keywords matching the business scenario, and verify the number and sorting of returned results meet the preset single search limit.
- Check the knowledge base update cycle settings, confirm they match the monthly update business rhythm, and avoid setting an overly short automatic update interval.
The values provided here are common starting points. Adjust them based on your 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.