What this category of data includes
Typical data sources for this category include the randomized probabilistic regulatory inspection platform with public disclosure, enterprise credit information public disclosure system, industry regulatory systems, and law enforcement officer directory. Daily generated documents include inspection plans, inspection target lists, inspection item checklists, on-site inspection records, rectification notices, and inspection result public notices. Data is updated on a monthly basis. Core fields include inspection target identifiers, law enforcement officer numbers, inspection item entries, and rectification requirements. No additional custom statistical fields are included.
Constraints imposed on vector models and indexing
The monthly update rhythm requires the indexing system to support a combination of batch full synchronization and incremental updates, to align with the regular maintenance workflow of regulatory data. The mixed presence of multiple document types requires a chunking strategy that distinguishes between structured list fields and unstructured text content, to avoid semantic breaks. The need for unified access to multi-source data requires the indexing system to support cross-system field mapping rules, to ensure content from different data sources can be retrieved uniformly. The high-frequency matching scenario between law enforcement officers and inspection targets requires the indexing system's recall efficiency to adapt to the real-time requirements of task generation, while ensuring matching accuracy meets regulatory compliance requirements.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for unstructured documents such as on-site inspection records and rectification notices |
| Overlap Length | 100–200 characters | Prevents critical context breaks after chunking, and maintains cross-chunk semantic connections |
| Similarity Threshold | 0.72–0.85 | Matches recall accuracy requirements for precise matching scenarios such as inspection targets and law enforcement officers |
| Single Search Limit | Top 10 entries | Adapts to the retrieval scale of monthly updated inspection target libraries and law enforcement officer directories |
defaultToken | 2048 | Meets context splicing length requirements during ledger summary |
maxToken | 8192 | Meets long text generation limits during regulatory report generation |
Three common configuration errors
- Vector model access fails in the interface, returning a 400 status code. Cause: Chunking rules are not configured according to the multi-source format of regulatory data, leading to missing core fields after chunking and inability to generate valid vectors.
- The number of search results is far lower than expected, with only 2 or fewer records returned. Cause: The similarity threshold is set too high, filtering out potential inspection targets or law enforcement officers that meet regulatory matching requirements.
- Duplicate inspection target entries appear after index update. Cause: The version verification mechanism for incremental updates is not enabled, leading to redundancy when old data is not overwritten during monthly synchronization.
How to verify correct configuration
- Upload a single on-site inspection record document, and check if the chunked text fragments retain core content such as inspection time and inspection items.
- Enter the keyword "2024 first half-year catering industry inspection targets", and verify that the returned results include valid entries from the corresponding directory.
- Trigger a monthly full index synchronization task, and check the system logs to confirm that data update is completed and no field exception prompts are displayed.
- Set
maxTokento 8192, then attempt to generate a summary report covering 3 or more rectification notices, to verify that context splicing is not truncated.
The values provided are common starting points and should be measured against the user'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.