What this category of data looks like
This category of data primarily comes from the random inspection and public disclosure supervision platform, enterprise credit information public disclosure system, industry supervision system, and law enforcement officer directory. It is updated monthly. Daily documents include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result public disclosures. Data fields cover the unified social credit code of inspection targets, law enforcement officer numbers, inspection item content, rectification completion status, public disclosure status, and more. Single document size ranges from hundreds to thousands of characters.
Constraints on Citation Sources and Traceability Link from These Data Characteristics
Data sources are scattered. Configure multi-source data association verification logic to ensure cited information such as inspection targets and law enforcement officers matches external system data. The monthly update frequency requires the knowledge base to synchronize full data regularly, avoiding lagging inspection results or target information. Multiple document types with large field differences require differentiated recall rules for different documents such as inspection plans, on-site records, and public disclosure results. The wide range of single document sizes requires controlling token usage per citation to avoid context overflow. The core inspection result public disclosure link requires ensuring cited inspection records fully match public information to meet compliance requirements for supervision traceability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
quoteMaxToken | 800–1200 token | This category has a wide range of single document sizes. Control token usage per citation to avoid context overflow |
responseData | Only return inspection target, law enforcement officer, and inspection result fields | This category has clear core business fields. Reduce redundant information interference and focus on core data required for supervision traceability |
detail | Enabled | Display the original source identifier of cited documents to ensure traceability |
| Single search limit | Top 5 entries | This category has a large number of inspection targets and items. Control the number of recalled entries to avoid context overload |
| Knowledge base update frequency | Monthly | Align with the monthly update rhythm of business data to ensure timeliness of cited data |
| Maximum context | 4000 token | Adapt to total context splicing of multiple document types, and comply with FastGPT v4.17.0 model context window limits |
Three Common Configuration Errors
- Phenomenon: The number of knowledge base citation results does not match the configured single search limit. Returns too many or too few documents. Reason: Failed to adjust the configuration based on the actual number of inspection targets and items in this category, leading to recall logic that does not match business needs.
- Phenomenon: Cited document fields are missing or contain irrelevant content. Reason: Incorrectly configured the return fields for
responseData. Missed core fields required for supervision traceability such as the unified social credit code of inspection targets, or returned non-business related redundant fields. - Phenomenon: The text extraction component cannot extract valid content from knowledge base citations. Reason: The
detailconfiguration is not enabled. Original source identifiers or core fields are not returned, so the extraction component cannot obtain valid information from cited documents.
How to Verify Correct Configuration
- Initiate a test for the inspection plan review scenario. Check the source identifier of citation results to confirm that the original document's source platform and update time are displayed.
- Adjust the single search limit configuration. Send a test request to confirm that the number of returned citations matches the preset top 5 requirement.
- Check the
quoteMaxTokenconfiguration. Verify that single citation token usage does not exceed the FastGPT v4.17.0 model context window limit. - Trigger a knowledge base update task. Confirm that the update frequency matches the monthly configuration to ensure cited data aligns with the business system's update rhythm.
The values listed here are common starting points. Adjust them based on your own specific samples and requirements.
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.