What this category of data looks like
Data for this category typically comes from the "Double Random, One Public" regulatory platform, enterprise credit information disclosure system, industry supervision systems, and law enforcement officer directory. The update frequency is once per month. Daily documents include inspection plans, inspection object lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. Most data fields include structured content such as inspection object identifiers, law enforcement officer numbers, inspection item entries, and rectification feedback status. Some documents are accompanied by on-site transcripts or photo attachments.
What constraints do these characteristics impose on the knowledge base retrieval and recall link?
The monthly update cycle of the data means the knowledge base must complete a full synchronization at fixed intervals, to avoid data lag affecting the accuracy of ledger summary. Mixed storage of multiple document types means the retrieval link must support both structured field matching and full-text content recall, to adapt to law enforcement query needs across different dimensions such as inspection items, law enforcement officers, and inspection objects. Dispersed data sources and a small number of duplicate entries require basic deduplication before recall, to prevent redundant data from interfering with results. Additionally, most queries in regulatory scenarios focus on law enforcement process nodes, so retrieval must prioritize matching core content directly related to inspection plans and inspection results.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.6–0.7 | Match precise law enforcement keywords in regulatory scenarios, to avoid low-relevance content being included in the ledger summary process |
limit | Top 8 entries | Control the number of documents recalled per single query, to adapt to the limited context length requirements of ledger summary |
usingReRank | Enabled | Re-sort recalled results by relevance, to raise the display priority of core law enforcement documents |
datasetSearchUsingExtensionQuery | Enabled | Expand keywords related to law enforcement scenarios based on the original query, to improve matching accuracy for inspection items and law enforcement objects |
collectionIds | Configured by regulatory business grouping | Only recall knowledge base collections corresponding to the current law enforcement domain, to avoid cross-category data interfering with retrieval results |
Three common configuration errors
- Retrieval results do not display the source path or file name of the corresponding document. The cause is that the answer source association configuration is not enabled, only plain text corpus is returned via API, and no document metadata is carried.
- Recalled results include documents from other law enforcement domains. The cause is that the exclusive knowledge base collection is not limited via
collectionIds, and cross-category data interferes with retrieval results. - Relevant records for specified inspection items cannot be recalled. The cause is that the similarity threshold does not match business requirements, and low-relevance content covers target matching items.
How to verify the configuration is complete
- Manually input a query term for a law enforcement scenario, and verify that recalled results only include documents from the current regulatory domain.
- Check the metadata fields of retrieval results, and confirm that traceability information such as document source and update time is included.
- Adjust query keywords, and verify that the relevance of recalled results meets business expectations, without relying on fixed thresholds.
- Trigger a full synchronization task, and verify that the knowledge base completes data updates according to the set cycle.
The values provided here are common starting points, and should be evaluated against samples appropriate for the specific deployment context.
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.