What This Category of Data Looks Like
Core documents for this category include inspection plans, inspection target lists, inspection task checklists, on-site inspection records, rectification notices, and inspection result public notices. Data sources include the Random Inspection and Public Disclosure supervision platform, enterprise credit information public system, industry supervision systems, and law enforcement officer roster. Most document fields are supervision-specific identifiers, such as unified social credit code, law enforcement ID number, inspection time period, etc. Update frequency is once per month. Daily maintenance work includes maintaining the inspection task and target database, matching law enforcement officers to generate tasks, entering inspection results, and publishing public notices.
What Constraints These Characteristics Impose on Knowledge Base Retrieval and Recall
Multi-source data sources require precise binding to the corresponding supervision dataset during retrieval, to avoid mixing irrelevant data from other categories. The monthly update frequency means real-time synchronization is unnecessary; only regular full updates are required. The presence of supervision-specific fields requires retrieval to balance semantic matching and exact field matching, to ensure key information such as law enforcement ID numbers and unified social credit codes is accurately retrieved. Documents have moderate length but fixed fields, so the number and scope of retrieved results must be controlled to prevent redundant content from interfering with document drafting.
Configuration Settings
FastGPT v4.17.0
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Supervision documents require high matching accuracy to avoid irrelevant results disrupting law enforcement workflows |
limit | Top 8 results | Supervision inspection-related documents have moderate length; 8 context entries cover core drafting requirements |
searchMode | Semantic + full-text hybrid retrieval | Balances exact matching for supervision-specific fields and semantic matching for business semantics |
usingReRank | Enabled | Improves ranking priority of highly relevant results and reduces false recall probability |
datasetSearchUsingExtensionQuery | Disabled | Supervision documents have fixed formats, no need to extend query terms |
collectionIds | Bind corresponding supervision dataset ID | Precise retrieval scope restriction to avoid mixing knowledge base data from other categories |
Three Common Mistakes
- Issue: Retrieval results include large volumes of non-supervision unrelated documents. Cause: The
collectionIdsparameter was not used to restrict the retrieval dataset scope, leading to mixing of knowledge base data from other categories - Issue: Retrieval latency exceeds 10 seconds in local deployment scenarios. Cause: The
limitparameter was not adjusted to avoid retrieving too many results, or the deployment did not adapt to local hardware computing power, leading to increased latency in the re-ranking step - Issue: Imported law enforcement officer contact information cannot be retrieved. Cause: Only semantic retrieval mode was enabled, and the full-text retrieval function in
searchModewas not activated, so exact text field matching cannot be performed
How to Confirm Proper Configuration
- Enter the application's retrieval test page, input "inspection plan template", and check if the returned context includes content from the locally bound supervision dataset
- View the retrieval log, and confirm that the similarity scores of returned results fall within the range configured for
similarity - After enabling the
usingReRankfunction, run the test again, and confirm that the sorting order of returned results differs from the initial retrieved results - Check the dataset synchronization status, and confirm that monthly full synchronization has been completed with no missing data records
The values listed are common starting points and should be evaluated 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.