What data for this category looks like
Data is typically sourced from "dual random, one public" supervision platforms, enterprise credit information disclosure systems, industry supervision systems, and law enforcement officer directories. Update frequency is monthly. Daily documents include inspection plans, target inspection lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. Fields typically include supervision item codes, unified social credit codes of inspected entities, law enforcement officer IDs, inspection times, and similar details. Most documents use structured or semi-structured formats, with no unified fixed non-standard custom fields.
Constraints on knowledge base retrieval and recall
Monthly updated supervision data requires the knowledge base index refresh cycle to match the data update rhythm. This prevents retrieval of expired inspection plans or announcement results. Mixed storage of multiple law enforcement document types requires the retrieval component to adapt to different document formats. It also requires accurate cross-document field association. Multi-data-source setups require strict limitation of retrieval scope to specified supervision-related datasets. This avoids introducing irrelevant publicly available social data that interferes with law enforcement judgments. Law enforcement scenarios have high retrieval accuracy requirements. Excessive redundant results increase screening costs for law enforcement officers. The number and relevance of returned results must be strictly controlled.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Regulatory policy documents use precise wording. High accuracy is required to avoid matching unrelated law enforcement items |
limit | Top 5 results | Law enforcement scenarios do not need excessive redundant results. Control the number of documents returned per retrieval |
searchMode | local | All data comes from internal supervision systems. No cross-external data source retrieval is needed. Limit scope to local datasets |
usingReRank | Enabled | Improve relevance ranking accuracy for long documents such as on-site inspection records. Adapt to long-text retrieval in law enforcement scenarios |
datasetSearchUsingExtensionQuery | Disabled | Policy and regulatory basis documents use standardized wording. No additional retrieval query optimization is needed. Avoid introducing irrelevant matches |
collectionIds | Bind specified supervision item library and inspected entity library datasets | Limit retrieval scope to ensure only documents related to dual random supervision are returned |
Three common mistakes
- When creating a knowledge base and binding structured data from the supervision platform, the process gets stuck at the "build index" step. The cause is failure to adapt shard index configuration for bulk law enforcement data, resulting in timeout during large-volume dataset construction.
- Retrieval results cannot extract images from on-site inspection records in documents. The cause is failure to enable image embedding support for the knowledge base, and uploaded docx documents do not correctly embed image resources.
- The text content extraction component cannot extract content from knowledge base citations. The cause is failure to configure the component's dataset association parameters, or the retrieval scope does not cover the target law enforcement document collection.
How to confirm correct configuration
- Initiate a retrieval for "policy basis for a certain type of supervision item". Check that the dataset source of returned results is the pre-configured supervision item library and inspected entity library.
- Adjust the
similarityparameter range. Verify that the relevance of returned results meets the accuracy requirements of law enforcement scenarios, with no unrelated matching items. - Import monthly updated inspection plan documents. Confirm that index construction has no errors, and retrieval results can return the latest data normally.
- Call the retrieval interface. Verify that the number of returned results matches the value set in the
limitparameter.
The values provided are common starting points and should be measured against the reader'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.