What this category of data looks like
The data sources for this category are online monitoring platforms, automatic pollution source monitoring data, ecological environment law enforcement systems, and third-party test reports. The update frequency is daily. Daily documents include on-site inspection records, sampling records, monitoring reports, order for correction decisions, and administrative penalty decisions. Law enforcement documents include metadata such as law enforcement subject, inspection period, and sampling points, along with law enforcement process text. Monitoring documents include structured content such as monitoring indicators and test results. The overall data covers full-process business records for ecological environment law enforcement and monitoring.
What constraints do these characteristics impose on the "knowledge base retrieval and recall" link
The requirement for daily data updates means the retrieval pipeline must support incremental synchronization and real-time verification. This prevents returning expired law enforcement or monitoring basis. Multi-source heterogeneous data formats require the retrieval module to adapt to the metadata and content structures of different documents. This ensures fields in law enforcement documents and monitoring reports are correctly identified. The core requirement of law enforcement and monitoring is accurate matching of policy provisions. Retrieval cannot rely solely on a single matching method. It must combine business scenarios to link law enforcement scenarios and regulatory provisions. It must also ensure retrieval results cover full-process law enforcement and monitoring basis.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Law enforcement and monitoring scenarios require accurate matching of policy provisions, to avoid irrelevant low-similarity results interfering with law enforcement decision-making |
limit | Top 8–12 entries | Law enforcement personnel need limited core basis for quick review. Excessive results increase retrieval and decision-making costs |
searchMode | vector+keyword | Balances semantic matching and precise keyword matching, adapting to fixed wording of policy provisions and specific scenarios of law enforcement records |
usingReRank | Enabled | Retrieval results from multi-source data need to be reordered by relevance, to ensure the most relevant policy or monitoring basis appears first |
datasetSearchUsingExtensionQuery | Enabled | Context from law enforcement scenarios such as law enforcement points and monitoring indicators can be expanded into auxiliary retrieval terms, improving matching accuracy |
collectionIds | Collection IDs specified by business scenario | Only retrieve knowledge base collections corresponding to environmental law enforcement and monitoring categories, avoiding interference from irrelevant data in retrieval results |
Three common errors
- Phenomenon: No matching results are returned when a question is submitted for the first time, but correct content is returned when submitted a second time. Cause: The first retrieval does not trigger question optimization, or daily incremental synchronization has not completed. This means the initial retrieval's knowledge base data does not cover the policy or monitoring basis required for the current query.
- Phenomenon: Retrieval results are empty or return documents unrelated to law enforcement and monitoring. Cause: The
collectionIdsparameter is not configured correctly, leading the retrieval scope to cover non-target knowledge base collections. Or thesimilaritythreshold is set too high, filtering out all valid matching results. - Phenomenon: External knowledge base association fails, and content of law enforcement or monitoring documents cannot be read. Cause: Access parameters for the external data source are not configured per the official FastGPT v4.17.0 documentation, or ID binding for the corresponding knowledge base collection is not completed.
How to confirm the configuration is correct
- View the collection list in the FastGPT knowledge base management interface. Confirm that the collections bound by the
collectionIdsparameter are all environmental law enforcement and monitoring related document collections. - Submit a typical law enforcement-related policy query. Check that the similarity scores of returned results fall within the preset range, and the number of results matches the
limitsetting. - After enabling
usingReRank, compare the result order before and after reordering. Confirm that the most relevant policy provisions or monitoring basis appear first. - Submit the same question twice. Verify consistency between the first and second retrieval results, and confirm there are no abnormal delays or data synchronization issues.
The values provided here 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.