Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from 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.
On-site inspection records contain inspection periods, law enforcement officer signatures, and on-site operating condition records. Sampling records include sampling point numbers, sampling times, and test items. Monitoring reports mark test standards and pollutant concentration values. Law enforcement documents include descriptions of illegal facts and rectification requirements.
The data structure covers two categories: structured law enforcement fields and unstructured test reports, adapted for knowledge retrieval needs in party building learning scenarios.
What constraints these characteristics impose on the knowledge base retrieval and recall link
The multi-source mixed data structure requires the retrieval function to support mixed matching of structured fields and unstructured text. The daily update frequency requires the knowledge base synchronization mechanism to adapt to high-frequency data updates, preventing data lag from affecting party building learning and law enforcement decision-making.
Diverse document types and strict relevance requirements demand accurate recall of all reference materials related to current law enforcement and monitoring scenarios, avoiding missing key evidence. The severe consequences of errors in law enforcement scenarios require the retrieval link to ensure the accuracy and completeness of recall results, while controlling the response time of a single retrieval to meet the real-time needs of on-site law enforcement and party building learning.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.80 | Adapts to semantic matching accuracy for law enforcement and monitoring documents, balances recall rate and precision, and meets knowledge retrieval needs for party building learning Q&A scenarios |
limit | Top 8 entries | Covers the volume of reference documents required for a single party building learning and law enforcement scenario, avoids overly long context affecting model responses |
searchMode | Hybrid retrieval | Supports mixed matching of structured law enforcement fields and unstructured monitoring reports, adapts to retrieval needs for multiple document types |
usingReRank | Enabled | Performs secondary sorting on recall results, raises the display priority of highly relevant documents, and optimizes knowledge presentation for party building learning scenarios |
datasetSearchUsingExtensionQuery | Enabled | Expands law enforcement-specific terminology based on the original question, improves recall coverage of relevant documents, and enriches the knowledge dimension for party building learning |
collectionIds | Bind dedicated collections to law enforcement entities | Isolates law enforcement and monitoring data from different entities, avoids interference from cross-entity data, and ensures the targeted nature of party building learning content |
Three Common Configuration Errors
- Symptom: The model returns a prompt that no relevant information is found, but the knowledge base semantic retrieval matching score is 0.77. Cause: The
similaritythreshold is set too high, or thedatasetSearchUsingExtensionQueryextended query is not enabled, resulting in some documents relevant to the business not being recalled. - Symptom: The retrieval result only returns a single document, failing to cover all reference materials required for the party building learning scenario. Cause: The
limitparameter is set too low, and not enough relevant documents are recalled. - Symptom: The retrieval result directly outputs the full text of the original document without necessary content organization. Cause: No context truncation rules are configured, or the content aggregation function is not enabled.
How to Verify Successful Configuration
- Upload a test on-site inspection record, enter a relevant party building learning question, and confirm that the recall result includes this document.
- Adjust the
similarityparameter to 0.75, verify that documents with different matching scores are recalled as expected. - Bind multiple law enforcement and monitoring knowledge base collections, enter a cross-type question, and confirm that the recall result covers documents from multiple sources.
- Enable the
usingReRankfunction, verify that the order of recall results follows the business relevance priority.
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.