What the data for this category looks like
Data comes from special equipment safety supervision systems, inspection and testing institution systems, user account books, and operator certificate databases. Update frequency is once per month. Daily documents include use registration certificates, periodic inspection reports, maintenance records, hidden danger rectification notices, accident reports, and operator certificates. Data fields include device unique identifiers, inspection cycle nodes, maintenance unit information, operator qualification numbers, hidden danger rectification status, and more. Field structures vary across different documents. Structured certificate documents have standardized fields, while report documents contain multiple sections of descriptive content.
What constraints these characteristics impose on knowledge base retrieval and recall
Multi-source data access creates a need for cross-system field alignment. Ensure that formats of fields such as device identifiers and inspection dates exported from different systems are unified, to avoid retrieval errors caused by field mismatches. The monthly update frequency requires the retrieval link to adapt to incremental synchronization logic, reducing resource consumption from full data scans. Diverse document types include structured certificates, semi-structured reports, and notification texts. Differentiated recall rules must be configured based on the field characteristics of different documents. The high-risk supervision attribute requires retrieval results to accurately match core fields such as device lifecycle, inspection cycle, and qualification validity, reducing false recall rates and avoiding supervision mistakes.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the precision requirement for core special equipment fields, filters low-similarity irrelevant data, and ensures retrieved content is strongly relevant to supervision business |
limit | Top 6 entries | Single-round retrieval results can cover multiple types of core documents for a single device, while controlling context length to adapt to conversation scenarios |
searchMode | vector_only | Prioritizes matching semantically precise supervision documents in the party building learning Q&A scenario, disables hybrid retrieval mode to avoid introducing irrelevant content |
usingReRank | Enabled | Addresses semantic differences across document types, uses reranking to filter low-correlation results, and improves retrieval accuracy in high-risk supervision scenarios |
datasetSearchUsingExtensionQuery | Disabled | No query extension is required in the party building scenario, to avoid introducing generalized content unrelated to special equipment supervision |
collectionIds | Configured exclusively for special equipment supervision party building collections | Isolates the knowledge base from other business datasets, preventing content confusion caused by cross-dataset retrieval |
Three Common Misconfigurations
- Issue: Retrieval results return non-specified special equipment supervision documents, including irrelevant administrative office content. Cause: The retrieval scope is not limited via
collectionIds, resulting in retrieval of content from other non-exclusive datasets.
Reference only; verify against the target FastGPT release and deployment documentation.
- Issue: Relevant content is not retrieved when a query matches the knowledge base during a conversation, or a preset fallback response is not triggered when no matching content exists. Cause: The combined parameters of
searchModeandusingReRankare not configured correctly, or no fallback response trigger rules are set for Q&A.
How to Confirm the Configuration Is Complete
- Access the dataset retrieval test page, enter precise queries for core fields such as device number and inspection cycle, and verify that returned document types and fields match preset special equipment supervision content.
- Review retrieval logs to confirm that actual values of configuration parameters such as
similarityandlimitmatch configured settings, with no parameter overrides. - Simulate a query with no matching content, verify that a preset fallback response is triggered, and confirm that Q&A logic meets set requirements.
- Check the dataset’s bound model, confirm that the currently used embedding model matches the configured model version, with no model compatibility issues.
The values provided here are common starting points, and should be evaluated 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.