What this category of data looks like
This category of data comes from food safety sampling monitoring systems, the State Administration for Market Regulation sampling results query system, laboratory LIMS systems, and food producer and operator ledgers. Daily documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control notices. Most documents have fixed business fields such as sampling locations, inspection items, and non-conformity judgment basis. The data updates monthly, and individual document lengths vary widely: from short sampling slips to inspection and disposal reports with thousands of characters.
What constraints these characteristics impose on knowledge base retrieval and recall
Monthly updated data must be synchronized to the knowledge base regularly to avoid retrieving outdated sampling plans or old versions of inspection reports. Fixed business fields and mixed-format documents require retrieval to accurately match keywords such as sampling locations and inspection items, to prevent irrelevant results from being included. Long documents with embedded tables and images increase the difficulty of chunking and semantic embedding, which reduces the accuracy of retrieved context. Differences in document formats generated by different business entities require retrieval rules adapted to multi-source data, to ensure that retrieval results for scenarios such as supervision and inspection meet business needs.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.72–0.88 | Matches precise business fields such as sampling locations and inspection items, filters low-relevance non-sampling documents |
limit | Top 5–7 entries | Covers multiple chunks of a long inspection report while keeping total context length within model input limits |
searchMode | Hybrid retrieval | Balances keyword matching and semantic association, adapts to mixed document scenarios of sampling plans and inspection reports |
usingReRank | Enabled | Re-ranks initial retrieval results to improve retrieval accuracy of unqualified food verification and disposal reports |
datasetSearchUsingExtensionQuery | Enabled | Expands retrieval scope based on keywords such as sampling batch numbers entered by users, matches associated sampling slips and inspection reports |
Three Common Misconfigurations
- Embedded tables and images in retrieval results cannot be fully retrieved or have semantic drift. The cause is failure to adjust chunking and embedding rules for mixed-format documents such as inspection reports and sampling slips, which prevents the model from capturing inspection items and judgment basis within tables.
- System-recommended extended questions do not match the exclusive business scenario of food safety sampling, and only generate generic questions. The cause is failure to enable question extension configuration based on exclusive documents of this category's knowledge base, resulting in extended content that deviates from actual business data.
- Model-related errors occur during retrieval calls. The cause is failure to configure retrieval model permissions adapted to this category's data, or model version mismatch with the knowledge base embedding model, resulting in failure to normally retrieve documents.
How to Verify Proper Configuration
- Manually enter core keywords from a sampling plan, check whether retrieved results include chunked content of the corresponding document.
- Adjust the
similarityconfiguration range, verify whether the relevance of retrieval results meets business needs. - After enabling
usingReRank, check whether long documents are sorted to prioritize core content related to unqualified food disposal. - Trigger the question extension function, confirm that all recommended extended questions come from exclusive document content of this category.
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.