What data for this category looks like
This category’s data comes from food safety sampling and monitoring systems, the State Administration for Market Regulation’s sampling results query system, laboratory LIMS, and food producer and operator enterprise ledgers. Update frequency is monthly. Daily business documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control notices. Data fields center on sampling numbers, sample information, inspection results, and disposal processes. The data covers full-process business records for entities including market supervision bureau food sampling teams, testing institutions, and grassroots supervision offices.
Constraints imposed by these characteristics on vector models and indexes
The monthly update requirement means index configurations must support periodic batch updates. This avoids overly high single-index load. Differences in document structure across multiple sources and types require chunking strategies that balance short fields from structured sampling slips and full paragraphs from long-text inspection reports. This prevents semantic truncation. Highly time-consuming core business links such as sampling plan formulation and unqualified item review require indexes to prioritize these high-value documents. This improves retrieval efficiency. Strict regulatory requirements for unqualified food disposal also demand accurate vector retrieval recall. This prevents missed or misjudged relevant documents.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Adapts to long-text content such as inspection reports and verification and disposal reports. Prevents truncation of key test data and disposal process descriptions |
| Overlap Length | 100–150 characters | Connects context between adjacent chunks. Ensures complete semantic transmission of unqualified items. Prevents chunk separation of test conclusions |
maxToken | 8192 token | Covers full content of long-text inspection reports. Retains all test items and disposal requirements for unqualified food |
| Similarity Threshold | 0.72–0.80 | Differentiates test descriptions for qualified and unqualified food. Filters low-relevance non-target documents. Meets accuracy requirements for regulatory scenarios |
| Single Search Limit | Top 10 entries | Matches daily query needs of grassroots supervision personnel. Focuses display of recent sampling plans and unqualified disposal cases. Reduces review burden |
defaultToken | 1024 token | Adapts to daily short-text queries such as sampling locations and sampling cycles. Optimizes context carrying for basic retrieval |
Three common configuration errors
- Phenomenon: After uploading a large number of sampling-related documents, the server restarts. The knowledge base status shows not ready. No automatic indexing progress is displayed. Normal retrieval cannot be performed. Cause: No automatic recovery rules for batch indexing are configured. The scheduled indexing trigger mechanism is not enabled. Incomplete indexing tasks are not automatically completed after restart.
- Phenomenon: After updating FastGPT from v4.9 to v4.17.0, vector retrieval for the original sampling knowledge base returns no results. Cause: Chunk configuration from the old version does not match the vector model input requirements of the new version. Index files adapted to the new version are not regenerated.
- Phenomenon: When searching for "unqualified food disposal", results include a large number of irrelevant documents related to sampling plans. Cause: The similarity threshold is set too low. Low-relevance non-target content is not filtered. This leads to redundant retrieval results.
How to confirm configurations are correct
- Upload a single inspection report document. Check the chunk preview interface. Confirm that core test items and disposal processes are not truncated.
- Trigger a batch indexing task. Check that the indexing progress bar advances normally. No error prompts or abnormal interruptions occur.
- Enter a query such as "202X XX region sampling plan". Confirm that retrieval results include core paragraphs of the corresponding document. Sorting meets expectations.
- Adjust the value of
defaultToken. Test short-text queries such as "sampling location requirements". Confirm that context carrying of retrieval results meets requirements. - Simulate a server restart. If scheduled indexing rules are configured, check if the knowledge base automatically triggers incremental indexing. Or manually trigger indexing and confirm progress is normal.
The values listed are common starting points. They should be evaluated against samples relevant to the specific use case.
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.