What this category's data looks like
Service entities for this category include food sampling departments of market supervision bureaus, inspection institutions, grassroots supervision offices, and food producers and operators. Data mainly comes from the food safety sampling monitoring system, the State Administration for Market Regulation sampling results query system, the inspection institution LIMS system, and the ledgers of food producers and operators. The update frequency is once per month. Daily documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control notices. Each document has exclusive fields such as sampling batch, inspection items, non-conforming items, and disposal measures. The data structure is fixed in line with regulatory requirements and inspection processes.
What constraints these characteristics impose on model access and configuration
Fixed monthly updated batch data requires model access to support batch task scheduling and incremental synchronization. This avoids repeated processing of historical data. Multi-source heterogeneous documents require configuration of parsing rules adapted to different formats. This prevents deviations in field extraction. High confidence requirements for core regulatory fields such as non-conforming items and disposal measures require limiting the matching accuracy between model-generated content and source data. Fixed format requirements for regulatory publicity documents require locking model output typesetting to match official templates. This avoids format errors that disrupt subsequent publicity processes.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the model service interface address to connect to. Use the corresponding transit address when using a transit service | Complete path configuration for model calls to adapt to batch task interface call requirements |
AIPROXY_API_TOKEN | Fill in the access key allocated by the model service | Complete identity verification to ensure interface call permissions are legal and valid |
maxContext | 8000–12000 token | Cover the context length of all monthly full sampling documents to avoid truncation of core sampling and inspection data |
maxTokens | 4000 token | Limit the output length of a single report generation to fit the fixed length requirements of regulatory documents |
temperature | 0.1–0.3 | Reduce generation randomness to ensure core field outputs such as non-conforming items and disposal measures highly match source data |
toolChoice | auto | Automatically select tools to handle batch document parsing, improving ledger summary processing efficiency |
Three common configuration mistakes
- An interface call failure prompt appears in the interface, and OneAPI tests normally on its own. The cause is an incorrect
AIPROXY_API_TOKENconfiguration, or the interface address does not correctly point to the valid path of the transit service. - After specifying a model in the application settings, the actual call still returns results from another model. The cause is failing to lock the target model identifier in the configuration, or incorrect model mapping configuration in the transit service.
- Core fields are missing in the generated report. The cause is an excessively small
maxContextvalue, which truncates the source document context and prevents full extraction of all necessary fields.
How to confirm successful configuration
- Call the FastGPT built-in test interface. A normal identity verification passed prompt confirms that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. - Upload a single sampling report to verify that the sampling batch, inspection items and other fields extracted by the model match the source document. This confirms that the
temperatureandmaxContextvalues fit field extraction requirements. - Initiate a single report generation task to check that the output format aligns with the official regulatory template. This confirms that the output constraint configuration takes effect.
- Configure batch task trigger rules and test the synchronization process according to the monthly update frequency. This confirms that the batch scheduling function operates normally.
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.