Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
Daily documents for this category include sampling plans, sampling sheets, inspection reports, unqualified food verification and disposal reports, and risk control announcements. Data sources include the food safety sampling and monitoring system, the State Administration for Market Regulation sampling results query system, laboratory LIMS, and enterprise ledgers. The update frequency is monthly. Document fields cover sampling numbers, inspection items, non-conformity judgment bases, disposal processes, and more. Units are mostly batches, copies, test item names, and similar. The structure primarily combines structured forms and unstructured text reports.
Constraints Imposed on Model Access and Configuration
The monthly updated data scale and update rhythm require model context configuration to adapt to recent sampling inspection documents, to avoid loading expired data. The multi-source and heterogeneous document structure requires access configuration to support parsing logic for both structured forms and unstructured text, to ensure accurate field extraction across different document types. Core evidence relies on laboratory inspection reports, so the model must accurately reference original content, requiring configuration of a reasonable reference length parameter. Additionally, the rigor of regulatory scenarios requires model output to avoid randomness, limiting the range of the temperature parameter.
How to Set Configuration Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers all monthly updated sampling-related documents, avoids context overflow and truncation |
maxTokens | 2000–3000 token | Meets the need for complete responses to policy clauses and inspection reports, adapts to the content length of regulatory Q&A |
quoteMaxToken | 1500 token | Ensures complete reference of original fragments from sampling reports and risk announcements, complies with reference specifications for regulatory documents |
temperature | 0.1–0.3 | Reduces randomness in model output, ensures rigor and consistency of policy Q&A |
AIPROXY_API_ENDPOINT | Fill in according to the official access address of the selected model service provider | Ensures correctness of the model call chain, avoids call failures caused by incorrect paths |
AIPROXY_API_TOKEN | Fill in the valid access key assigned by the service provider | Completes interface identity verification, initiates legitimate model call requests |
Three Common Configuration Mistakes
- Model interface calls return 404 errors. The full path of
AIPROXY_API_ENDPOINTis not filled correctly, or the selected model version does not match the access address. - Channel tests succeed but workflow runs fail. The structured field format of sampling-related documents is not adapted, so the model cannot accurately extract key judgment bases from inspection reports.
- Redundant reasoning content is still generated after thinking process output is disabled. The default reasoning output logic of the model is not overridden via system prompts or parameter configuration, or the temperature parameter is set too high, leading to random output.
How to Verify Successful Configuration
- Perform channel connectivity testing, check that the interface returns status code 200, with no authentication failure or path error prompts.
- Upload a single sampling report and sampling plan document, initiate a policy basis Q&A, verify that the response accurately references original document content.
- Disable thinking process output in the configuration, test the Q&A result, confirm that the response does not include unnecessary reasoning steps.
- Adjust the
temperatureparameter to 0.2, initiate the same Q&A three times, verify the consistency and rigor of the results.
The values provided are common starting points and should be assessed against relevant 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.