What Data for This Category Looks Like
Data sources for this category include the Food Safety Sampling and Monitoring System, the State Administration for Market Regulation Sampling Results Query System, the laboratory information management system (LIMS) of inspection institutions, and the account books of food producers and operators. Data is updated monthly. Core documents include sampling plans, sampling forms, inspection reports, unqualified food verification and disposal reports, and risk control announcements. Data fields focus on sampling information, inspection results, and disposal processes. The core evidence is the inspection report issued by the laboratory. The difference from other market supervision categories is that it centers on sample inspection and non-compliant food disposal processes.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The monthly updated data cycle requires multi-turn dialogue contexts to tie exclusively to the current batch’s unique information, to avoid mixing data across cycles. Core documents include structured sampling forms and unstructured inspection reports, so the processing logic for the two types of documents must be distinguished during dialogue. Data from multiple systems must have their call scope clearly defined in the prompt to prevent mixing documents from different entities. The strict requirements for non-compliant disposal require the dialogue to forcibly associate corresponding fields in the verification and disposal report, to ensure guidance content complies with business specifications. Additionally, the time-consuming nature of sampling point planning requires the dialogue to gradually guide users to supplement information.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind the unique business number of the current sampling batch | Ensure multi-turn dialogue contexts only associate with exclusive documents such as sampling plans and sampling forms for the corresponding batch, to avoid mixing data across batches |
messages | Retain the last 12 rounds of dialogue, with a 7500-character limit | Match the monthly updated data cycle, retain sufficient context to associate historical operations, while limiting length to avoid interference from redundant information |
variables | Preset three variables: sampling batch number, inspection institution name, and disposal node | Match the structured fields of core documents, facilitate automatic insertion of corresponding business parameters into prompts, and reduce manual input errors |
| Number of recalled documents per single retrieval | Top 3 matching documents | Core documents revolve around key materials such as inspection reports and sampling forms, which are limited in number. Excessive recall will lead to context overload |
| Context window length | 6000-10000 characters | Adapt to session storage limits of different deployment environments, ensuring coverage of all associated documents within the current sampling cycle |
Three Common Misconfigurations
- A 503 error is prompted when uploading documents via the dialogue box, but background logs show the file was uploaded successfully. Cause: The
messagesparameter of the session context does not limit the document association scope, causing the system to attempt loading bulk documents exceeding storage limits and triggering service current limiting. - An "answer not found" prompt is displayed when retrieving image addresses after using a strict question-and-answer template with the knowledge base. Cause: The prompt does not clearly specify processing rules for image documents, so the system only matches text-based knowledge base content and cannot parse information associated with image addresses.
- The same sampling point information is requested repeatedly during multi-turn dialogue. Cause: The
variablesare not bound to collected business parameters, so the context is reset with each dialogue, causing already collected information to be lost.
How to Verify Successful Configuration
- Initiate a dialogue for formulating a sampling plan, and check whether the batch number bound to
chatIdautomatically appears in the prompt of the dialogue context. - Upload the sampling form document for the corresponding batch, and check whether the system only recalls documents associated with this batch, without mixing materials from other batches.
- Trigger a dialogue for reviewing non-compliant reports, and check whether preset
variablesautomatically fill in business parameters such as the inspection institution name. - After uploading image documents, switch templates for testing, and confirm that the system can correctly associate business information corresponding to the image address.
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.