What this category of data looks like
This category of data primarily comes from food safety sampling and monitoring systems, the State Administration for Market Regulation sampling results query system, laboratory LIMS systems, and food producer and operator ledgers. Data is updated monthly. Core documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control announcements. Data fields include sampling identifiers, inspection items, non-conformity judgment bases, disposal process nodes, and no additional custom statistical fields. All data revolves around the full sampling and inspection workflow.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Batch data updated monthly requires dialogue contexts to sync the latest sampling results regularly. This prevents the use of expired data. Multi-source heterogeneous document structures have large differences. Prompts must adapt to field extraction logic for different documents. This ensures accurate matching of content such as sampling plans and inspection reports. Scenarios with multiple participating entities require multi-turn dialogue to clarify key information like sampling stages and report numbers to be queried. This reduces ambiguity in inquiries. Q&A related to non-conformity disposal must strictly match the disposal processes in documents. It must not generate content beyond the scope of the documents.
How to configure the settings
These configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Use sampling numbers as the binding value | Ensures multi-turn dialogue contexts for the same sampling task are linked, avoiding confusion across tasks |
messages | Retain the most recent 8-12 rounds of interactions, and concatenate them in the order of system prompts, user questions, and historical replies | Matches the multi-turn inquiry logic of regulatory personnel, and controls context load |
variables | Inject the effective time of the current sampling batch and the list of inspection items | Limits the Q&A scope to the current task, reducing irrelevant search results |
| Maximum context length | 8000-12000 characters | Adapts to the length after splicing multi-source documents, avoiding exceeding model processing thresholds |
| Question optimization switch | Enabled | Automatically completes vague inquiries from regulatory personnel. For example, it completes "non-conforming report" to "non-conforming food inspection report corresponding to the specified sampling number" |
| Single interaction return limit | Top 3 matching documents | Avoids outputting excessive information that interferes with regulatory personnel's judgment |
Three common configuration mistakes
- Symptom: Dialogue output is displayed directly in page pop-ups, without going through the specified text splicing component. Cause: No output interception rules for the dialogue node are configured. The AI reply is directly bound to the default page rendering component.
- Symptom: After knowledge base search results are passed to an HTTP request, the AI cannot correctly reference sampling report content. Cause: Search results are not spliced according to the field structure of the category documents. Key identifiers such as sampling numbers and inspection items are missing.
- Symptom: Multi-turn dialogue cannot use the previous round's AI output as input for the next round. Cause: The
variablesparameter is not correctly bound to pass historical interaction results. This leads to contexts not being loaded correctly.
How to confirm the configuration is complete
- Initiate a simulated sampling inquiry. Check if the AI reply only associates with the currently bound sampling number, and no sampling data from other batches appears.
- View the output logs of the dialogue node. Confirm that the AI reply is correctly passed to the specified text splicing component, and is not directly rendered on the page.
- Pass the previous round's AI output as the user input for the next round. Check if the context is correctly loaded, and the reply aligns with the logic of the current sampling task.
- Upload an inspection report image and initiate an inquiry. Check if the AI can correctly extract non-conforming items and judgment bases from the report.
The values provided here are common starting points. They should be evaluated 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.