What This Category’s Data Looks Like
The data sources for this category include animal quarantine information, supplier batches, production traceability systems, temperature collection equipment, metal detection instruments, and laboratories. Daily documents cover quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data is updated per batch and per shift, with continuous real-time collection of temperature control data. Document fields include batch number, quarantine number, temperature value, test results, and more. Units include degrees Celsius, batch numbers, test indicator values, and more.
Constraints on Multi-turn Dialogue and Prompt Configuration
Multi-turn dialogue and prompt configuration for this category must adapt to the following constraints. First, data is updated per batch and shift and closely linked. Bind the corresponding batch context during dialogue to avoid mixing cross-batch data. Second, real-time temperature control data volume is large and requires fast querying. Prompts must support fast extraction of time-series data. Third, document fields include multiple types of professional information such as quarantine numbers and temperature values. Prompts must clarify field extraction rules to prevent confusion of units or identifiers. Fourth, audit results are directly linked to compliance risks. Add compliance verification logic to prompts to ensure output meets category requirements.
How to Configure the Setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | First 1200–1800 characters | Matches the average length of single-batch production records to avoid truncating critical batch-related information |
| Question optimization switch | Enabled | Automatically splits multi-turn quality inspection queries to adapt to the step-by-step logic of multi-link audits |
| Variable binding | Bind variables as production batch number | Associates sessions with corresponding production batch data to ensure accurate context matching |
| Session ID configuration | Bind chatId | Differentiates sessions from different production lines or shifts to avoid context confusion |
| Context recall count | First 6–8 entries | Covers core audit link records for a single shift to avoid interference from redundant data |
| Dialogue message format | Enable messages array format | Adapts to context transfer rules for multi-turn dialogue to ensure historical messages are correctly recognized |
| Timeout setting | 60 seconds | Adapts to the fast query requirement of real-time temperature control data to avoid long wait times |
Three Common Mistakes
- Phenomenon: LaTeX formatted content renders correctly in debug preview, but only raw LaTeX code is displayed in the published dialog box. Cause: The rich text rendering switch was not enabled in the publish configuration. This function is enabled by default only in the local debug environment.
- Phenomenon: A Docker-deployed instance cannot initiate dialogue normally, and stays stuck at the AI response stage for a long time. Cause: Network permissions for external API calls were not configured correctly during deployment, preventing model requests from transferring normally.
- Phenomenon: The results of the AI dialogue node inserted in the workflow are automatically included in the final output content. Cause: The "Output node results" option was not disabled in the workflow node configuration. All node content is spliced into the final output by default.
How to Confirm the Configuration Is Complete
- Initiate a multi-turn query that includes a production batch number, and check whether the dialogue context automatically associates the corresponding batch’s production records and quarantine information.
- Submit test formula content in LaTeX format, and check whether the rendering effect in the published dialog box matches the debug preview.
- Trigger workflow execution, and confirm that the final output only includes the preset audit conclusion and does not include redundant content from intermediate AI dialogue nodes.
- Simulate the post-deployment call scenario, and check whether the dialogue response time meets the preset timeout configuration requirements.
The values provided are common starting points and should be measured against the reader's own samples.