What This Category's Data Looks Like
The typical entities in this category are bakeries, cookie manufacturers, instant noodle factories, and frozen pastry production facilities. Daily technical documents include formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data sources cover raw and auxiliary material qualification certificates, ERP/MES systems, weighing systems, ovens or frying lines, laboratory equipment, and food traceability ledgers. Documents are updated by batch, shift, or daily. Core fields include allergen material labels, single-batch feeding weight, baking temperature and duration, batch numbers, and other relevant items.
Constraints on Multi-Turn Dialogue and Prompt Configuration
The high-frequency updates, multi-type documents, and specificity of core fields in this category create multiple constraints for multi-turn dialogue and prompt configuration. First, documents updated by batch or shift require conversation contexts to be accurately bound to production batches, to avoid mixing data across batches. Second, core fields include sensitive content such as allergen materials and feeding quantities. Prompts must cover key links such as allergen verification and feeding verification, to ensure no omissions in review. Third, differences across multiple document types require conversations to guide users to clearly specify the current document type being reviewed, to narrow search scope and improve review efficiency.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
messages | Retain the first 6–10 turns of conversation history | The production task cycle for a single batch in this category is short. Excess conversation history will dilute key information for the current review |
variables | Bind the current production batch number and product type | Documents in this category are updated by batch. Variables must be used to accurately match corresponding feeding lists, production records, and other data |
chatId | Generate a unique identifier per production batch | Prevent confusion of conversation contexts across different batches, and ensure review results are linked to correct production batch data |
| Prompt template | Fixed to include three modules: allergen verification, feeding quantity verification, and inspection record matching | The core time-consuming links in this category are formula review and allergen control. Key review points must be covered fully |
| Single-response character limit | 1500–2000 characters | Technical document content in this category is standardized. Overly long responses will increase reading costs for manual review |
Three Common Misconfigurations
- Phenomenon: A new conversation session is created every time a technical document review is initiated, and multi-turn verification for the same batch cannot be continued. Cause: The
chatIdis not bound to the production batch, so the system generates a new session by default, and cannot retain context. - Phenomenon: AI review results are only displayed in the sidebar pop-up window, and are not directly output to the main conversation interface. Cause: The default display rule for conversation output is not configured, and only sidebar search result display is enabled.
- Phenomenon: AI responses automatically use Markdown syntax formatting, which does not conform to reading habits for manual review. Cause: The prompt does not explicitly prohibit format rendering, and the system enables Markdown output by default.
How to Verify Correct Configuration
- Initiate a review conversation for the corresponding production batch, check whether the session retains previous verification records, and confirm that the
chatIdbinding logic is correct. - Enter query content for different batches, check whether the system matches production data for the corresponding batch, and confirm that the
variablesconfiguration takes effect. - Review the review content output by the AI, confirm that Markdown formatting is not used, and that core modules such as allergen verification and feeding quantity verification are covered.
- Test the workflow scenario: after the AI returns results, check whether the question classification node is skipped, and confirm that the branch configuration meets requirements.
The values provided here are common starting points, and should be evaluated against the reader's own samples.