What the data for this category looks like
The main entities in this category are bread and pastry, biscuit, instant noodle, and frozen pastry factories. Daily documents include formulas and feeding lists, production records, baking/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, oven/frying line equipment, laboratory test data, and food traceability ledgers. Update frequency follows production batches, shifts, or daily updates. Core fields include allergen material lists, baking/frying temperature and duration, foreign object detection results, packaging dates, and more.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Products in this category have frequent line changes and diverse formulas. Multi-turn dialogue must quickly isolate context across different batches to avoid mixing cross-batch data. Requirements for allergen management and cleaning switching are strict. Dialogue must retain historical cleaning and allergen verification records to ensure accuracy of defect analysis. Data is updated per batch and linked to multi-stage documents, so prompts must bind batch-related variables to reduce repeated input. The ready-to-eat attribute requires traceability results with no errors. Multi-turn dialogue must fully load all stages of production and test data to support full-link analysis.
How to set up the configuration
This guidance is written for FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind a unique chatId to each production batch | Baked goods and convenience foods have frequent line changes, requiring isolation of dialogue context across different batches to avoid data mixing |
messages | Retain the first 20 historical dialogue messages | Batch traceability requires linking multi-stage test and production records; sufficient historical context supports cross-stage correlation analysis |
variables | Preset batch number, allergen material list, and baking curve parameters as global variables | This category requires frequent verification of formulas and production parameters; binding variables reduces repeated input |
| Maximum context length | 8000–12000 token | Multi-stage data documents are lengthy, requiring full loading of batch-related production and test records |
| Dialogue log retention period | Retain for 180 days after batch archiving | Food traceability requires retaining full cycle data for each batch, complying with industry traceability requirements |
| Default tool call model | Bind the appropriate model based on business scenarios | Different large models have varying output formats and precision to suit different analysis needs, ensuring prompt effects match business goals |
Three common misconfigurations
- Issue: After calling the API to create an application, multi-turn dialogue context is lost, and replies cannot link to historical production records. Cause: No unique
chatIdbound to the production batch, so dialogue from different batches is mixed into the same context window. - Issue: After connecting a specified large model, reply effects from the same prompt differ from those on the native platform. Cause: The
variablesparameter for the corresponding model is not bound in the dialogue configuration, and the prompt does not adapt to the model's output format requirements. - Issue: After the Docker container restarts, previously saved dialogue logs are lost and cannot be recovered. Cause: Logs are not mounted to a persistent storage volume, and logs are not archived by batch; local container files are cleared upon restart.
How to confirm the configuration is complete
- Initiate a new dialogue and enter the batch number of a specific batch, check if the dialogue interface automatically loads preset variables such as formulas and production records linked to that batch.
- Submit 3 consecutive questions about foreign object detection and baking parameters for the same batch, check if replies link to batch information from historical dialogue with no context break.
- Restart the Docker container, check if previously saved dialogue logs can still be retrieved in the workspace.
- Switch the large model, use the same allergen verification prompt to ask a question, check if the reply format complies with preset field output requirements.
The values provided here are common starting points, and should be evaluated against the user's own samples.