What Data Looks Like for This Category
Typical entities in this category are bread and pastry, biscuit, instant noodle, and frozen pastry factories. Daily documents include recipes 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, laboratories, and food traceability ledgers. Data updates follow production batch, shift, or daily schedules. Document fields include batch number, allergen labeling, temperature and time parameters, feeding quantity, and more. The structure of single-batch documents changes frequently with product line switches.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
This category has frequent line changes, data updated by batch or shift, and prominent allergen and cleaning requirements. These factors create clear constraints for multi-turn dialogue and prompt configuration. Bind chatId to sessions for different production scenarios to avoid context confusion across shifts and batches. Pass exclusive parameters for the current batch via variables to ensure the prompt accurately retrieves corresponding recipes, baking curves, and cleaning records. Limit the historical turn count of messages to prevent overly long contexts from introducing redundant data from unrelated batches, which reduces the accuracy of inspection and maintenance Q&A.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Unique identifier for the current session (e.g., factory ID + shift ID) | Distinguish session histories across production shifts to avoid cross-batch context confusion |
messages | Retain the previous 3–5 rounds of conversation content | Adapt to the frequent line change scenario of this category, avoid introducing redundant data via overly long contexts |
variables | Pass in the current batch's recipe ID, baking temperature range, and cleaning shift time | Accurately retrieve exclusive documents and parameters for the current production scenario |
| Maximum context token count | 8000–12000 tokens | Balance retrieval efficiency and context completeness; this value fits most production scenarios under FastGPT v4.17.0 |
| Question Optimization | Enabled | For frequent line change scenarios, optimized questions can accurately match production records for the current batch |
| Single Retrieval Limit | Top 4 results | Documents in this category are updated by batch; too many results will interfere with accurate judgment in the current session |
Three Common Misconfigurations
- Phenomenon: Only single-turn questions are supported, and follow-up questions cannot reference previous device status or batch information. Cause: The
messagesparameter is not enabled to pass historical conversation context, so the system cannot remember the previous question background. - Phenomenon: Outputs from all AI dialogue nodes in the workflow appear in the final session interface, and the output content of specified nodes cannot be hidden. Cause: Context hiding rules for nodes are not configured, or non-essential output fields are not filtered via
variables. - Phenomenon: Retrieved equipment maintenance recommendations do not match the allergen requirements of the current batch. Cause: The
variablesare not used to bind the current batch's allergen parameters in the prompt, resulting in retrieval of cleaning and maintenance records from unrelated batches.
How to Verify Proper Configuration
- Initiate an equipment inspection query, enter the current batch's device model and shift information, and check whether the system can follow up with follow-up questions referencing this information.
- Trigger workflow execution, and check whether the output of AI dialogue nodes configured for hiding does not appear in the final session interface.
- Pass in the current batch's allergen variable, and confirm that the retrieved equipment maintenance recommendations match the cleaning and disinfection requirements of this batch.
- Switch sessions using different shift
chatIdvalues, and confirm that the historical contexts of the two sessions do not interfere with each other.
The values provided are common starting points and should be measured against the reader's own samples.