What Data Looks Like for This Category
Data for this category originates from raw material qualification certificates, ERP/MES system records, weighing equipment logs, oven and frying line operation records, laboratory test records, and food traceability ledgers from bread, pastry, biscuit, instant noodle, and frozen pastry factories. Daily documents include formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory release inspection reports. Fields include material name, feeding weight, baking temperature and time, allergen labeling, shelf life, and related items. Data updates occur per production batch, per shift operation, and daily archiving.
Constraints for Multi-turn Dialogue and Prompt Engineering
This category has a large number of formulas and frequent line changes. Allergen and cleaning switching requirements are strict. Multi-turn dialogue must continuously associate exclusive data for the current production batch to avoid mixing cross-batch information. Data updates occur frequently and trigger per production batch. Prompts must support dynamic loading of the latest single-batch feeding lists and test records. Document fields include process parameters with units such as temperature and time. Multi-turn dialogue must accurately match field units to prevent incorrect parameter use. Products in this category are for direct consumption. Q&A results must strictly align with allergen labeling and cleaning records. This avoids missing key compliance information and ensures production and food safety.
Configuration Setup
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context length | 800–1200 characters | Matches the average length of single-batch production documents, avoids redundant information interfering with current process Q&A |
| Question optimization switch | Enabled | Automatically optimizes wording of current batch process questions for frequent line change scenarios, improves retrieval accuracy |
| Single search limit | Top 6 results | Covers retrieval needs for multiple document types per single batch of this category, avoids missing key process parameters |
chatId binding rule | Bind current production batch ID | Ensures multi-turn dialogue only links data for the current batch, prevents cross-batch information mixing |
variables injection configuration | Inject current batch allergen list | Lets dialogue match current production allergen control requirements in real time |
messages context truncation threshold | Keep last 3 conversation turns | Avoids irrelevant historical information interfering with current batch process Q&A |
Three Common Misconfigurations
- Workflow execution hangs at the AI dialogue step with no response. The workflow node stays stuck on the AI dialogue step, with no returned results. This issue occurs when querying process questions for specific production batches. The root cause is failure to bind the
chatIdfor the current production batch. This stops the dialogue context from linking to exclusive data, triggering an interruption. - Streamed output function does not work correctly. After API calls, no segmented content returns. Only full results are sent. The root cause is failure to enable the streamed return switch in dialogue configuration, and failure to adapt front-end rendering logic. New engineers often miss this configuration item due to limited front-end expertise.
- Conversation log annotation cannot link to corresponding documents. After annotating logs, no matching retrieved process procedure content appears. Annotation information does not align with actual retrieval results. The root cause is failure to inject document IDs and
variablesparameters into the prompt. This prevents annotations from binding to corresponding document fragments.
How to Verify Successful Configuration
- Trigger a single-batch process question query. Check that the dialogue context only includes exclusive data for that batch, such as feeding lists and baking curves.
- Call the API to start a dialogue. Verify that historical messages are truncated according to the set maximum context length, avoiding redundant information interference.
- View conversation logs. Confirm that the annotation function can link to specific retrieved document fragments, and that annotation information matches retrieval results.
- Test allergen-related questions. Confirm that dialogue results automatically match the current batch’s injected allergen list, with no omissions or errors.
The values provided here are common starting points. They should be evaluated against the reader’s own samples.