What Data for This Category Looks Like
This category’s data comes from registered or filed formulas, raw material safety documents, weighing and emulsification equipment logs, ERP/MES systems, laboratory test records, and packaging material version documents. Data updates finish with each production batch and each line change. Daily documents include formula process sheets, weighing and feeding records, emulsification or filling records, cleaning and sanitization records, semi-finished and finished product test reports, and label review forms. Core fields include raw material INCI names, feeding weights, production batch numbers, emulsification temperatures, homogenization durations, and packaging material models. No additional percentage-based statistical data is included.
Constraints on Multi-Turn Dialogue and Prompt Engineering
This category’s data updates in real time with each production batch, and covers multiple types of scattered documents. Multi-turn dialogue must dynamically retrieve the latest data for the corresponding batch, such as filed formulas and equipment logs. It must avoid using static knowledge base content. Fields across different document types vary. Prompts must guide the AI to integrate information from different sources. For example, link feeding record weights to allowed dosages in filed formulas. Production compliance rules are strict. Errors may trigger product removal or recall. Multi-turn dialogue must gradually verify raw material compliance, production parameter matching, and label claim consistency. No drug-related treatment verification logic may be used.
Configuration Settings
The following configurations apply to FastGPT v4.17.0 workflow node settings.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables | Pass the current production batch number, workshop ID, and operator ID | Adapt to dynamic data retrieval needs for each production batch, ensure dialogue links to corresponding batch filings and production records |
chatId | Bind to the current production work order ID | Connect dialogue logs to work orders, enable traceability of review processes later |
messages | Concatenate historical dialogue and current requests, grouped by document type | Ensure coherent context for multi-turn dialogue, let the AI complete cross-review step-by-step |
| Maximum single-turn dialogue token count | 8000–12000 tokens | Cover information needed to integrate fields across multiple document types and run compliance checks, calibrated via actual testing |
| Dialogue timeout setting | 600 seconds | Match average review durations for production processes, avoid delaying work order progress |
| Maximum retry count | 3 | Resolve field mismatches or missing data during single verification runs |
Three Common Misconfigurations
- Phenomenon: When running multi-turn dialogue in bulk, some nodes return verification results that do not match the target production batch. Cause: No independent
chatIdassigned to each execution node, leading to conflicting dialogue contexts across different work orders. - Phenomenon: Specific production work orders or operators cannot be linked in dialogue logs. Cause: Work order ID and operator information from
variablesare not passed to the dialogue node, so logs only record generic dialogue content. - Phenomenon: The AI cannot recognize uploaded JPG-format packaging material design drawings. Cause: No tool node supporting image parsing is connected before the dialogue node. Only text-format review parameters are passed, so image information is not read.
How to Verify Proper Configuration
- Initiate a review dialogue for a test work order. Check if dialogue logs include relevant identifiers for the current test work order, confirm the
chatIdbinding logic works. - Upload the filed formula and feeding record for the corresponding batch. Initiate multi-turn verification, check if the AI automatically links fields from the two document types, confirm the
variablesdynamic parameter logic works. - Upload a JPG-format packaging material design drawing. Check if the dialogue node correctly parses the image content and includes it in the verification scope, confirm the image tool node configuration is correct.
- Trigger a bulk execution task. Check if each child node’s dialogue logs are independent with no context confusion, confirm the
chatIdallocation logic works.
The values provided are common starting points and should be measured against the reader's own samples.