What the data for this category looks like
Typical entities for this category are bread, pastry, cookie, instant noodle and frozen pastry factories. Daily documents include recipe 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. Update frequency is per batch, per shift, or daily. Document fields include allergen labels, feeding amounts, baking temperature and duration, shelf life, and other items. Units include degrees Celsius, seconds, kilograms, and other standard units.
What constraints these characteristics impose on workflow orchestration
Data sources for this category are scattered, and update frequency is high. Workflows must support connecting multiple production system nodes to pull real-time updates of baking curves or feeding data. Frequent line changeover requires workflows to dynamically switch bound knowledge bases, to avoid mixing process parameters across different products. Core review links are time-consuming, and the consequences of errors are significant. Workflows must include built-in check nodes to automatically verify key indicators such as allergen controls and foreign matter detection. Batch production models require workflows to trigger per production batch or shift, to support parallel processing of multiple batches.
How to configure the workflow
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
variables configuration | {"kb_id": "Bound dedicated process knowledge base ID", "batch_no": "Current production batch number"} | This category requires associating corresponding production data per batch. Global variables simplify calls across multiple nodes |
nodeId configuration | Select the IDs of three core nodes: recipe verification, baking parameter matching, and allergen review | Frequent line changeover for this category requires precise triggering of verification for corresponding process links |
| File upload validation rules | Only allow uploads of .xlsx and .pdf formats, with a maximum file size of 100 MB | Daily documents for this category are mostly recipe spreadsheets and baking curve PDFs. Oversized files will slow down workflow execution |
| Node timeout setting | 600 seconds | Most data for this category comes from production equipment, so sufficient time must be reserved to pull data such as baking curves |
| Trigger timing setting | Trigger upon completion of a production batch, or on a scheduled basis per daily shift | Update frequency for this category's data is per batch, per shift, or daily, so configuration must match this rhythm |
| Knowledge base binding setting | Bind a dedicated process knowledge base, do not use a globally shared knowledge base | Recipe and process parameters for this category change frequently with line switchovers. A dedicated knowledge base improves retrieval accuracy |
Three common configuration mistakes
- Calling the workflow API returns a
400 Bad Requeststatus code, with a prompt indicating missing parameters. The cause is failure to correctly configure thevariablesparameter in the request body, and failure to pass the bound knowledge base ID or production batch identifier. - After uploading a file, the workflow remains in the pending state. The cause is that the uploaded file format exceeds the configured allowed range, or the file size exceeds the 100 MB limit.
- After workflow execution, no corresponding process parameters are matched, and search results are empty. The cause is failure to bind a dedicated process knowledge base, and use of a globally shared knowledge base instead, which cannot match exclusive recipes and parameters for this category.
How to confirm the configuration is correct
- Call the workflow API, pass a test production batch identifier and knowledge base ID, and check if the returned results include preset core fields.
- Upload a test document that matches the configured format, and check if the workflow automatically triggers the analysis process.
- View the workflow node logs, and confirm that the
variablesparameter andnodeIdhave loaded correctly. - Trigger the workflow, and check if the output results include key indicators such as allergen compliance and baking parameter matching.
The values provided are common starting points and should be evaluated against the reader's own samples.