This configuration is based on FastGPT v4.17.0.
What this category’s data looks like
The core data for this category comes from purchase orders, drawing specifications, supplier quality certificates, inspection equipment logs, historical quality ledgers, and supplier portals. Daily processed documents include inspection specifications, sampling plans, incoming material inspection reports, supplier audit reports, 8D exception reports, concession acceptance approval forms, and more. Data updates generate new reports with each batch of incoming materials. Supplier quality evaluations are completed monthly or quarterly, with ad-hoc special audits conducted periodically. Most documents contain structured fields such as batch number, supplier code, inspection items, judgment results, and rectification requirements. Some drawing documents include non-standard parameters.
Constraints on workflow orchestration from these data characteristics
The data characteristics of this category impose multiple constraints on workflow orchestration. Data sources are scattered, covering internal procurement systems, external supplier portals, and inspection equipment. Workflows must support cross-source data pulling and format compatibility. Update rhythms vary: there are real-time tasks triggered by each batch of incoming materials, as well as periodic monthly or quarterly evaluation tasks. Workflows must support concurrent configuration of event-triggered and scheduled triggers. Document types are diverse, including structured reports and non-standard drawings. Workflows must adapt to multi-format parsing logic. Key fields such as batch number and supplier code require mandatory validation to avoid confusion between cross-batch data. Approval nodes must be embedded to balance quality control and production supply needs.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger method | File upload event + daily scheduled polling | This category has both immediate documents uploaded with each batch of incoming materials and periodic monthly supplier evaluation tasks. Dual triggers cover all business scenarios |
variables | Pre-set batch number, supplier code, inspection item enumeration values | Documents in this category mostly include these key identifiers. Pre-set variables reduce repeated data entry and improve data consistency |
| HTTP request node timeout | 300 seconds | Supplier portal data pulling may experience network delays. 300 seconds covers most normal request durations |
nodeId | Bind to the corresponding parsing node ID by document type | This category has diverse document types. Binding corresponding nodes ensures parsing accuracy for files of different formats |
| File classification rules | Distinguish image and non-image files by file extension + content characteristics | Files uploaded for this category include inspection photos, reports, drawings, and more. Classification allows matching to corresponding processing logic |
| Node execution retry count | 2 times | Network fluctuations may cause request failures. 2 retries reduces task interruption rates |
Three common configuration mistakes
- Phenomenon: After a workflow uploads a file, inspection photos are processed using the structured report parsing logic, resulting in missing result fields. Reason: No file classification rule combining file extension and content characteristics is configured. Using only a single file extension to judge file type fails to accurately distinguish between image and document files.
- Phenomenon: After a workflow triggers, it directly enters the AI conversation link without executing the preset HTTP request node. Reason: No correct execution trigger condition is configured for the HTTP request node, or the
nodeIdbinding is incorrect, causing the workflow to skip this step. - Phenomenon: After multi-node deployment, some tasks cannot read historical quality data and supplier ledgers. Reason: No unified shared storage mount is configured. Each node cannot access shared business data, resulting in missing task-dependent data.
How to confirm a correct configuration
- Upload a test file set containing inspection photos and reports, and verify that files are correctly classified to corresponding processing nodes.
- Trigger scheduled tasks and manual upload events, and confirm that the HTTP request node executes as configured and retrieves external supplier data.
- View task logs for multi-node deployments, and confirm that each node can access historical quality data in shared storage.
- Trigger a test process for non-conforming judgments, and confirm that the approval node triggers as configured and enters the corresponding verification link.
The values provided are common starting points and should be evaluated against the reader’s own samples.