What data looks like for this category
This category’s core data originates from order design systems, sheet material and wood batch ledgers, CNC cutting and drilling equipment logs, ERP/MES systems, and on-site moisture content testing devices. Daily output documents include custom cutting plans, cutting lists, edge banding and drilling processing programs, coating process cards, hardware material lists, and assembly and packaging records. Data updates align with order creation, daily batch ledger updates, and the arrival of each material batch. Document fields include panel size parameters, sheet batch identifiers, processing step numbers, hardware models, and some documents include structured numerical values and equipment operation logs.
What constraints these characteristics impose on model integration and configuration
This category’s custom order-driven workflow requires model integrations to adapt to multi-source heterogeneous production documents. Integrations must support parsing non-standard structured documents including custom cutting plans and cutting lists. Frequently updated order and batch data requires reserving interface adaptation space for dynamic data synchronization during configuration. The large volume of panels and strict dimensional accuracy requirements mean models must accurately match size and process fields in documents. This prevents production errors caused by field recognition deviations. Cross-device and system data sources also require configuration to support importing logs and ledger data in different formats.
How to set up the configuration
This document is written for FastGPT v4.17.0. All following configuration items adapt to the interface and parameter logic of this version.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–16000 token | Order documents for this category are mostly long-text cutting plans and cutting lists. Sufficient context must be retained to associate multiple panel parameters |
maxTokens | 2000–4000 token | Inspection and audit tasks require outputting structured verification results. This avoids overly long outputs exceeding the processing limits of production workflows |
quoteMaxToken | 1000–2000 characters | Specific fields in cutting lists and hardware lists must be accurately referenced. This avoids redundant reference content interfering with verification logic |
| Workflow/API tool selection | auto | This scenario requires alternating calls to document parsing and field verification tools. Automatic mode can adapt to dynamic task workflows |
AIPROXY_API_ENDPOINT | Fill in the actual address of the deployed proxy service | Must connect to the enterprise's internal model proxy service to ensure production data is not leaked |
temperature | 0.1–0.3 | Inspection and audit tasks require consistent and accurate output. This avoids random deviations leading to inconsistent verification results |
Three common mistakes
- Symptom: The platform displays a no available channels prompt. Restarting the proxy service and confirming no group restrictions do not resolve the issue. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly. This prevents the proxy service from forwarding model requests normally. - Symptom: Some PDF-format cutting plans and process files upload with empty content, while other files are recognized normally. Cause: The
visionparameter is not enabled, or the configured model does not support visual parsing capabilities. This cannot extract graphical dimension annotations and process information from PDFs. - Symptom: The model does not call the knowledge base to output verification results by default, and only returns generic responses. Cause: The knowledge base association switch is not enabled during configuration, or the
maxContextparameter is set too small. This fails to trigger the knowledge base recall logic.
How to confirm the configuration is complete
- Import a single typical cutting list PDF, and confirm the model can extract fields such as panel size and batch number with no empty content.
- Call the model to initiate an inspection and audit request, and confirm the returned result associates production data from the knowledge base with no generic responses.
- Check the proxy service logs, and confirm there are no connection errors related to
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKEN. - View the database configuration page, and confirm table model information for each database can be pulled normally with no permission or connection error prompts.
The values provided are common starting points and should be measured against the reader's own samples.