What data looks like for this category
Typical enterprises in this category include panel furniture, solid wood furniture, custom home and door manufacturers. Daily generated documents include order breakdown drawings, cutting lists, edge banding and drilling programs, coating processes, hardware lists, assembly and packaging records. Data mainly comes from order design systems, sheet and wood batch records, CNC cutting and drilling equipment, ERP/MES systems, and moisture content testing equipment. Data update frequency is per order, daily, or per material batch. Documents are available in text, table, and image formats. Fields include panel dimensions, material batches, process parameters, hardware models, and there is no unified standard format.
What constraints do these characteristics impose on model access and configuration
The multi-source heterogeneous data, high-frequency updates, and customization characteristics of this category impose multiple constraints on model access and configuration. First, data includes image-based documents such as breakdown drawings and CNC programs, so models must support visual parsing capabilities to accurately extract key information like panel dimensions. Second, custom order-driven production modes result in process documents containing large amounts of panel and hardware information, so model context capacity must support long-text processing to avoid truncating critical data. Third, inconsistent field formats from multiple industrial systems require models to have strong structured understanding capabilities to ensure fields in abnormal reports match original data. Fourth, high-frequency updated data requires controllable API call latency to meet real-time process document generation requirements.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | Adapt to the total length of process documents for custom furniture with multiple panels, avoid truncating core information such as breakdown lists and hardware details |
temperature | 0.2–0.4 | Maintain accuracy of process descriptions and abnormal reports, reduce unnecessary subjective deviations |
maxTokens | 1500–2000 characters | Match the average length of single cutting lists and coating process documents, ensure complete processing of single-batch data |
vision | Enabled | Parse image-based process documents such as breakdown drawings and CNC drilling programs, extract key information including panel dimensions and hole position parameters |
AIPROXY_API_ENDPOINT | Fill in using domestic nodes provided by the FastGPT v4.17.0 platform | Adapt to domestic access latency, meet requirements for real-time process document generation |
AIPROXY_API_TOKEN | Associate with bound enterprise accounts | Ensure API call permissions and data isolation, prevent unauthorized access |
Three common mistakes
- Message receiving address verification fails, with prompts of public network inaccessibility or SSL certificate issues. Cause: FastGPT is not deployed to a publicly accessible server, or a valid SSL certificate is not configured and certificate verification is not disabled.
- The model fails to accurately extract panel dimensions and hole position information from breakdown drawings. Cause: The
visionparameter is not enabled, so image-based process documents cannot be processed. - Generated abnormal reports have missing fields or truncated content. Cause: The
maxContextparameter is set too small, truncating original hardware list or assembly record data from order systems.
How to confirm configurations are correct
- Test calling the DingTalk interface, send a simulated process document generation request, check if results are returned normally with no verification errors.
- Upload a breakdown drawing and cutting list, verify that the model can accurately extract core information such as panel dimensions and hardware models.
- After adjusting the
temperatureparameter, compare two generated abnormal reports to confirm result stability meets expectations. - Check API call logs to confirm that the
AIPROXY_API_TOKENpermission configuration is correct, with no failed call records.
The values provided here are common starting points, and should be evaluated against the reader's own samples.