Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from engineering quality and safety supervision systems, on-site construction real-name management platforms, supervision logs, and test reports. The update frequency is weekly. Daily documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Fields include inspection points, rectification deadlines, acceptance conclusions, and more. Some documents include on-site photos and test values, with no unified standard units.
Constraints imposed by these characteristics on model access and configuration
The weekly updated data scale is fixed. Limit total context capacity to avoid loading expired redundant data. Documents from multiple sources have significant format differences. Configure parsing rules that support structured fields and unstructured attachments. On-site inspection records include photo attachments. Adapt visual processing capabilities to extract hidden danger descriptions from images. Fields related to hidden danger rectification closure must be accurately matched to prevent confusion between ledger entries from different stages and ensure the accuracy of summarized data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Adapts to the summary length of weekly batch ledgers and avoids context overflow |
maxTokens | 4000–6000 tokens | Covers the generation requirements for long documents such as completion acceptance supervision reports |
quoteMaxToken | 1500 tokens | Limits the length of quoted ledger fragments to ensure clear report typesetting |
vision | Enabled | Adapts to the extraction of on-site photos and numerical charts from supervision logs and test reports |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address of the deployment node | Ensures compliant and stable model call links |
AIPROXY_API_TOKEN | Use a dedicated authentication key | Isolates model call permissions for different projects to ensure data security |
Three common misconfigurations
- The generated report includes the text "Citation Marker: [1]". Cause: The
quoteMaxTokenconfiguration to limit the output format of quoted fragments is not enabled, causing the system to add a default citation identifier. - Model calls return empty results. Cause: The
visionconfiguration is not enabled, so on-site photo attachments in test reports cannot be parsed, leaving unstructured data unprocessable. - Single ledger summary takes excessive time. Cause: The
maxContextvalue is too large, exceeding the model's context capacity limit and triggering a timeout restriction.
How to confirm configuration is complete
- Upload a single supervision inspection record and test report to confirm the model correctly extracts inspection points and test values, verifying that the
visionconfiguration is active. - Submit a weekly ledger summary request and check if the generated report includes the specified document entries, confirming that
maxContextsupports batch data loading. - Review model call logs to confirm no configuration errors exist for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, and that no call failures are recorded. - Generate a report with quoted fragments to confirm no extra citation marker text is included, verifying that the
quoteMaxTokenconfiguration is appropriate.
The values provided here are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.