What the data for this category looks like
Engineering consulting supervision ledger data primarily comes from on-site supervision logs, on-site inspection and acceptance records, material inspection reports, progress payment application forms, and other frontline documents. Data is submitted daily for same-day on-site records. Weekly ledgers are summarized each week. Periodic reports are formed monthly. Single ledger documents contain structured fields and unstructured notes. Structured fields include supervision section, material batch, acceptance pass rate, rectification closed-loop status. Unstructured fields include on-site problem descriptions and rectification plans. Field units include square meters, tons, workdays, and other standard units.
What constraints do these data characteristics impose on model access and configuration
The mixed structured and unstructured document content requires the model to complete both structured field extraction and unstructured semantic understanding, to avoid format conversion deviations. The large volume of accumulated ledger documents each month requires configuring parameters adapted for long context processing, to prevent truncation of critical rectification records and acceptance data. Diverse field units require the model to accurately identify and retain unit information, to prevent data distortion. Daily updated real-time data requires call latency to match the business rhythm, to avoid timeouts that disrupt the summary process.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total document length of monthly accumulated ledgers, preventing truncation of critical rectification records and acceptance data |
maxTokens | 2000–3000 token | Matches the content length of a single generated report, ensuring the summary result fully covers all ledger entries |
quoteMaxToken | 1500 characters | Retains sufficient original ledger fragments as reference basis, meeting the traceability requirements of supervision reports |
temperature | 0.3–0.5 | Reduces generation randomness, ensuring structured field extraction results strictly match ledger data |
toolChoice | auto | Automatically selects appropriate tools to complete field summary and format organization, adapting to diverse ledger document structures |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Matches the model service address for local or cloud deployment, ensuring normal call connectivity |
Reference only; verify against the target FastGPT release and deployment documentation.
Three common configuration errors
- The model returns empty content after a call. Investigation shows
AIPROXY_API_TOKENis configured incorrectly: the platform-assigned secret key is not filled correctly, causing authentication failure. - Report field units are missing. The cause is that a suitable
maxContextparameter is not configured, leading to truncation of unit information from original ledgers. - Single call times out. The cause is that
maxTokensis set too large, exceeding the single request limit of the model service, causing the request to be interrupted.
How to confirm configurations are correct
- Upload a single standard supervision ledger document, trigger a model call, and check if the returned result fully includes all structured fields and unstructured notes.
- View the model call log, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the current deployment environment, with no authentication errors. - Adjust the
temperatureparameter to 0.3, generate summary reports for multiple identical types of ledgers, check for consistent field results with no random deviations. - Batch upload monthly accumulated ledgers, check if the
maxContextparameter adapts to the total document length, with no content truncation.
Note: The configuration values listed above are common starting points. They should be evaluated against the reader's own sample data.
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.