What this type of data looks like
The data used for declaration materials and file review at state-owned asset management companies mainly comes from internal state-owned asset project declaration systems, asset disposal archive libraries, and project approval dossiers. Data updates are triggered alongside individual project declaration workflows. Data is archived and fixed once a project is completed. A single document typically includes fixed fields such as project number, asset category, declared amount, approval node records, and attachment list. Common units and metrics include ten thousand yuan, evaluation benchmark date, and floor area, aligned with professional state-owned asset management scenarios.
Constraints imposed by data characteristics on model access and configuration
Project declaration and file data are updated dynamically alongside individual project workflows. This requires model access links to support real-time association with latest archived project documents. Offline fixed knowledge base copies must be avoided. Single dossier files are lengthy and contain multi-field professional information. This requires adaptation to long-context processing capabilities. Key approval node and asset data must be prevented from being truncated. Field combinations vary across different projects. This requires configuration of model parameters that can flexibly adapt to field extraction. The total document scale processed in a single session must be limited. This avoids exceeding model load and system performance thresholds.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total content length of a single long dossier and associated attachments, avoiding truncation of core approval information |
maxTokens | 2000–3000 token | Controls the length of audit conclusions output by the model in a single round, adapting to the structured output requirements of file review |
quoteMaxToken | 1500 characters | Limits the length of original document fragments referenced by the model, ensuring referenced content is precise and does not occupy excessive context |
temperature | 0.1–0.3 | Reduces output randomness, ensuring consistency and compliance of audit conclusions, and meeting the strict requirements of state-owned asset management |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Adapts to the intranet access requirements of state-owned asset management systems, ensuring secure data transmission |
AIPROXY_API_TOKEN | Bind dedicated permission tokens | Restricts the scope of interface calls, ensuring access security for state-owned asset project data |
Three Common Configuration Errors
- Symptom: Model calls return a 403 permission error. Cause:
AIPROXY_API_TOKENis not configured correctly. An expired token or one without permissions for the target project is used. - Symptom: Irrelevant content from other state-owned asset projects appears in model output. Cause: The context range of
maxContextis not limited. Document data from non-current review projects is loaded. - Symptom: A single review session is interrupted after exceeding the preset duration. Cause:
maxTokensis not set reasonably. The model output exceeds the system timeout limit due to excessive length.
How to Verify Successful Configuration
- Submit a single test dossier document. Confirm the model correctly extracts core fields including project number and asset category. Check that the output includes exclusive information for the current test project.
- Review model call logs. Confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations match the deployment environment. Ensure no connection failure records exist. - Adjust the
temperatureparameter. Submit the same test document multiple times. Confirm that the consistency of output conclusions meets expected standards. - Test input of long documents. Confirm the model can fully process the dossier content. Ensure no key information is truncated.
The values provided 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.