What this category of data looks like
This category of data originates from state-owned assets supervision information systems, enterprise asset management systems, property rights exchange systems, evaluation agency reports and financial statements, with a monthly update frequency. Daily documents include property rights registration forms, asset evaluation reports, property rights transfer plans, on-site transaction announcements, and asset disposal approval forms. Each document includes fixed fields such as ownership number, asset category, book value, evaluation base date, and transaction target. The volume of individual documents varies significantly.
What constraints these characteristics impose on model access and configuration
Monthly updated data sources require scheduled full synchronization logic to be configured during model access, to prevent data from lagging behind business update cycles. Differences in document formats across multiple sources require adapted parsing rules in the configuration, to accurately extract core fields like ownership details and evaluation values. Fixed field business requirements mandate that model output strictly matches preset fields, with no irrelevant or incorrect content allowed. Significant variation in single-document volume creates constraints for context length limits and recall fragment screening rules. A balance must be struck between recall accuracy and response speed.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the volume of single documents such as property rights transfer plans and asset evaluation reports, avoiding context overflow |
maxTokens | 2000–3000 token | Controls the output length of single-round responses, ensuring concise and clear responses to service inquiries |
quoteMaxToken | 1500–2000 characters | Limits the total length of recalled fragments, matching the typical fragment volume of business documents |
temperature | 0.1–0.3 | Reduces response randomness, ensuring accuracy of key business information such as ownership details and evaluation values |
AIPROXY_API_ENDPOINT | Fill in the corresponding address based on the deployment environment | Adapts to the access path of self-built proxies, ensuring connectivity for model calls |
AIPROXY_API_TOKEN | Set based on actual testing | Matches the authentication requirements of the proxy service, avoiding unauthorized access errors |
Three common misconfigurations
- A 401 unauthorized error is returned when calling the model. The cause is incorrect configuration of
AIPROXY_API_TOKENor an invalid proxy address, leading to authentication failure. - The AI model dropdown list in the workflow is empty. The cause is that the basic configuration of the model proxy has not been completed, or the proxy service does not comply with the access standards of FastGPT v4.17.0.
- Recalled asset fragments do not match the current inquiry. The cause is that
quoteMaxTokenis not set reasonably, leading to recalled fragments that are too long or too short to accurately match business requirements.
How to confirm the configuration is complete
- Run a model connectivity test, check whether the returned result contains valid business fields, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. - Upload a single asset evaluation report for testing, check whether context loading operates normally, and confirm that the values of
maxContextandquoteMaxTokenmatch the document volume. - Initiate an inquiry related to property rights transfer, check whether the model output strictly matches fixed fields such as ownership number and evaluation value, and confirm that the
temperaturevalue meets business requirements. - Trigger the monthly data synchronization task, check whether the synchronization log is completed normally, and confirm that the scheduling configuration matches the data source update frequency.
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.