What data in this category looks like
This category of data primarily comes from local public data open platforms, integrated big data platforms, departmental business databases, and electronic certificate repositories. The update frequency is once per month. Daily documents include public data resource catalogs, data opening lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. The document structure primarily uses structured field combinations, including core fields such as data item name, opening scope, desensitization requirements, interface address, and update cycle, with no additional unstructured redundant content.
What Constraints These Characteristics Impose on the Model Access and Configuration Link
Fixed monthly updated data sources require configurations that support periodic data synchronization verification to avoid inconsistencies between the catalog and actual data. The predominantly structured document structure requires model inputs to adapt to structured field extraction, avoiding redundant overhead from unstructured parsing. Multi-source data requires unified formatting, so configurations must support format conversion rules for multiple data sources to ensure data consistency. The association between desensitization rules and interface documents requires model configurations to bind desensitization parameters for corresponding data items to prevent data leaks. For the long-text file review scenario, the context window configuration must adapt to the length of a single batch of review materials to avoid truncation of critical information.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the full catalog documents updated monthly and the context length of a single batch of file reviews, avoiding truncation of critical information |
temperature | 0.1–0.3 | Used for rigorous file review scenarios, reduces output randomness to ensure consistency of review results |
maxTokens | 2000–3000 token | Limits the output length of a single review result, adapting to the field verification requirements of declaration materials |
toolChoice | auto | Automatically selects tool calls for interface document verification and data item matching |
AIPROXY_API_ENDPOINT | Fill in the standard interface address according to the deployment environment | Adapts to the private deployment network environment of government affairs scenarios, ensuring no data leakage |
AIPROXY_API_TOKEN | Use the security key calibrated through actual testing | Ensures identity verification security for interface calls, complying with government data access specifications |
Three Common Configuration Errors
- An
Unexpected end of JSON inputerror occurs when calling the model. The cause is that the context overflow of the locally deployed model causes the response to be truncated, and the required JSON structure is not fully returned. - Off-topic responses occur when reviewing long-text declaration materials. The cause is insufficient
maxContextconfiguration, which cannot carry complete context information, causing the model to lose key review basis. - Field content not defined in desensitization rules appears in review results. The cause is that the desensitization configuration for the corresponding data item is not bound, causing the model to output information beyond the allowed scope.
How to Verify Successful Configuration
- Run a single full parsing test of public data catalog documents, verify that the output fields match the source document, and adjust configuration items until they match.
- Submit a simulated data synchronization task for monthly updates, check for prompts of data inconsistency or leakage, and confirm that desensitization and format conversion rules take effect.
- Call the test interface to verify that the
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTconfigurations can complete identity verification and data requests normally. - Submit multiple batches of long-text declaration materials, verify that the review results output by the model do not have off-topic or redundant content, and adjust relevant configuration items until they meet requirements.
The values provided are common starting points and should be evaluated against local deployment 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.