What data in this category includes
This category of data primarily originates 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 maintained documents include public data resource catalogs, data opening lists, data item descriptions, desensitization rules, interface documents, and update frequency descriptions. Data fields include data item name, affiliated department, opening scope, desensitization method, interface address, update cycle, and others. There are no unified fixed units; some fields must match the legal units of the corresponding data items.
Constraints on model access and configuration
The monthly update frequency requires model access configurations to support regular batch synchronization of catalog data, avoiding reliance on one-time static data. Differences in data formats across multiple sources require the configuration process to adapt to parsing logic for different data sources, ensuring cross-platform data can be processed uniformly. The presence of desensitization rules and interface documents requires model configurations to associate corresponding rules and call parameters, ensuring generated ledgers comply with security requirements and interface call specifications. The need to process structured data and interfaces requires model tool call configurations to accurately match the authorization boundaries of data opening.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Public data catalog and opening list documents are typically lengthy, requiring sufficient context to fully process multiple document contents |
maxTokens | 4000 token | Ledger summary and report generation need to output complete structured data and descriptions, requiring sufficient reserved output length |
temperature | 0.2–0.4 | Strict ledger data generation is required to avoid fictional content or deviation from actual catalog information |
toolChoice | auto | Automatic calling of data interfaces to obtain the latest public data catalog and opening list is required, without manual tool specification |
AIPROXY_API_ENDPOINT | Fill in the official access address of the corresponding platform | Follow FastGPT's proxy configuration specifications to ensure normal forwarding of model requests |
AIPROXY_API_TOKEN | Fill in the assigned exclusive secret key | Used for security verification of model requests, ensuring access permissions are legitimate |
Three common configuration errors
- Incorrectly filling an invalid secret key when configuring
AIPROXY_API_TOKEN. The interface test will prompt a connection error. Forced operation may temporarily generate a report due to cached old data. This is because secret key verification is only triggered during the first request or when the cache expires. - Not setting a reasonable range for
maxContext. When processing long catalog documents, context overflow will occur, resulting in missing data items in the latter part of the generated ledger. This is because insufficient context length causes content to be forcibly truncated. - Setting
temperatureabove 0.6. The generated ledger will include opening permission descriptions that do not match the actual catalog. This is because an overly high temperature parameter causes the model to generate content that deviates from real data.
How to confirm the configuration is set correctly
- Access the FastGPT model configuration page, click the test connection button, and verify that a normal model response is returned with no connection error prompt.
- Upload a sample public data catalog document, trigger the ledger summary task, and check that the generated report includes required fields such as data item name, affiliated department, and opening scope.
- View the task execution log to confirm that the model automatically called the configured data interface, with no context overflow or tool call failure errors.
- Compare the generated report with the original catalog document to confirm that desensitization rules were correctly applied, with no sensitive data leakage.
The values provided are common starting points and should be measured 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.