What this category of data looks like
Core data for this category comes from government portals, document management systems, government information disclosure directories, policy document repositories, and press conference materials. The update frequency is daily. Commonly processed documents include policy documents, policy interpretation drafts, public disclosure directories, information release approval forms, press releases, and website column update records. Data fields include document number, publishing entity, public disclosure attribute, confidentiality level, publishing time limit, column affiliation, and other relevant fields. Document formats vary across different sources, and some documents include approval process nodes and signature traces.
What constraints these characteristics impose on model access and configuration
The daily update requirement means scheduled incremental sync tasks must be configured to ensure the latest policy documents and approval forms are connected to the knowledge base in a timely manner. The multi-type, multi-source document structure means parsing rules compatible with different formats must be configured to avoid field missing or format errors that disrupt model calls. The strict consequences of errors mean model calls must align with government public disclosure compliance requirements. The randomness of model outputs must be limited, and a sensitive information verification link must be configured. The proactive public disclosure attribute means the model must first judge the public disclosure compliance of documents, and the processing workflow must follow the time limit requirements for proactive publication.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the context requirements of long texts such as policy documents and approval forms |
maxTokens | 2000–3000 token | Adapts to the conventional output length of policy interpretations and published copy |
temperature | 0.2–0.4 | Limits the randomness of model outputs to ensure stable and consistent public disclosure standards |
AIPROXY_API_ENDPOINT | Local private deployment service address | Meets the secure transmission requirements for government data |
AIPROXY_API_TOKEN | Token value matching the deployment environment | Verifies the legitimacy of model calls and prevents unauthorized access |
toolChoice | auto | Automatically matches verification rules for document types to adapt to review requirements across multiple scenarios |
Three common configuration errors
- An unmarshalresp error occurs during model calls. This is caused by incorrect configuration of `AIPROXYAPIENDPOINT
andAIPROXYAPI_TOKEN`, leading to failure to parse the model return format. - Local Ollama models cannot be accessed after private deployment. This is caused by failing to point
AIPROXY_API_ENDPOINTto the local Ollama service address, and failing to set a matchingAIPROXY_API_TOKEN. - Model calls fail after embedding a government portal via iframe. This is caused by missing cross-origin whitelist configuration, leading to front-end requests being blocked by the browser.
How to confirm the configuration is complete
- Verify that the configuration values for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the deployment environment, and initiate a single model call to confirm the return format is normal. - Upload a policy document containing sensitive information to verify that the model's sensitive information interception results meet compliance requirements.
- Configure a daily incremental sync task, and check whether incremental data is automatically synchronized to the knowledge base the next day.
- Embed a test page via iframe to verify that there are no cross-domain errors during front-end model calls.
The values provided are common starting points and should be validated against the target deployment’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.