What data looks like for this category
Data sources for this category include the national petition information system, local petition systems, the 12345 citizen hotline platform, grassroots governance platforms, and historical petition archives. The update frequency is daily. Commonly processed documents include petition registration forms, acceptance notices, transfer and assignment letters, handling opinions, review and re-examination applications, and resolution plans. Most documents use structured fields, including the requesting party, matter content, handling stage, review and re-examination status, and more. Some historical archives are scanned documents or unstructured text, with significant variation in the length of individual documents.
What constraints these characteristics impose on the model access and configuration step
Data sources for this category are scattered and updated daily. This requires model access to support compatible configuration for multiple system export formats, while adapting to incremental sync update rhythms. Commonly processed document types are diverse, including structured forms and unstructured text. Some historical archives are scanned documents, so parsing rules adapted to different formats must be configured. The length of individual documents varies widely. Limit context length to avoid model overflow. Petition case handling must strictly follow statutory procedures. Model output must match preset handling stage fields to ensure results comply with business specifications. Additionally, some documents contain sensitive request content. Configure data desensitization related parameters.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average length of petition-related documents, avoids context overflow |
maxTokens | 2000–3000 token | Controls single-round model output length, adapts to generation requirements for handling opinion texts |
quoteMaxToken | 1000–1500 characters | Limits maximum length of referenced documents, ensures accurate extraction of core requests and handling basis |
toolChoice | auto | Automatically selects tool calls, adapts to needs of multi-document verification and cross-stage information checking |
temperature | 0.3–0.5 | Reduces output randomness, ensures standardization and consistency of handling opinions |
AIPROXY_API_ENDPOINT | Fill in according to deployed model service address | Required configuration to connect to local or third-party large model services |
Three common errors
- Symptom: Model calls return a connection timeout error, and the interface displays a request timeout status code. Cause:
AIPROXY_API_ENDPOINTis not correctly configured to point to the deployed model service address, or no valid access key is configured, causing identity verification failure. - Symptom: Tool call processes interrupt output early, returning incomplete handling opinions or policy explanation content. Cause:
maxTokensis set too small, leaving insufficient output length to complete full text generation. - Symptom: Valid text content cannot be extracted after uploading scanned petition archives. Cause: The
visionparameter is not configured to support image text recognition, or the visual model adaptation function is not enabled.
How to confirm the configuration is complete
- After filling in
AIPROXY_API_ENDPOINTand the corresponding access key, initiate a test call to confirm that a normal model response is returned, with no identity verification or connection errors. - Import a single petition registration form document, verify that fields extracted by the model match the document's actual content, and adjust
maxContextandmaxTokensvalues to a range that meets business requirements. - Upload a scanned petition archive, test whether the model can correctly recognize text content in the image, and confirm that the
visionparameter configuration takes effect. - Simulate a multi-round tool call scenario, verify that output content is complete with no early interruptions, and confirm that the
maxTokensvalue is reasonable.
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.