Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from the social insurance management system, personal benefit records, labor relations and work injury identification system, and electronic social security card platform. The update frequency is daily. Daily processed documents include benefit application forms, payment period certificates, retirement approval forms, work injury identification materials, and unemployment insurance benefit application materials. Data fields cover insured identity, payment period, work injury identification grade, unemployment insurance coverage duration, benefit calculation standards, and more. Units include month, year, yuan, grade, and others. Each single document has many data fields, and cross-document correlation is high.
What constraints these characteristics impose on the model access and configuration link
Multiple data sources with daily updates require model access to support real-time or near-real-time data synchronization, to avoid using expired benefit calculation rules. Diverse document types and high field correlation require configuring model logic that can identify cross-document associated fields, to avoid siloed handling of the relationship between payment period and benefit standards. Fields involve social security specialized terminology and multiple units, so enable terminology adaptation or custom vocabulary in the configuration to prevent incorrect parsing of specialized fields by the model. Additionally, data directly relates to benefit payments, so strict control of model output accuracy is required. Enable a result verification link during configuration.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category has many documents and associated fields, so sufficient contextual association information must be retained to ensure the logical integrity of process guidance |
maxTokens | 2000–3000 token | Benefit calculation results must clearly display the calculation process and final amount, so the output length must cover complete step descriptions and supporting basis |
temperature | 0.1–0.3 | This scenario requires strict accuracy of calculation results. Avoid using overly high randomness to prevent non-standard benefit calculation results |
toolChoice | auto | Allow the model to independently choose whether to call tools for professional calculations such as payment period and work injury grade, to reduce the limitations of manual configuration |
quoteMaxToken | 4000 token | Retain sufficient source document fragments for reference in process guidance, ensure users can trace the original materials for benefit calculation |
AIPROXY_API_ENDPOINT | Internal social security data proxy address | Use a dedicated endpoint when connecting to in-house social security systems to ensure compliance and security of data transmission |
Three common misconfigurations
- Symptom: When configuring
AIPROXY_API_TOKEN, an error "Data too long for column 'models'" appears after adding the token. Cause: The token parameter value is too long, exceeding the storage limit of the database field. - Symptom: After enabling model access, processing uploaded documents such as work injury identification materials is too slow. Cause: Unreasonably configured
maxContextleads to redundant context loading, or the document chunking optimization logic is not enabled. - Symptom: After upgrading the FastGPT version, saved custom model configurations become invalid, and chats automatically switch to the default model. Cause: The model configuration storage path changes after the version upgrade, and the original configuration is not migrated synchronously.
How to confirm the configuration is complete
- Launch a test for the old-age benefit calculation process guidance, check whether the calculation logic output by the model associates document information such as payment period and retirement approval.
- Check the model call logs to confirm that the actual values of parameters such as
maxContextandmaxTokensmatch the configured content. - Simulate abnormal data input, check whether the model triggers the preset verification logic and no incorrect benefit calculation results appear.
- Connect to the in-house social security system interface, test whether data synchronization is performed normally at the daily update frequency.
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.