What the data for this category looks like
This category’s data originates from government centralized payment systems, unified budget management systems, electronic invoice platforms, and contract management systems. Updates occur daily. Daily processed documents include payment applications, fund use plans, invoices, contracts, acceptance documents, reimbursement approval forms, and payment vouchers. Each document contains fields such as budget indicators, economic classification, bill amount, contract terms, and acceptance nodes. Single records link the full fund payment chain information, and no cross-category summary statistics fields are included.
What constraints these characteristics impose on the "model access and configuration" link
Most data comes from structured fields and semi-structured documents of business systems. Compliance of single fund payments must be verified. Model access must prioritize accurate context matching to avoid compliance risks from generalized reasoning. Full daily updated data requires control of batch processing resource usage to prevent timeouts. Multiple documents linked to single records must be associated by business chain during retrieval, not only by keyword matching. Errors may involve fund security. Configuration must strictly limit the model’s free generation scope to ensure responses only use existing business data.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multiple documents linked to a single business transaction, avoiding truncation of critical budget indicators and contract terms |
maxTokens | 2000–3000 token | Controls the output length of single-round responses, ensuring compliance explanations and business basis are clear and complete |
quoteMaxToken | 1500 token | Limits the total token proportion of referenced documents, prioritizing retention of core bill and payment voucher content |
toolChoice | none | Prevents the model from calling external tools, ensuring responses are only based on configured knowledge base content and preventing unauthorized operations |
temperature | 0.1–0.3 | Reduces generalization ability, ensuring responses strictly match business data and avoiding generation of unsubstantiated compliance judgments |
AIPROXY_API_TOKEN | Fill in the key allocated by the platform | Completes identity verification for model calls and ensures interface security |
Three common configuration mistakes
- The symptom is the model directly returning a rejection message stating the knowledge base has no matching content, without attempting to reason based on existing knowledge. The cause is incorrect configuration of the
temperatureparameter, or mistakenly settingtoolChoiceto forced retrieval only, resulting in generalized reasoning being disabled. - The symptom is the model calling the interface and returning an authentication failure error. The cause is incorrect filling of the
AIPROXY_API_TOKENor use of expired key information. - The symptom is retrieved documents that do not match the business scenario, such as confusing budget indicator and reimbursement amount fields. The cause is failure to associate multi-system data according to the business chain, only matching by keywords leading to incorrect recall.
How to confirm the configuration is complete
- Submit test payment application and reimbursement approval form data, verify whether the model's returned content includes corresponding business fields, and confirm the matching logic meets expectations.
- Trigger a model call, check the interface log, confirm that the connectivity of
AIPROXY_API_TOKENis normal and there are no authentication errors. - Adjust the value range of the
temperatureparameter, compare the content differences between two responses, and confirm the generalization level meets compliance requirements. - Simulate a scenario where the knowledge base has no matching content, check whether the model's returned prompt conforms to the preset compliance wording, and there is no additional generated content.
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.