What this category of data looks like
Data comes from centralized treasury payment systems, unified budget management systems, electronic invoice platforms, and contract management systems, with daily updates. Daily documents include payment applications, fund appropriation plans, invoices, contracts, acceptance receipts, reimbursement approval forms, and payment vouchers. Structured fields include budget indicator codes, economic classification codes, payment amounts, approval node status, and more. Some documents include electronic attachments. Data volume receives incremental updates based on daily business volume.
What constraints do these characteristics impose during model access and configuration
Multi-system data sources require multi-source data docking configuration, which must adapt to different systems' API call formats and permission rules. Daily updates require configuring incremental synchronization logic to avoid resource waste from full indexing. Multiple document types require adaptation for structured field extraction and unstructured attachment parsing, to ensure core business information is fully loaded. The business attributes of individual payments require the model to focus on compliance verification for single business items, avoiding call failures caused by excessive context. Additionally, strict fiscal audit compliance demands consistent and accurate model output.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The core context for individual payment verification includes 1-2 application forms, vouchers, and contracts. Keeping the total character count within this range avoids context overflow and aligns with the window limits of FastGPT v4.17.0 |
maxTokens | 4000–6000 | Fiscal audit compliance verification logic requires sufficient tokens to carry rules and business data, while avoiding exceeding the model's native context window limits |
quoteMaxToken | 2000–3000 | Priority must be given to quoting text from core vouchers such as invoices and contracts. Controlling the token proportion of quoted content ensures core information is loaded first |
toolChoice | auto | Automatic triggering of compliance verification tools and field extraction tools is required to adapt to multi-stage audit requirements |
temperature | 0.1–0.3 | Fiscal audits require strict compliance. A lower temperature value ensures consistency and accuracy of output results |
AIPROXY_API_ENDPOINT | Corresponding internal proxy address of fiscal systems | Internal data sources and compliant API call links must be connected to ensure data transmission security |
AIPROXY_API_TOKEN | Exclusive token generated according to internal permissions | Ensures access permissions for data source interfaces and prevents unauthorized calls |
Three common misconfigurations
- Phenomenon: The Rerank model is enabled and the knowledge base index configuration is completed, but rearranged results are not displayed in online recall tests. Cause: The requirement to use rearranged result sorting is not clearly stated in the business guidance prompt, or the Rerank model's call key and endpoint are not configured correctly.
- Phenomenon: When
quoteMaxTokenis set above 3000, the large language model does not receive context content. Cause: The total token limit of FastGPT is not matched, or the value ofmaxContextis not adjusted synchronously to adapt to the total token upper limit. - Phenomenon: When deploying an Ollama large language model locally, frequent Request Timeout errors appear in conversations. Cause: Reasonable model call timeout logic is not configured, or the local port is not open, preventing requests from establishing a normal connection.
How to confirm the configuration is complete
- Log in to the FastGPT backend, enter the model configuration page for the corresponding scenario, and verify that the values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the internal system authorization requirements. - Upload a single payment application and supporting vouchers, initiate a recall test, and confirm that the returned context content includes core business fields, and the token proportion of quoted content falls within the configured
quoteMaxTokenrange. - Initiate two identical audit requests, compare the consistency of the output results, and confirm that the
temperatureparameter value meets the requirements for compliance audits. - View the knowledge base index operation logs to confirm that the Rerank model call process is normal and there are no error records.
The values provided here are common starting points and should be evaluated against your 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.