What this category of data looks like
This category of data comes from the unified budget management system, centralized treasury payment system, accounting system, and asset management system. The update frequency is once per year. Daily documents include department final account statements, balance sheets, income and expenditure final account statements, final account explanations, and government financial reports. These materials combine structured reports and written explanations. Fields cover revenue and expenditure details, asset and liability items, reporting entity codes and related information. Units follow legal metrology standards. The data volume of a single document increases as the unit hierarchy rises.
What constraints do these characteristics bring to the model access and configuration link
The annual update feature means models do not need to pull data at high-frequency real-time intervals. Offline batch processing can be used. Structured reports and data from multiple systems require models to adapt to unified multi-source data formats during access, to avoid field mapping errors. Core time-consuming links such as account statement verification and difference interpretation rely on the model’s accurate identification of professional financial fields. Sufficient context length must be configured to associate reports and payment data. The risk of errors from returned final account statements requires model outputs to include clear source references, to reduce misjudgment risks.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Requires association of multiple reports and payment data; sufficient context length covers cross-system data association analysis needs |
maxTokens | 2000–3000 token | Adapts to the content length of a single final account explanation or report, avoids output truncation leading to missing key information |
quoteMaxToken | 1500 token | Requires referencing original report fields as references; limits the length of quoted content to ensure output readability |
temperature | 0.2–0.4 | Financial data requires high accuracy; lower temperature reduces output randomness, ensures rigor of professional expressions |
toolChoice | auto | Automatically selects tools to adapt to multi-source data extraction and verification needs, no manual tool type specification required |
AIPROXY_API_ENDPOINT | Fill in the address provided by the deployment service provider | Used to connect external large model interfaces, must be used with AIPROXY_API_TOKEN |
AIPROXY_API_TOKEN | Fill in the secret key provided by the deployment service provider | Used for interface identity verification, ensures data transmission security |
Three common mistakes
- Symptom: Interface returns 401 unauthorized error after configuring
AIPROXY_API_TOKEN. Cause: Incorrect secret key entry, missing access permission for the corresponding large model, or mismatched interface address with the deployment environment. - Symptom: Model returns empty content after uploading a Word-format final account explanation document. Cause: Document parsing adaptation configuration is not enabled, or table formats in the Word document are not correctly identified, leading to failed structured data extraction.
- Symptom: The content corresponding to the knowledge base’s report image link cannot be output during conversation. Cause: The
visionconfiguration item is not enabled, or the image link fails the system compliance check and cannot be read and parsed by the model.
How to confirm successful configuration
- Use an interface testing tool, enter the correct
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, verify interface connectivity, confirm no identity verification errors. - Upload a single Word-format final account report document, trigger model parsing, check if revenue and expenditure detail fields in the table can be extracted, confirm the content is not empty.
- Enable the
visionconfiguration item, upload a public link to a report image, initiate a conversation asking about the image content, confirm the model can correctly identify key information in the image. - Adjust the
temperatureparameter to 0.3, initiate a test request involving association analysis of multiple report data sets, check if the output content remains professional and rigorous, with no overly divergent statements.
The values provided are common starting points and should be evaluated 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.