What data looks like for this category
This scenario addresses service inquiry and response needs for internal control departments, audit departments, finance departments, and external audit institutions of state-owned enterprises. Data primarily comes from enterprise internal control systems, ERP systems, financial sharing systems, audit management systems, and procurement contract project systems. Daily processed documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification closing certificates. Data is updated annually. Core documents contain fields such as issue entries, rectification requirements, and supporting materials. Some documents are unstructured, requiring key information extraction from text.
What constraints these characteristics impose on model access and configuration
Business data updated annually in batches requires model access to support batch processing context quotas, avoiding core information loss from long document truncation. Unstructured audit working papers and rectification ledgers demand models to accurately extract structured fields, raising requirements for parameter rigor. Data sources are scattered across multiple internal systems, so access configurations must support multi-channel data calls. Rectification closing certificates need association and verification with corresponding issue lists, requiring reasonable context recall logic to be configured. This category centers on issue discovery and rectification processes, focusing on post-event inspection and evidence sampling. Errors have serious consequences, so the accuracy of model output must be strictly controlled to prevent audit issues from being missed or misjudged due to parameter deviations.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context requirements for long documents such as audit working papers and rectification ledgers, avoiding truncation of core business information |
maxTokens | 2000–3000 token | Meets structured output length requirements for rectification requirements and issue lists, covering complete rectification process descriptions |
quoteMaxToken | 1500 token | Ensures recalled audit evidence and rectification supporting materials sufficiently support issue analysis, without exceeding context quotas |
temperature | 0.1–0.3 | Reduces output randomness, ensuring accuracy in extracting audit issues and rectification requirements, meeting the rigor requirements of internal control compliance |
toolChoice | auto | Automatically selects tool call timing, adapting to extraction and association verification needs for multi-system data |
AIPROXY_API_ENDPOINT | Fill in the corresponding address based on the deployment environment | Adapts to access requirements for scattered enterprise internal systems, ensuring compliance of internal data transmission links |
Three common configuration mistakes
- Phenomenon: An error indicating no available embedding capability is prompted when calling the vector model, or the knowledge base call returns empty results. Cause: The corresponding vector model was not added to the compliant API channel configuration, and the adaptability between the model and current business documents was not verified.
- Phenomenon: An error of
'KokoroModel' object has no attribute 'mode'is prompted when testing the TTS model. Cause: The accessed model type does not match the configured call protocol, and the built-in attribute fields of the model were not correctly identified. - Phenomenon: Empty values are returned when extracting structured information such as ID cards using the specified model. Cause: The
temperatureparameter was not adjusted for structured field extraction, and excessive output randomness failed to match fixed-format business information.
How to confirm configuration is complete
- Upload a single audit working paper, check whether the issue extraction results from the model cover the core entries of the document, and adjust parameters until the output meets business expectations.
- Configure access addresses for multiple system data sources, verify whether the cross-system data call link is normal, with no timeout or permission errors.
- Trigger the rectification ledger association verification task, check whether the recalled supporting materials are correctly bound to the issue list, and confirm that the context association logic is effective.
- View the model call log, confirm that the actual call values of parameters such as
maxContextandmaxTokensmatch the configured values, with no abnormal truncation.
The values provided here 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.