Internal Office and Employee Services for Internal Control Evaluation, Audit and Rectification: Model Access and Configuration

What This Type of Data Looks Like

Data sources include enterprise internal control systems, ERP, financial sharing systems, audit management systems, and procurement contract project systems. Daily generated documents include internal control self-assessment reports, audit notices, audit working papers, issue lists, rectification ledgers, and rectification cancellation certificates. Data updates occur annually. Each single document includes fields such as issue identifier, rectification measures, responsible entity, and supporting material association. Relationships exist between different documents.

Constraints Imposed by Data Characteristics on Model Access and Configuration

Data sources cover multiple internal enterprise systems. Access and configuration must adapt to interface validation across these sources to prevent mismatched data formats between systems. Data updates happen annually, so model context caching does not need frequent refreshes. Configure data loading in annual batches. Daily documents include both structured and unstructured content. Configuration must adapt to field parsing rules for different document types. For example, extract associations between issue lists and rectification ledgers. The most time-consuming business steps involve long text extraction and rectification requirement generation. Adjust model configuration parameters to ensure stable processing of long documents. Avoid losing critical information due to insufficient parameter settings.

How to Set the Configuration

Reference only; verify against the target FastGPT release and deployment documentation.

Three Common Misconfigurations

• When calling the built-in tool "Get Current Time", the model stream response is empty, and the interface shows empty model stream output. The cause is incorrect configuration of the toolChoice parameter, or incorrect entry of AIPROXY_API_TOKEN, which prevents the model service from returning stream output normally.
• When configuring a deepseek model deployed via ollama, the model thinking settings do not take effect, and the generated content does not use deepseek's native thinking logic. The cause is incorrect setup of parameter adaptation for model access, or failure to enable the model's thinking mode switch, which prevents the platform from calling the model's thinking capabilities.
• When attempting to update the output of a previous AI model to a null value in a workflow, the variable update operation does not take effect, and the variable value is not cleared. The cause is incorrect configuration of the trigger timing for variable updates, or incorrect variable scope settings, which prevent the variable from updating normally.

How to Confirm Successful Configuration

• Initiate a test call, input a single audit working paper, and review the rectification suggestions generated by the model. Confirm that the values of maxTokens and quoteMaxToken are adapted to the document length.
• Call the built-in tool, check whether the tool return results meet expectations. Confirm that the toolChoice parameter configuration is effective.
• View the document content loaded by the model context. Confirm that the maxContext configuration covers the required associated document information.
• Check the interface return status of the model service. Confirm that the AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN configurations are correct.

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.