What data for this category looks like
State-owned capital investment company internal office data primarily comes from state-owned assets supervision and reporting systems, internal OA platforms, financial accounting systems, human resource management systems, and project management platforms. Data update frequencies fall into three categories: real-time (such as employee attendance, project progress), daily (such as financial daily reports), and on-demand (such as temporary request reports). Document structures include structured reports (such as monthly state-owned assets supervision reports, employee salary details) and unstructured documents (such as meeting minutes, project feasibility study reports). Fields include unified state-owned assets codes, employee IDs, project numbers. Monetary units are mostly ten thousand yuan, and time fields use the YYYY-MM-DD format.
What constraints these characteristics impose on model integration and configuration
State-owned assets data sensitivity requires private deployment for model interfaces. Configure exclusive AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN to secure access. For scenarios with high structured report volume, require models to support automatic tool calling for SQL queries, and set the toolChoice parameter appropriately. For long documents and multi-turn conversations, adjust maxContext and maxTokens values to carry complete context. Match knowledge base refresh cycles to different data update frequencies, to avoid stale data harming question-and-answer accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Internal office documents are mostly long documents, including project reports and state-owned assets supervision statements, requiring sufficient context to carry historical conversations and document content |
maxTokens | 4000–6000 token | Employee service-related question-and-answer mostly involves structured queries and document summarization, to avoid exceeding the model's output limit |
toolChoice | auto | Required to support SQL queries of internal financial and human resource system data, automatically selecting tool calling |
temperature | 0.1–0.3 | Internal office question-and-answer requires accurate information output, to avoid generating irrelevant content |
AIPROXY_API_ENDPOINT | Exclusive interface address of privately deployed models | State-owned capital investment company data is sensitive, so locally privately deployed model interfaces must be used |
AIPROXY_API_TOKEN | Exclusive access token for privately deployed models | Ensures interface access security and prevents unauthorized access |
Three common configuration errors
- Issue: A privately deployed Qwen3-32B model returns an error when calling the MCP MySQL query service. Logs indicate interface connectivity exceptions. Cause:
AIPROXY_API_ENDPOINTdoes not point to the actual private model deployment address, so requests cannot reach the target model. - Issue: Knowledge base question-and-answer prompts that a text understanding model must be configured, but a knowledge base associated model has already been configured. Cause: The model bound to the knowledge base was not also configured as a text understanding model, or the model type did not match the structured and non-mixed format of internal office documents.
- Issue: The model inside the MarkPDF container fails to load normally, and document parsing fails. Cause: The model image was not pulled to the local container environment in advance, or the image pull process was interrupted, resulting in incomplete model files.
How to confirm the configuration is complete
- Run a connectivity test for the local private model: send a standard request to
AIPROXY_API_ENDPOINTand confirm that a normal model response is returned. - Upload a test document containing structured reports and unstructured meeting minutes, trigger knowledge base question-and-answer, and confirm that parsing works normally without additional text understanding model configuration.
- After setting
toolChoicetoauto, initiate a test question that includes an SQL query requirement, and confirm that the model automatically calls the MCP tool to complete data query. - Check the configuration panel in FastGPT v4.17.0, confirm that the values of
maxContextandmaxTokensmatch the average length of current internal office documents and conversation context requirements.
The values provided here 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.