What data for this category looks like
The data for this category primarily comes from the state-owned asset supervision and administration system, enterprise ERP and project management systems, public industry market data, audit and financial statements. Data is updated on a quarterly basis. Commonly processed documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, and post-investment evaluation reports. Most documents include basic project information, approval process node records, financial forecasting modules, post-investment operation tracking indicators, and other content. Fields include project number, construction cycle, core business parameters, with units adapted to the native format of each document.
What constraints do these data characteristics impose on model access and configuration?
The data characteristics of this category impose multi-dimensional constraints on model access and configuration. The quarterly data update rhythm requires the knowledge base sync cycle to match the update frequency, to avoid data lag affecting the accuracy of business process guidance. Diverse and structurally complex long-text documents require configuring context parameters adapted to long-text processing. Multi-source heterogeneous data sources require clear field mapping rules when calling models, to ensure accurate identification of cross-system data. Business attributes related to state-owned asset management require configuring compliant model call permissions and log recording parameters, to ensure data security.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Most documents in this category are long text, need to accommodate complete context from multiple feasibility study and post-evaluation reports, ensuring the model can associate all business information |
maxTokens | 2000–3000 token | Need to generate complete process guidance or abstracts for investment review meeting materials, to avoid truncated output content |
temperature | 0.2–0.4 | Business process guidance requires strict accuracy, reduce output randomness, ensure guidance steps comply with regulatory requirements |
toolChoice | auto | Automatically select whether to call the knowledge base or tools based on user input, adapt to multiple types of business consultation scenarios |
AIPROXY_API_ENDPOINT | Enterprise-specific internal proxy address | Adapt to the enterprise's internal network environment, ensure data transmission security, comply with state-owned asset data compliance requirements |
AIPROXY_API_TOKEN | Regularly rotated encrypted token | Prevent unauthorized access caused by token leakage, ensure the security of model calls |
Three common configuration errors
- Symptom: Calls to models configured on a third-party API platform from the FastGPT page return no response or connection errors. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the proxy address does not have internal network access permissions enabled. - Symptom: A locally deployed third-party large language model does not activate in model thinking settings. Cause: The model access address is not set to the local service address, or the FastGPT third-party model access option is not enabled.
- Symptom: The output result of a prior AI model in a workflow cannot be updated to an empty value. Cause: The trigger condition for variable updates is not configured correctly, or the variable type is set to unmodifiable read-only mode.
How to confirm successful configuration
- Test model calls by inputting a fragment of a project proposal, check if the model correctly returns business process guidance content, to confirm the context parameter configuration meets expectations.
- View model call logs, verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no connection errors or permission exceptions. - Trigger a workflow variable update operation, check if the output of the previous model can be correctly set to an empty value, to confirm the variable configuration meets business requirements.
- Adjust the
temperatureparameter and test the output, verify that the strictness of the output content complies with business process requirements.
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.