What the data for this category looks like
Core data for this category comes from the state-owned assets investment supervision system, enterprise ERP and project management systems, industry market data, audit and financial statements. The update frequency is quarterly. Documents processed daily include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, and post-investment evaluation reports. Significant structural differences exist across document types: project proposals contain project background and preliminary investment estimates; feasibility study reports cover multi-dimensional analysis of technology, market, and finance; post-investment evaluation reports include project implementation reviews and benefit evaluations. Data fields cover proprietary business fields such as project number, investment subject, approval time, post-evaluation score, with no unified standard format.
What constraints do these characteristics impose on the "model access and configuration" link
Multi-source heterogeneous data sources require the model access link to support unified parsing and adaptation of multi-format data, preventing parsing failures caused by differences in document structures. The quarterly update frequency requires the vector database synchronization cycle to align with the data update rhythm, ensuring the timeliness of retrieved content. The high proportion of long documents requires sufficient token space to be reserved for context configuration, preventing key information from being truncated. This category differs from government investment approval scenarios, with greater focus on board investment decisions, negative lists, and post-investment evaluation. As such, tool calls must adapt to exclusive business processes such as investment decision-making material generation and post-evaluation data integration. The high compliance requirements of the business scenario mean model output must strictly meet the rigor standards of state-owned assets supervision, avoiding risks of state-owned asset losses or accountability investigations.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context processing needs for long documents such as feasibility study reports and post-investment evaluation reports |
maxTokens | 4000 token | Balances single-round response length and system response speed, meeting the content volume requirements for investment decision-making material generation |
quoteMaxToken | 1500 token | Limits the token proportion of a single cited document, preventing overall context overflow |
toolChoice | auto | Allows the model to independently select tools to complete tasks such as approval assistance and post-evaluation data integration |
temperature | 0.3–0.5 | Reduces response randomness, ensuring the rigor of service consultation responses and complying with state-owned assets supervision compliance requirements |
AIPROXY_API_ENDPOINT | Fill in according to the enterprise intranet deployment address | Adapts to the internal network environment of state-owned enterprises and public institutions, ensuring transmission security of sensitive business data |
AIPROXY_API_TOKEN | Configure according to the enterprise unified identity authentication token | Complies with internal permission control and data security specifications |
Three common mistakes
- Symptom: Knowledge base search returns non-standard JSON format errors. Cause: No adaptation has been made for the structured fields of investment project documents, leading to format exceptions during model parsing.
- Symptom: Tool calls cannot automatically select the corresponding tool to execute tasks. Cause:
toolChoiceis configured in fixed mode, or insufficient business scenario descriptions are provided for the model to match tools. - Symptom: Semantic matching results cannot accurately associate target investment projects. Cause: No semantic matching logic adapted to the proprietary fields of state-owned assets projects has been selected, or the matching threshold has not been adjusted to adapt to business fields such as project number and investment subject.
How to confirm that the configuration is complete
Reference only; verify against the target FastGPT release and deployment documentation.
- Upload a typical feasibility study report, testing whether the model can correctly extract core business fields such as project investment estimates and technical solutions.
- Trigger the tool call flow, verifying whether the model can independently select the corresponding tool to complete investment decision-making material generation or post-evaluation data integration tasks.
- View the API call log, confirming that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no network connection errors. - Adjust the
temperatureparameter, testing response rigor across different values to ensure output complies with state-owned assets supervision compliance requirements.
The values provided are common starting points and should be measured against their 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.