This chapter follows the parameter logic of FastGPT v4.17.0.
What the data for this category looks like
Data for this category primarily comes from state-owned asset supervision systems, enterprise ERP and project management systems, industry market data, and audit and financial statements. Daily processed documents include annual investment plans, project proposals, feasibility study reports, due diligence reports, asset appraisal reports, and post-investment evaluation reports.
Data updates follow a quarterly schedule. Core fields cover project identification, investment scale, implementation cycle, evaluation conclusions, and more. Some fields require alignment with standard definitions based on industry classification.
What constraints these characteristics impose on model access and configuration
Data sources are scattered, and update cycles are fixed. This requires the model access link to support batch synchronization and incremental updates of multi-system data, and avoid reloading historical data outside quarterly cycles.
Core documents vary widely in length, from short approval applications to dozens of pages of post-evaluation reports. This places clear requirements on context window adaptability.
Data is tied to state-owned asset supervision requirements. Configuration links must ensure traceability of tool calls, match the approval process logic of business scenarios, and avoid generating content that does not meet regulatory requirements.
Data fields have no unified fixed units. The configuration link must support custom field mapping rules to adapt to caliber differences across different projects.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapt to the full context of documents from project approval applications to post-evaluation reports, avoid truncation of key information |
maxTokens | 4000–6000 token | Balance generated content length and response speed, meet output needs for investment decision materials organization and post-evaluation report writing |
temperature | 0.3–0.5 | Reduce randomness of generated content, ensure rigor of project data and evaluation conclusions, comply with state-owned asset supervision compliance requirements |
toolChoice | auto | Automatically trigger tool calls to obtain latest project management system data and regulatory policy information, adapt to dynamically updated business scenarios |
quoteMaxToken | 2000–3000 token | Retain sufficient original reference fragments, support argumentation logic and compliance traceability of post-evaluation reports |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address according to the deployment environment | Ensure smooth model call link, adapt to access restrictions of the enterprise internal network |
Three Common Mistakes
- Phenomenon: The data source configuration page shows that Feishu-related services have been bound, but only scattered cloud drive files are returned during retrieval, and structured documents such as project proposals are not matched. Cause: The correct knowledge base type was not selected in the Feishu data source configuration, and the general cloud drive directory was bound by mistake.
- Phenomenon: The embedding model call returns a 401 status code, or the deployment log shows that the interface key is invalid. Cause:
AIPROXY_API_TOKENwas not filled in correctly, or the current FastGPT instance was not granted call permissions for the corresponding model. - Phenomenon: No return result in the tool call link, or the generated post-evaluation report does not reference core project data. Cause:
toolChoicewas forcibly set tonone, automatic tool calls were not enabled, ormaxContextwas set too small to cover necessary project context information.
How to Confirm Configuration is Complete
- Test retrieval of a specified annual investment plan document, confirm that the returned results include complete project entries and corresponding fields, with no missing or redundant content.
- Trigger a tool call test, verify whether the latest project management system data can be automatically obtained, and generate intermediate results that conform to business logic.
- Check the deployment log, confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no interface call errors. - Adjust the
temperatureparameter, compare the two generated project proposal contents, and confirm that the output rigor meets the business scenario 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.