Data overview for this category
State-owned asset management companies’ internal office and employee service data comes from several sources: state-owned asset supervision reports, asset ledgers, internal management systems, employee attendance and salary records, and cross-department collaboration minutes. Data update rhythms differ across sources. Asset ledgers and supervision reports update monthly. Internal systems get revised and released as needed. Employee records adjust in real time when staff are hired or leave. Document structure splits into two types: structured and unstructured. Structured data has fields such as asset number, book value, and using department. Common units are ten thousand yuan and sets. Unstructured data takes long-text format, including system documents and meeting minutes.
Constraints for model access and configuration
Structured asset data has many fields and strict uniform unit requirements. Models must accurately identify field formats and numerical meanings to avoid parsing errors. Data sources with varied update rhythms need configurations that support batch and real-time synchronous interface calls. This prevents data lag or duplicate uploads. Long unstructured system documents use more context window space. Adjust model context bearing parameters accordingly. Internal office data covers state-owned asset supervision and employee privacy. Deploy model services in a privatized environment to avoid data leaks.
Configuration settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
| maxContext | 8000–12000 token | State-owned asset management system documents and asset ledgers are usually lengthy, requiring context coverage for multiple related files |
| temperature | 0.1–0.3 | Internal office and asset data requires strict accuracy, and content deviating from facts should not be generated |
| quoteMaxToken | 1500–2000 characters | Asset ledger entries and supervision document fragments must be fully quoted, to avoid truncation of critical data |
| AIPROXYAPIENDPOINT | Fill in according to the privatized deployment environment | Must connect to internal model services, ensuring state-owned asset and employee data does not leave the domain |
| toolChoice | auto | Must automatically call internal tools such as asset query and employee information query based on queries |
| vision | Disabled | Internal office documents are mostly in text format, no visual processing is needed |
Three common configuration mistakes
- Phenomenon: Knowledge base query returns timeout errors. Cause: The maxContext parameter is not adjusted based on the average length of asset documents. This creates an overly large single context window, exceeding the model’s processing limit.
- Phenomenon: Model testing passes on a third-party proxy platform, but testing fails after creating a new application in FastGPT. Cause: Test environment and production environment AIPROXYAPITOKEN values are not properly distinguished. Mixed use of the two key types causes authentication failure.
- Phenomenon: Returned asset data includes fictional numerical values. Cause: The temperature parameter is set too high. The model generates content that does not match actual ledger records.
How to verify a complete configuration
- Open the application configuration panel in FastGPT v4.17.0. Confirm that AIPROXYAPIENDPOINT and AIPROXYAPITOKEN are filled correctly, with no format errors.
- Upload a sample asset ledger document. Trigger the knowledge base indexing process. Check that the indexing progress bar moves normally, with no error prompts.
- Run an asset number query test. Confirm that returned results include accurate fields and corresponding numerical values, and that quoted fragments are complete.
- Adjust the temperature parameter to 0.2 and run a test. Observe changes in the rigor of returned content. Confirm that the results meet data accuracy 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.