What This Type of Data Looks Like
Data for state-owned asset management companies’ documents comes mainly from regulatory documents released by state-owned asset supervision departments, internal asset disposal plans, property registration materials, annual operation reports, and risk control and compliance documents. Regulatory files are updated monthly or quarterly. Internal documents are generated in real time as projects progress. Most documents have a fixed header including state-owned asset exclusive numbers and asset target information, a chaptered main body, and a compliant signature block. Common fields include original asset value, appraised value, shareholding ratio, and transfer approval document numbers. Common units are ten thousand yuan, square meters, and percentage.
What Constraints These Characteristics Impose on Model Integration and Configuration
The fixed format and exclusive field requirements for state-owned asset documents mean the model must generate content that strictly matches official templates. It must accurately extract and fill structured information. Long documents such as annual operation reports can reach tens of thousands of characters. This places high demands on the model’s context window capacity. Internal documents are generated in real time as projects progress. A real-time synchronization trigger mechanism must be configured for data sources to ensure knowledge base timeliness. Compliance documents must strictly follow state-owned asset supervision terminology. This adds extra constraints on compliance checks for model outputs. Cross-border data transmission risks must also be avoided.
How to Configure the Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long document context requirements of state-owned asset documents, which often have thousands of characters, and avoids truncating core asset data |
maxTokens | 2000–3000 token | Meets the output length requirements for single document drafting, covering complete plan or report content |
temperature | 0.1–0.3 | Reduces generation randomness, ensuring the format and terminology of state-owned asset documents comply with official regulatory requirements |
toolChoice | auto | Allows the model to automatically decide whether to call retrieval tools to supplement the latest state-owned asset supervision policy data |
AIPROXY_API_ENDPOINT | Domestic compliant large model service address | Adapts to the domestic storage and compliance requirements of state-owned asset data, avoiding cross-border data transmission risks |
AIPROXY_API_TOKEN | Configure with the exclusive key provided by the service provider | Ensures the identity legitimacy of interface calls, complying with the security control requirements of state-owned asset systems |
Three Common Mistakes
- Phenomenon: Deviations or format errors appear in the original asset value field in generated asset disposal plans. Cause: No dedicated prompt word configured for the structured fields of state-owned asset documents, or the
temperaturevalue is set too high, increasing generation randomness. - Phenomenon: Model response timeouts occur when processing long documents. Cause: The
maxTokensparameter is not adjusted to fit long document generation requirements, or the interface call timeout threshold does not match the large model response duration. - Phenomenon: Preset model configurations fail after upgrading FastGPT, and the model called during chat does not match the preset. Cause: The configuration information for
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTwas not retained during the upgrade, and the system loads default model parameters.
How to Confirm the Configuration Is Complete
- Upload a standard state-owned asset property registration document, and check that the generated document includes correct structured fields such as asset number and appraised value.
- View the model interface call logs, and confirm that the configurations for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave been correctly read, with no authentication errors. - Generate a draft long document, and check that the output length matches the
maxTokenssetting, with no forced truncation. - Adjust the
temperatureparameter, compare the two generated document contents, and confirm that the generation randomness meets expectations.
The provided values are common starting points and should be evaluated against applicable local 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.