What data looks like for this category
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints do these characteristics impose on model access and configuration
Integrating multi-source data requires the model context length to adapt to the total spliced length of multiple materials, to avoid truncation of key approval information. Standardized verification of fixed fields requires the model to maintain a stable output format, to avoid verification deviations caused by randomness. Government system data security requirements mandate strict control of API access permissions, to prevent leaks of sensitive information such as project and unit data. Daily updated materials and qualification data require ensuring the timeliness of model calls, to avoid using outdated verification bases.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must integrate full content of multiple approval materials, to avoid truncation of key fields such as project names and participating unit qualifications |
maxTokens | 2000–3000 token | Output length for a single review request must cover all content such as material compliance verification results and approval suggestions |
temperature | 0.1–0.3 | Review scenarios require consistent output formats, to avoid verification logic deviations caused by randomness |
AIPROXY_API_ENDPOINT | Internal privately deployed OneAPI access address | Adapts to intranet access rules of government systems, to ensure data transmission security |
AIPROXY_API_TOKEN | Exclusive token generated according to system permissions | Restricts unauthorized access, complies with government data security management requirements |
vision | Enabled | Can verify visual content such as signatures on construction drawing review qualification certificates and drawing compliance |
Three common misconfigurations
- Symptom: The Embedding model returns a 401 unauthorized or connection timeout error when calling OneAPI. Cause:
AIPROXY_API_TOKENis not configured correctly, or the access address does not point to a valid port of the internal OneAPI service. - Symptom: The model's review result output format is chaotic, and material verification items are not listed as required. Cause: The
temperatureparameter is set too high, causing output randomness to exceed business expectations. - Symptom: After enabling the
visionfunction, the model cannot parse construction drawing images normally. Cause: A compatible visual analysis model is not configured, or visual processing parameters are not correctly enabled during access.
How to confirm successful configuration
- Submit a test construction permit application material, and check whether the model's returned verification result includes verification records for all required fields.
- View API call logs, confirm that requests for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENboth return a 200 status code, with no error messages. - Adjust the
temperatureparameter to 0.1 and 0.3, compare the output format differences between the two runs, and confirm that output stability meets business requirements. - Upload a test construction drawing image, check whether the model can identify signature information in the image, and verify that the
visionfunction is enabled normally.
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.