What data for this category looks like
Data for this category primarily originates from engineering quality and safety supervision systems, on-site construction site real-name management platforms, supervision logs, and test reports. The update frequency is weekly. Daily processed documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Each document contains exclusive fields for its corresponding business scenario: for example, a hidden danger rectification notice includes fields for rectification time limit, rectification content, and re-inspection results. There are no unified fixed units; values are adjusted based on the business requirements of individual documents.
What constraints these characteristics impose on model integration and configuration
Decentralized data sources and the inclusion of multiple types of structured and unstructured documents require model integration to support multi-format data parsing. Weekly updated batch data requires batch processing configuration to avoid overload from single calls. Exclusive business fields require the model to accurately recognize terminology related to housing and construction sector quality and safety, and not rely solely on general generalization capabilities. Errors in reviewing core documents such as completion acceptance reports can lead to serious consequences, so strict result verification links must be configured to ensure the accuracy of review conclusions.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of weekly batch documents per batch, avoiding loss of critical information due to context truncation |
maxTokens | 2000–3000 token | Ensures the output length of a single review result, covering core content such as complete rectification re-inspection and acceptance condition verification |
temperature | 0.1–0.3 | Reduces result randomness, ensures consistency of review conclusions related to quality and safety, and avoids deviations from generalized extraction |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address according to the deployment environment | Adapts to the model call link for self-hosted or cloud deployment, ensuring security and stability of data transmission |
toolChoice | auto | Automatically selects whether to call tools, adapts to the process requirements of multi-document batch review, and reduces the complexity of manual configuration |
Three common configuration mistakes
- When a third-party large model is manually added, connection timeout or authentication failure is prompted. The cause is that the
AIPROXY_API_TOKENparameter is not configured correctly, or the access address does not match the model deployment environment. - The speech recognition module cannot select the SenseVoiceSmall model. The cause is that the access permission for the speech model is not enabled, or the platform does not pre-install the adaptation components for this model.
- The text extraction result contains a large amount of irrelevant content or misses core business fields. The cause is that the
temperatureparameter is not adjusted for exclusive documents in the housing and construction sector, leading the model's generalized extraction to deviate from business requirements.
How to confirm successful configuration
- A single hidden danger rectification notice is uploaded, and the model output is checked for exclusive fields including rectification content and re-inspection results, with field matching rate verified.
- A weekly batch of documents is submitted for testing, and the system is verified to complete all reviews within the preset time without context truncation errors.
- The
temperatureparameter is adjusted to different ranges, and the consistency of two review results is compared to confirm parameter effectiveness. - Third-party model access configuration is verified, a call is initiated, and the returned result is checked to conform to housing and construction quality and safety review business logic, with no authentication errors.
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.