What the data for this category looks like
This category of data comes primarily from engineering quality and safety supervision systems, on-site construction real-name registration platforms, supervision logs, and test reports. Updates occur weekly. Daily documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Each document includes fields such as supervision subject, project information, hidden danger description, rectification requirements, and acceptance conclusion. No unified fixed units apply. Fields and parameters adjust based on actual project conditions. Data is generated during on-site construction supervision, covering quality and safety management links across the full process from project commencement to completion acceptance.
What constraints these characteristics impose on model access and configuration
Batch supervision documents updated weekly require the model to adapt to scheduled synchronization access logic, to avoid performance loss from real-time requests. The structures of multiple document types vary significantly, so context splitting rules adapted to different fields must be configured to ensure the accuracy of cross-document associated queries. The data covers the entire on-site construction process, so the model must support associating information from supervision plans, rectification records, and acceptance reports, to guarantee the integrity of hidden danger rectification tracking and acceptance verification links. On-site materials include a mix of structured and semi-structured content, so configuration must balance text extraction and field matching capabilities. The volume of documents processed in a single operation is large, so the context length of single-round requests must be limited to avoid timeouts or performance overruns.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Documents in this category are mostly multi-page supervision records and rectification reports, requiring sufficient context to associate cross-document hidden danger information |
maxTokens | 2000–3000 token | Addresses the summarization needs for single documents such as hidden danger rectification notices and acceptance reports, balancing output length and response speed |
temperature | 0.3–0.5 | Ensures the accuracy of supervision data processing, avoiding generation of rectification requirements or acceptance conclusions that do not comply with industry specifications |
toolChoice | auto | Automatically selects whether to call tools based on query requirements, adapting to multiple scenarios such as supervision inspection record verification and rectification closed-loop tracking |
AIPROXY_API_TOKEN | Bind a dedicated project secret key | Data in this category includes sensitive information from construction sites, so access permissions must be restricted to prevent data leaks |
Three common configuration errors
- Calling the model interface returns a 405 status code. The cause is incorrect configuration of the request method for
AIPROXY_API_ENDPOINT, or the interface permission has not been granted POST request access. - The model takes too long to think, failing to meet the rapid response requirements of daily supervision inspections. The cause is failure to properly set the
maxContextandmaxTokensparameters, resulting in the model loading excessive redundant context. - The file index status continuously displays "Indexing" and fails to complete document ingestion. The cause is failure to match the embedding model parameters adapted to this category, or the number of batch processed documents exceeds the currently configured concurrency limit.
How to confirm the configuration is complete
- Send a test query, enter the core issue of a single supervision inspection record, and verify whether the rectification suggestions returned by the model comply with the standard format of documents in this category.
- Upload a single hidden danger rectification notice, check whether the model can accurately extract core fields such as rectification responsible person and rectification deadline, and confirm that the context configuration can normally read document content.
- View the interface log corresponding to the model ID, confirm that both the request and response status codes are 200, with no abnormal errors.
- Initiate a batch document indexing task, observe whether the indexing progress advances over time, and confirm there is no abnormal state of continuous stagnation.
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.