What the data for this category looks like
The data mainly comes from project survey reports, structural design drawings, on-site survey logs, and administrative approval process documents, with versions updated on demand as each project phase is delivered. Documents are divided into submodules by specialty such as geotechnical engineering, structural engineering, water supply and drainage, etc. They include fields like project number, survey point coordinates, design parameter values, approval node status, and file version number. Commonly used engineering units such as meters, megapascals, square meters, and cubic meters are supported.
Constraints imposed by these characteristics on the model access and configuration link
The presence of specialized fields and engineering units requires configuring a prompt adapted to engineering terminology during model access to avoid unit recognition errors. The multi-specialty modular document structure requires dividing context grouping ranges by specialty during configuration to avoid interference from cross-specialty data. The on-demand update feature requires configuring a trigger logic for automatically synchronizing data sources to ensure the model uses the latest version of survey and design data when called. The existence of long documents requires adjusting the model context and token limits to avoid exceeding the model's processing threshold.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | The length of a single module of survey and design documents is mostly several thousand characters, requiring coverage of complete professional context |
maxTokens | 4000–6000 token | Engineering documents and approval processes have high token consumption, avoiding exceeding the model's output limit |
quoteMaxToken | 2000–3000 token | Sufficient original quoted fragments of survey and design documents must be retained to ensure the accuracy of process guidance |
toolChoice | auto | Automatic tool invocation is required to query real-time survey data or approval node information |
temperature | 0.2–0.4 | Business process guidance must remain rigorous, avoiding randomly generated content |
AIPROXY_API_ENDPOINT | Dedicated interface address for privately deployed models | Survey and design data is mostly internal sensitive data, requiring the use of a private proxy for security |
Three common errors
- Phenomenon: After integrating the FastGPT chat window via iframe on the frontend, business process results returned by the model cannot be obtained. Cause: The postMessage listening logic for the iframe is not configured, and the message transfer events between windows are not correctly bound.
- Phenomenon: When calling the model to query survey data stored in internal MongoDB, the token consumption displayed by the platform does not match the actual API call consumption. Cause: The
quoteMaxTokenparameter is not correctly configured, leading to a deviation between the platform's counted context tokens and the tokens actually processed by the model. - Phenomenon: An error is returned when using a privately deployed Qwen model to call the MCP MySQL query service, while the same model deployed online works normally. Cause: The API endpoint of the privately deployed model is not configured with correct access permissions, or the proxy key is not bound to the access permissions of internal data sources.
How to confirm the configuration is correct
- Initiate a query targeting a specific survey project process, and check whether the results returned by the model include the latest approval nodes and corresponding handling requirements for the current project.
- Check the platform operation logs to confirm that the token consumption of model calls matches the configured
maxTokensandquoteMaxTokenparameters, with no obvious overrun. - Test the message transfer logic of the iframe integration to confirm that the frontend page can normally receive the process guidance content returned by the model.
- Switch to the privately deployed model, and verify that the MCP service call can normally return survey data query results.
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.