What this category of data looks like
Data for this category originates from the engineering construction project approval management system, construction drawing review system, enterprise qualification database, and public credit platform, with a daily synchronization update frequency. Daily processed documents include construction permit application materials, construction drawing review qualification certificates, quality and safety supervision procedures, completion record forms, and similar items. The data structure includes fields such as project unique identifier, participating unit qualification information, approval node status, application material details, and more. All fields follow general specifications in the housing and urban-rural development sector, with no custom special units.
What constraints do these characteristics impose on the model access and configuration link
Multi-source data synchronization requirements demand configuration of interface permissions and data formats adapted to different systems. The daily update rhythm requires configuration of scheduled incremental synchronization tasks to avoid data lag that impacts response accuracy. Different types of approval documents have significant structural differences, so the model must have context processing capabilities adapted to diverse text formats. The need for precise matching between participating unit qualifications and approval nodes puts clear requirements on the model’s information extraction accuracy. Incorrect responses may lead to unlicensed construction or failure of completion record filing, so configuration must prioritize the rigor of outputs.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Engineering approval inquiries need to cover multiple approval materials and the complete approval process; sufficient context ensures response coherence |
maxTokens | 2000–3000 token | Service inquiry responses must clearly convey approval steps and material requirements, avoiding truncation of key information |
quoteMaxToken | 1500 characters | Precise reference to material fields and official requirements within the approval system is required; controlling referenced text length improves readability |
temperature | 0.2–0.4 | Approval responses require rigor and accuracy; a lower temperature parameter reduces output randomness |
vision | Disabled | Business data is primarily in text format, so visual processing capabilities are not needed |
AIPROXY_API_ENDPOINT | Fill in the access address of the internal proxy service | Adapt to the internal network environment and ensure stability of large model interface calls |
AIPROXY_API_TOKEN | Fill in the valid authentication token generated by the proxy service | Ensure interface call security and prevent unauthorized access |
Three common configuration errors
- Symptom: After replacing the large model, an error about the specified model persists, and only the first model in the
llmModelslist takes effect. Cause: Failed to correctly update all associated model call configurations in the configuration file, or the proxy service did not synchronously switch the target model. - Symptom: After deploying the vector model, it cannot be added via OneAPI. Cause: Failed to configure the correct port and access path for the vector model, or container network policies restricted external exposure of the port.
- Symptom: An authentication failure prompt appears after calling the proxy interface. Cause: Failed to correctly update the
AIPROXY_API_TOKENconfiguration, or the token expired and was not regenerated.
How to confirm the configuration is complete
- Enter an engineering approval-related inquiry, such as "What materials are required for construction permit application", and check if the returned result includes an accurate official material list and handling requirements.
- View the FastGPT v4.17.0 system logs to confirm that both large model and vector model call requests return successfully, with no timeout or authentication errors.
- Manually adjust the
maxContextparameter value to verify whether the processing effect of long document context meets expectations. - Check the running logs of the daily data synchronization task to confirm that incremental data has been correctly loaded into the knowledge base, with no synchronization failure records.
The values provided are common starting points and should be evaluated against the corresponding deployment 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.