What this category of data looks like
This category of data mainly originates from highway maintenance management systems, bridge and tunnel monitoring systems, road administration patrol systems, and traffic flow monitoring data, with a daily update frequency. Commonly processed documents include maintenance plans, patrol logs, disease disposal orders, road-related construction permits, and road property damage compensation orders. Document fields typically include section identifiers, event occurrence times, disposal process nodes, associated permit numbers, and other items. Some fields use units such as kilometers, days, and entries.
Constraints imposed on the model access and configuration workflow
Since data comes from multiple heterogeneous systems, model access must adapt to the format specifications of each data source to prevent field mismatch issues. The daily updated data volume must align with the FastGPT platform’s incremental update logic to avoid excessive resource consumption from full synchronization. Document types cover multiple structures including plan, log, and permit categories. Targeted chunking rules must be configured for different document types to ensure accurate matching of policy basis keywords during retrieval. Additionally, since errors may lead to road safety and compliance issues, model configuration must prioritize the relevance and accuracy of retrieval results.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Policy and business documents of this category have high relevance, so sufficient context must be retained to match complete regulatory basis |
temperature | 0.1–0.3 | Policy Q&A requires strict matching of original text expressions to avoid generating content that deviates from actual conditions |
quoteMaxToken | 2000–3000 token | Sufficient length of original text fragments must be retained as reference for Q&A to ensure compliance |
toolChoice | auto | Automatically trigger retrieval tools to match road maintenance and property rights policy documents related to the current query |
AIPROXY_API_ENDPOINT | Deployed large model service address | Specify the service entry for model access to complete connection with external large models |
AIPROXY_API_TOKEN | Access key for the corresponding service | Complete identity verification for model access to secure interface calls |
Three Common Configuration Errors
- Phenomenon: GPU resources are not utilized, and single-core CPU usage stays at 100%. Cause: The large model’s acceleration backend is not properly configured, or no deployment parameters specifying GPU usage are set, causing the model to run exclusively on the CPU.
- Phenomenon: The number of retrieval results does not match expectations, and relevant policy documents cannot be located. Cause: Context matching rules are not adjusted based on the document structure of this category, or chunking settings are unreasonable, leading to truncation of key information.
- Phenomenon: The model service cannot be connected after configuring
AIPROXY_API_ENDPOINT. Cause: The entered service address lacks the correct port path, or the access keyAIPROXY_API_TOKENhas insufficient permissions.
How to Verify Successful Configuration
- Check the model connection status panel in the FastGPT backend to confirm it shows connected with no error messages.
- Submit a test query for this category, such as "What is the regulatory basis for road property damage compensation", and confirm the returned results include document fragments from the corresponding category.
- Review the system’s resource monitoring panel to confirm GPU resources are being used normally and CPU usage falls within a reasonable range.
- Verify that the reference sources of returned results are correctly associated with document types of this category, such as patrol logs, road-related construction permits, and others.
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.