What the Data for This Use Case Looks Like
Data sources include toll station traffic record ledgers, overload detection enforcement case files, construction permit application documents, and road administration patrol logs. Update frequencies cover real-time (single traffic record), daily (same-day enforcement case summaries), and weekly (monthly declaration material archiving). Documents exist in the form of structured fields paired with attachments. Structured fields include toll passage card number, overload detection weight, approval document number, enforcement time, and other items. Units include tons, hours, and kilometers. Most attachments are scanned PDF files or scanned paper documents in image format.
Constraints on Model Integration and Configuration
Differences in data timeliness and archiving cycles require model access configuration to adapt to input content of varying lengths. This prevents resource waste from short-text calls or exceeding context limits with long texts. The input format combining structured fields and unstructured scanned attachments requires configuring support for both structured data extraction and multimodal content parsing. Most attachments are scanned PDFs or images, so corresponding functions must be enabled to recognize text within images. Data with different update frequencies must adapt to batch processing concurrency rules. This prevents single calls from exceeding model load thresholds.
Configuration Guidelines
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long-text review needs of expressway case files, avoids truncating key approval process information |
maxTokens | 2000–3000 token | Controls the output length of single-round reviews, aligns with the output specification for structured extraction results of declaration materials |
vision | Enabled | Used to parse text content in scanned case attachments, covers recognition needs for unstructured attachments |
toolChoice | auto | Automatically selects whether to call tools for structured field extraction, adapts to diverse scenarios with mixed inputs |
temperature | 0.1–0.3 | Reduces random output probability, ensures consistency and accuracy of case review results |
AIPROXY_API_ENDPOINT | Fill in the dedicated address based on the deployment environment | Connects to the dedicated proxy service, ensures data transmission security and stability |
AIPROXY_API_TOKEN | Dedicated key assigned according to permissions | Verifies call identity, prevents unauthorized access |
Three Common Configuration Errors
- Phenomenon:
Unexpected end of JSON inputerror occurs during calls. Cause:AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly, causing the proxy service to return an invalid response format. - Phenomenon: Off-topic results appear during long-text case review. Cause:
maxContextis set too small, truncating key context information and preventing the model from associating complete approval logic. - Phenomenon: Text content in scanned attachments cannot be recognized. Cause: The
visionconfiguration is not enabled, and multimodal content parsing capability is not activated.
How to Verify Successful Configuration
- Upload structured data and scanned attachments, verify that the model can simultaneously extract structured field content and recognize text in scanned documents.
- Submit long-text case materials, check that the output does not show truncation or off-topic situations, and adjust
maxContextto a value suitable for the current scenario. - Call the proxy interface, verify that the returned response format conforms to JSON specifications and has no invalid errors.
- Adjust the
temperatureparameter to the appropriate range, submit multiple similar case files, and check the consistency of output results.
The values provided here 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.