What This Category of Data Looks Like
Urban public transit declaration materials and case review data originates from two primary sources: line operation declaration documents and daily operation case files. Line declaration materials include line adjustment applications, fare verification reports, safety assessment documents, and similar items. These materials are mostly a mix of structured tables and PDF scan attachments. Daily operation case files include passenger complaint tickets, vehicle fault records, station yard use approval forms, and similar items. These files are mostly structured text. Update frequencies follow these rules: declaration materials are updated per individual business cycle; case file data is synced daily. Standardized fields include line number, operation period, verified passenger capacity (unit: person-times), line length (unit: kilometers), and other similar items. Some materials include handwritten annotations or stamped scan attachments.
Constraints for Model Integration and Configuration
Mixed structured and unstructured data requires models to support both field extraction and general text comprehension. Integration work must balance standardized field parsing and complex scenario understanding. Daily synced case file data requires context update rhythms that match the business cycle. This prevents delays that cause review lag. Standardized field format requirements must strictly align with the data source. Misalignment leads to model output review conclusions conflicting with actual business rules. PDF scan attachments in declaration materials require visual recognition capabilities. These capabilities parse non-standard visual elements such as handwritten annotations and stamped documents.
Configuration Settings
This document uses the configuration logic of FastGPT v4.17.0. The following configuration items are optimized for the urban public transit declaration materials and case review scenario:
| Configuration Item | Recommended Value/Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average text length of urban public transit declaration materials and case files. Prevents context truncation that causes missing review information |
maxTokens | 2000 token | Controls the length of single-round review output. Ensures approval opinions are clear and complete, and comply with internal document specifications |
quoteMaxToken | 1500 token | Retains sufficient original reference fragments to support the basis explanation for review conclusions |
vision | Enabled | Parses visual attachments such as PDF scan files and stamped documents in declaration materials |
temperature | 0.2–0.4 | Reduces random output. Ensures the rigor of review conclusions and complies with standardized government approval requirements |
AIPROXY_API_ENDPOINT | Fill in the dedicated address according to the deployment node | Adapts to network access rules for local or private cloud deployments. Ensures secure data transmission |
AIPROXY_API_TOKEN | Fill in the dedicated authentication key | Ensures the legitimacy of model call permissions. Prevents unauthorized access |
Three Common Configuration Errors
- Phenomenon: After running code, generated review results cannot be passed into the model context. The interface displays error logs of the type
chat:ai_input_is_e. Cause: The code execution result is not injected into the model input queue in the specified context variable format. This prevents the model from obtaining external calculation data. - Phenomenon: After locally deploying an Ollama model, FastGPT deployed via Docker Compose cannot call the model. A connection timeout error is returned. Cause: Container network intercommunication rules are not configured correctly. This prevents the FastGPT container from accessing the listening port of the local Ollama service.
- Phenomenon: The model's output review opinions do not match the preset approval rules. Adjusting the general prompt word does not produce obvious improvement. Cause: The prompt word is not bound to the dedicated urban public transit approval scenario. It does not clearly limit the output format and review dimensions. This leads to generalized model output content.
How to Confirm Successful Configuration
- A PDF attachment of an urban public transit line adjustment declaration may be uploaded. Verify that the model correctly extracts core fields such as line number and operation period.
- A daily case review process may be triggered. Check that the model's output review opinions include original reference fragments and that no context truncation occurs.
- Model call logs may be reviewed. Confirm that the configuration values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENmatch the deployment environment, with no connection errors. - The
temperatureparameter value may be adjusted. Verify that the rigor of the model's output meets expectations, and that no overly divergent content appears.
The values provided here are common starting points. They should be evaluated against relevant 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.