What the data for this category looks like
Data related to urban public transit business processing mainly comes from public transit group operation management systems, route ledger databases, and real-time passenger flow monitoring platforms. The data is divided into two categories: structured and unstructured. Structured data includes fields such as route number, station name, required documents list, processing time limit, and offline window address, with units including kilometers, minutes, and passenger trips. Unstructured data includes station guides, processing flow diagrams, announcements, and the like. Basic route information is updated quarterly, real-time passenger flow and queuing data is updated per minute, and document formats for offline processing procedures are fixed with no complex nested structures.
What constraints these characteristics impose on model integration and configuration
Fixed structured data fields for transit business processing and large differences in update rhythms mean appropriate retrieval and context length parameters must be configured for data sources with different update frequencies. Unstructured visual materials require visual model support, so the vision configuration item must be enabled. Process documents have no complex nesting, suitable for fixed-length context windows, but key step descriptions and material lists must be avoided from being truncated. The per-minute update of real-time passenger flow data requires the model calling interface to have low latency, so maxTokens must be reasonably set to limit reply length and avoid excessive generated content occupying too many interface resources. In addition, some urban public transit processing guidelines must strictly follow official rules, so the generation randomness of the model must be controlled within a reasonable range.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to multi-step documents for transit processing and multiple route information, avoiding truncation of key processing rules and material descriptions |
maxTokens | 1200–1800 token | Controls the generation length of a single processing guideline, ensuring complete information while avoiding excessive occupation of interface resources |
quoteMaxToken | 1800–2200 characters | Retains complete field content when retrieving structured data of transit routes and stations, preventing key information from being truncated |
vision | Enabled | Used to parse unstructured visual materials such as transit station guides and processing flow diagrams, supplementing intuitive explanations for text information |
temperature | 0.3–0.5 | Maintains the accuracy of processing guidelines, avoiding generation of content that does not comply with public transit operation rules |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the service provider | Connects to the specified vector or visual model interface, ensuring correct data transmission path |
AIPROXY_API_TOKEN | Fill in the authentication key assigned by the platform | Completes identity verification for model interface calls, ensuring request legitimacy |
Three Common Misconfigurations
- When using Qwen2.5-vl to process transit station guides, an error "failed to download image" is returned. The cause is that the marker service for parsing remote image resources is not configured, causing the model to fail to pull the incoming image link.
- After the agent enables the voice input function, there is no text conversion result for voice conversations. The cause is that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENof the voice model are not correctly configured, or the voice-to-text function switch is not enabled. - The transit route information retrieved by the vector model does not match the actual situation, with low matching accuracy. The cause is that the normalization configuration of the vector model is not enabled, resulting in incompatibility between non-normalized embedding vectors (such as Doubao's embedding) and the system default configuration.
How to Verify Successful Configuration
- Upload structured documents for transit processing procedures and station guides, initiate a test conversation, and check whether route information can be correctly retrieved and image content can be parsed.
- Check the model call logs in FastGPT v4.17.0 to confirm that the authentication requests for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENreturn normal status codes. - Adjust the
temperatureparameter and initiate multiple rounds of tests, checking whether the consistency of replies complies with the official rules of transit business. - Enable the vector model normalization configuration, compare the matching accuracy of retrieval results before and after adjustment, and confirm that the configuration takes effect.
The values provided are common starting points and should be evaluated against the user'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.