What the Data for This Category Looks Like
Data for urban public transit documents comes from bus operation dispatching systems, ticket settlement systems, safety management ledgers, route adjustment announcements, passenger service records, and more. Daily operation data is updated daily. Route policy documents are updated as needed. Monthly revenue and safety summary reports are generated each month. Documents include structured tables such as shift statistics sheets and revenue details, and unstructured text such as safety rectification notices and passenger complaint response letters. Fields include route number, shift time period, passenger trips, revenue amount, complaint number, and others. Units include trips, yuan, shifts, kilometers, and more.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The mixed structure of structured and unstructured documents requires clarifying the current document type during multi-turn dialogue. This prevents chaotic formatting of generated documents. Dispersed data sources with varying update rhythms require confirming the data time range and source range in the initial multi-turn dialogue stage. This ensures documents use the latest valid data. The large number of fields with fixed units requires gradually guiding users to supplement necessary fields during multi-turn dialogue. This prevents missing items or incorrect units in generated documents. Different document types have fixed templates. This requires specifying the current drafting document category in prompt engineering to match corresponding format requirements.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Context window size | 8000–12000 preceding characters | Urban public transit documents contain multiple fields such as route and shift information. Sufficient multi-turn dialogue context is needed to avoid loss of key information |
| Variable binding | Bind route number, shift time period, passenger trips from variables | Matches fixed business fields for urban public transit documents. Reduces repeated inquiries during multi-turn dialogue |
| Dialogue context binding | Bind conversation history by chatId | Ensures consistent context across multi-turn dialogue. Prevents repeated requests for the same information |
| Dialogue message storage | Save in messages array format | Fully retains content from each dialogue round. Supports subsequent context review and reuse |
| Reply format constraint | Output in the specified JSON format | Enables automatic import of generated documents into public transit operation management systems later |
| Maximum dialogue rounds | 6 rounds | Aligns with conventional steps for public transit document drafting. Prevents context overflow from overly long multi-turn dialogue |
| Timeout setting | 300 seconds | Reserves sufficient time for information integration and format adjustment when processing multi-field public transit operation documents |
Three Common Errors
- Attempting to upload a bus operation ledger attachment triggers the error
fail to create post presigned url. The cause is incorrect configuration of cross-origin access rules for S3 storage, resulting in failed attachment uploads. - Requesting the AI to reply in JSON format but receiving plain text content. The cause is failure to explicitly specify the JSON field structure and required items in the prompt engineering, or incorrect binding of business variables.
- An error occurs when uploading files to RAG after upgrading the version, or an error appears in the dialogue window. The cause is failure to correctly migrate the original knowledge base and S3 storage configuration, resulting in abnormal context calling and attachment upload functions.
How to Confirm the Configuration Is Complete
- Initiate a test dialogue, enter the route number and shift time period, and check whether the AI can automatically associate the bound business variables
- Upload a public transit operation ledger attachment, confirm that the upload succeeds and no error prompts are displayed
- Request the AI to reply with test content in the specified JSON format, check whether the returned result meets the preset format requirements
- Initiate a multi-turn dialogue within 6 rounds, confirm that context information is not lost, and the generated document contains all necessary business fields
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.