What the data for this category looks like
Infrastructure construction business data mainly comes from project approval documents, on-site construction logs, material inspection reports, completion acceptance reports, and similar materials. Data updates follow project execution milestones: construction logs are updated daily, and ledger reports are updated monthly. Most documents use structured fields, including project number, construction area, material specification, inspection time, and other fields. Units include meters, cubic meters, tons, workdays, and similar units. The length of individual documents varies widely, ranging from hundreds of characters to tens of thousands of characters.
What constraints these characteristics impose on model integration and configuration
Infrastructure construction business data has wide length variation, clear structured fields, and diverse units, which create multiple constraints for model integration and configuration. Long text documents consume more context quota, so values of context-related parameters must be adjusted appropriately. The need to recognize units for structured fields requires higher consistency in model output, so the temperature parameter needs calibration. The feature of data updated by milestones requires balancing knowledge base synchronization frequency and model call quota limits in configuration, to avoid process guidance errors caused by delayed data updates or exceeded call limits. The FastGPT v4.17.0 configuration interface must match the data characteristics of this category, and pre-set parameter templates adapted to construction field parameters.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the length variation of infrastructure construction project documents, preventing long texts from being truncated and losing key field information |
maxTokens | 2000–3000 token | Matches the single reply length required for business process guidance, ensuring complete step instructions and field verification requirements are covered |
quoteMaxToken | 1500 characters | Limits the length of referenced business document fragments, focusing on key content of core processes such as construction inspection and progress confirmation |
temperature | 0.2–0.4 | Reduces random output probability, ensuring accuracy of structured field recognition and process steps, adapting to the rigor requirements of construction work |
toolChoice | auto | Automatically selects tool calls, adapting to scenarios requiring querying project ledgers and material parameters during business processing |
AIPROXY_API_TOKEN | Fill in the exclusive key allocated by the platform | Ensures valid identity verification for model calls, avoiding channel unavailability caused by incorrect keys |
Three common misconfigurations
- Phenomenon: Prompt of no available channel after configuring
AIPROXY_API_TOKEN. Cause: The access permission of this key is not configured in the corresponding group, or there is a format error in the key entry. - Phenomenon: Inconsistent model configuration between conversation and workspace. Cause: Model parameters are not updated synchronously in global configuration and application configuration, leading to mismatched model versions or parameters across different scenarios.
- Phenomenon: Poor process guidance response speed and high hard disk IO usage. Cause: The
maxContextparameter is not set reasonably, leading to loading too many long text documents per call, increasing disk reading and model processing load.
How to confirm correct configuration
- Enter the model management interface of FastGPT v4.17.0, verify that the entries for
AIPROXY_API_TOKENandAIPROXY_API_ENDPOINTmatch those allocated by the platform. - Upload a typical construction inspection report document, trigger the process guidance test, and check whether the reply accurately identifies structured fields such as material specification and inspection time.
- Adjust the
temperatureparameter, trigger the process guidance for the same scenario multiple times, and confirm that the consistency of output results meets expectations. - View the model call log, confirm that the
maxContextandmaxTokensparameters of each call match the preset configuration, with no over-limit error reports.
All provided configuration values are common starting points, and should be evaluated 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.