What data for this use case looks like
Data sources for building construction general contracting project bills include structured records from on-site construction management systems. They also include material procurement and acceptance documents, subcontract settlement reports, and paper on-site acceptance logs. Update frequency varies by scenario. Daily submitted construction progress bills are updated on the same day. Monthly aggregated cost and material bills are updated at the end of each month. Each bill document includes fields such as project number, building number, material name, incoming quantity, accepted qualified quantity, usage amount, remaining stock, and submission date. Units cover cubic meters, tons, square meters, labor days and other engineering-specific measurement standards. Some bills are associated with unstructured attachments such as scanned copies of paper acceptance slips. Reference only; verify against the target FastGPT release and deployment documentation.
Constraints for model access and configuration
Building construction project bills contain multiple fields and engineering-specific units. Models must accurately identify and convert data formats. Set sufficient context length to carry complete bill entries. Multi-source data has different update rhythms. Set reasonable request timeout thresholds during batch aggregation. This avoids inference failure caused by incomplete data loading. Some bills are associated with unstructured attachments such as scanned paper acceptance slips. Enable visual recognition-related configurations to extract structured content from images. Single-batch bill entry data volume is large. Restrict the token limit for single inference. This prevents context overflow or inference interruption.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Large single-batch data volume for building construction project bills, requires carrying complete bill entries and historical context |
maxTokens | 4000–6000 token | Limits output length per inference round, avoids inference timeout caused by overly long bill summary reports |
vision | Enabled | Some bills are associated with scanned paper acceptance slips, requires support for image content recognition to extract structured data |
temperature | 0.1–0.3 | Ensures accuracy of format and numerical values during bill data summary and report generation, avoids meaningless deviations in generated content |
AIPROXY_API_ENDPOINT | http://127.0.0.1:8000/v1 | Adapts to private model deployment requirements in building construction project scenarios, ensures data stays within the domain |
AIPROXY_API_TOKEN | Custom private token | Used for identity authentication, ensures security of model calls |
Three common configuration errors
- Model calls return 404 status code, and requests fail to forward properly. Cause:
AIPROXY_API_ENDPOINTfull path is not configured correctly, and the API suffix of the model service (such as /v1) is omitted, causing requests to fail to match the model service route. - Engineering unit conversion errors appear in bill summary reports, such as incorrectly recording steel bar tons as square meters. Cause:
temperatureis set too high, leading to meaningless deviations in model-generated content. - Inference interruptions occur when processing multiple bill entries in batches, with context overflow prompts returned. Cause:
maxContextsetting is smaller than the total token count of single-batch bill data, unable to carry complete input content.
How to confirm configurations are correct
- Initiate a single test call on the FastGPT platform model management page, input simulated bill data containing engineering-specific fields, confirm that the model can accurately identify fields and corresponding units.
- Upload scanned copies of paper acceptance slips as input, verify that the model can extract structured data content from them.
- Check model call logs, confirm that the request address and identity token configuration match the parameters of the privately deployed model service.
- Initiate a batch bill processing task, confirm that no timeout or context overflow prompts appear during the inference process.
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.