What this category of data looks like
The data for building engineering general contract declaration materials and file review mainly comes from construction submission forms, construction drawing design documents, enterprise qualification certificates, sub-project acceptance records, and similar materials. Data is updated according to project submission milestones. The total number of documents for a single project increases as construction progresses. Document formats include PDF, Word, and Excel. Structures contain fields such as project number, construction section, submission batch, material details, and acceptance date. Units involved include standard engineering units such as square meters, cubic meters, and ten thousand yuan.
What constraints do these characteristics impose on model access and configuration
The data characteristics of building engineering general contract declaration materials and file review bring multiple constraints to model access and configuration. Mixed-format engineering documents require models to support multimodal parsing, with clear requirements for visual recognition capabilities. Content with multiple fields and engineering-specific units requires models to retain sufficient field association information during context processing to avoid parsing deviations. Data updated according to submission milestones requires the knowledge base synchronization rhythm to match the construction cycle, and model call frequency must align with data update frequency. File review requires comparison with historical submission files, and the need to recall relevant historical documents limits the available context length setting space.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| vision | Enabled | Declaration materials for building engineering general contracts include a large number of construction drawings and scanned submission forms, requiring recognition of engineering parameters and text content within images |
maxContext | 8000–12000 token | Must retain context for multiple historical files and current submission materials to avoid loss of field associations |
quoteMaxToken | 2000–3000 token | File review requires recalling sufficient historical submission documents to ensure complete relevant information |
temperature | 0.1–0.3 | Engineering review requires strict matching of fields and specifications, reducing random output |
AIPROXY_API_ENDPOINT | Fill in according to the officially designated address | Ensures stable model call links and adapts to the parsing needs of engineering data |
AIPROXY_API_TOKEN | Fill in according to the obtained authorization key | Completes legitimate model call authentication |
Three common mistakes
- Issue: Model calls return engineering field parsing errors, such as section numbers identified as garbled text or incorrect unit matching. Cause: The
visionconfiguration is not enabled, making it impossible to accurately recognize printed text and engineering parameters in scanned submission materials. - Issue: Channel link access returns "undefined" is not valid JSON error. Cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly, leading to abnormal model interface call links. - Issue: Review results do not match the latest submission materials. Cause: Knowledge base synchronization and model call frequency are not adjusted according to project submission milestones, resulting in calls using old knowledge base data.
How to verify a successful configuration
- Upload a single scanned construction drawing, trigger model parsing, and confirm that engineering dimensions, section information and other content can be extracted.
- After configuration is complete, generate a test review process, and verify that there are no abnormal errors in the model call link.
- Compare historical submission files with current materials, and confirm that the model can recall relevant historical documents and complete association verification.
- Adjust the
temperatureparameter, verify the consistency of output results, and confirm that randomness control meets review requirements.
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.