What this category of data looks like
Engineering consulting supervision declaration material and file data mainly comes from project approval documents, on-site supervision logs, phase acceptance reports, enterprise qualification declaration materials, and similar sources. Data updates per project milestone, and syncs immediately following major changes. Documents contain structured fields and unstructured attachments. Structured fields include project ID, project cost, supervision scope, and other items, with units such as ten thousand yuan, square meters, and workdays. Unstructured attachments are mostly long text formats, such as full-process supervision reports and rectification notices.
What constraints do these characteristics impose on model access and configuration
The data includes structured fields and long-text attachments, so the model must recognize both professional field formats and understand long-text logic. Adjust context length and reranking-related parameters. Fields use engineering professional units, so ensure the model retains unit recognition capability during calls to avoid errors in review judgments. High update frequency requires configuring a matching knowledge base sync cycle to ensure timeliness of review data. Multi-round review and circulation of declaration materials and files require a stable model call link to ensure consistency of review results.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the average text length of engineering consulting supervision files, avoids truncating core review data |
maxTokens | 2000–3000 token | Controls the output length of single-round review results, complies with file review document specifications |
quoteMaxToken | 1200–1800 characters | Limits the length of quoted declaration material fragments, ensures the reranking model can fully process input content |
temperature | 0.1–0.3 | Maintains rigor in review judgments, reduces unnecessary subjective bias |
AIPROXY_API_ENDPOINT | Fill in the dedicated address according to the deployment node | Ensures connectivity and stability of the model call link |
AIPROXY_API_TOKEN | Key assigned according to permissions | Ensures security of model calls, prevents unauthorized access |
Three common errors
- Phenomenon: The reranking model returns
false, and retrieval results have no sorting logic. Cause: Reranking trigger conditions are not configured correctly, ormaxContextis set beyond the model's supported upper limit, causing the reranking module to fail to start. - Phenomenon: Long-text declaration materials are retrieved, but reranking is not triggered. Cause:
quoteMaxTokenis set too small, truncating the retrieved long-text fragments, so the reranking model cannot obtain complete input content. - Phenomenon: Audit results from API calls and online conversations differ significantly. Cause: Online conversations enable context caching by default. API calls do not correctly configure the
maxContextparameter, leading to context truncation, or do not synchronize parameter settings from online conversations.
How to confirm correct configuration
- Check the model call logs in the FastGPT backend, confirm that the actual values of parameters such as
maxContextandquoteMaxTokenmatch the configured settings. - Upload a single supervision declaration material exceeding 5000 characters, check whether the reranking module is triggered and sorted results are returned.
- Initiate an API call, set
streamtofalseanddetailtotrue, compare the returned results with online conversation content to confirm parameter matching. - Verify the validity of
AIPROXY_API_TOKEN, ensure no 401 or 403 status codes are returned during calls.
Reference only; verify against the target FastGPT release and deployment documentation.
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.