What Data Looks Like for This Use Case
Data for this use case comes from construction engineering quality and safety supervision systems, on-site construction real-name management platforms, supervision logs, and test reports. Update frequency is weekly. Daily documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Most individual documents contain core fields such as project identifiers, supervision entities, rectification requirements, and acceptance conclusions. Some test reports include quantitative test indicators. Document formats are primarily PDF and TXT, with some sourced from structured files exported from internal systems.
Constraints for Model Access and Configuration
Weekly updated data requires configuring an index update strategy that supports weekly incremental synchronization during model access. This avoids resource consumption caused by full re-scanning. Differences in document formats and structures across multiple sources require parsing configurations compatible with PDF, TXT, and structured exported files. Adjust chunking rules for different document types as needed. Strict requirements for quality and safety related content require limiting context length and generation parameters. This prevents interference from irrelevant information. Mixed data of quantitative test indicators and unstructured rectification records requires configuring recall rules adapted to multiple content types. This ensures no key information is missed. Mixed recall of party building study content and quality and safety documents requires unified semantic matching rules. This ensures consistent recall accuracy for both types of content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context concatenation needs for multiple supervision documents and party building study content, avoids exceeding model context limits |
maxTokens | 2000–3000 token | Limits the length of single-round generated content, ensures clear and complete output of party building Q&A and quality and safety rectification requirements, aligns with reading habits of regulatory documents |
quoteMaxToken | 1500–2000 characters | Limits the length of recalled and cited document fragments, adapts to content extraction from short documents such as supervision inspection records and rectification notices, avoids redundant citations |
temperature | 0.2–0.4 | Reduces generation randomness, ensures party building study Q&A answers conform to official expressions, and outputs quality and safety related content with strict accuracy |
AIPROXY_API_ENDPOINT | Locally deployed document parsing service address | Adapts to localized processing needs for engineering documents, meets compliance requirements for data to stay within the domain |
AIPROXY_API_TOKEN | Access token generated by the local service | Ensures access security for the parsing service, prevents unauthorized calls |
Three Common Misconfiguration Issues
- Issue: Cannot select a configured model in the workflow. Cause: The model was not enabled on the system model configuration page, or the
AIPROXY_API_TOKENconfiguration is incorrect, causing service connection failure. This issue can be troubleshooted using the model channel test button in FastGPT v4.17.0. - Issue: Generated summary of uploaded TXT documents deviates significantly from original content. Cause:
maxContextwas not adjusted to fit context concatenation for short documents, or chunking rules did not match the line break format of TXT documents, leading to key information being split and omitted. - Issue: Quantitative indicators are missing from parsed PDF test reports. Cause: Parsing rules adapted to structured PDF documents were not configured, or
quoteMaxTokenvalue is too small, causing key indicator fragments to be truncated.
How to Verify Successful Configuration
- Access the system model configuration page, check that the configured model status is enabled, and that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENcan be tested for connectivity via curl command. - Upload a single supervision inspection record document, initiate a party building study Q&A test, confirm the returned answer includes key rectification requirements and supervision entity information from the document.
- Configure a weekly incremental synchronization task, view the index update log, confirm only newly added weekly documents are included in the recall range, with no full re-scanning records present.
- Adjust the
temperatureparameter to 0.1 and initiate a test, confirm the generated party building study Q&A answers contain no obvious subjective elaboration, and conform to official expression styles.
The values provided here are common starting points and should be evaluated against samples specific to the deployment.
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.