What data for this category looks like
Data for this category mainly comes from engineering quality and safety supervision systems, construction site real-name platforms, supervision logs, and test reports. Data is updated weekly. Daily generated documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Most document fields include project numbers, on-site locations, rectification requirements, and acceptance conclusions. There is no unified fixed cross-project field format. Single document lengths range from hundreds to thousands of words.
Constraints on model access and configuration
Data sources for this category are scattered, and update cycles are fixed. Model access must support scheduled batch synchronization of multi-source data. Daily documents have no unified field format. Configure parsing and retrieval logic adapted to unstructured text to avoid retrieval issues caused by missing fields. Single document lengths vary widely, from hundreds to thousands of words. Limit the maximum processing length of single-round context to prevent exceeding model capacity limits. Policy and regulatory basis Q&A requires precise matching of regulatory clauses. Configuration must prioritize retrieval accuracy to ensure returned content directly corresponds to regulatory basis.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the requirement of associating multiple weekly updated documents for single-round conversations in this category, avoiding context overflow |
maxTokens | 2000–3000 token | Ensures the output length of policy basis Q&A covers regulatory clause details, while controlling single-round response latency |
quoteMaxToken | 1500 token | Limits the total length of retrieved documents, ensuring retrieved content is highly relevant to the current query and avoiding interference from redundant information |
temperature | 0.1–0.3 | Ensures the rigor of policy responses, avoiding content that does not comply with regulatory clauses |
AIPROXY_API_ENDPOINT | http://127.0.0.1:8000/v1 (calibrated via actual testing) | Adapts to offline deployment scenarios, ensuring data is not leaked externally |
AIPROXY_API_TOKEN | Access key generated by the local model | Ensures the security of model access, preventing unauthorized access |
Three common misconfigurations
- Symptom: Clicking the model test function displays a "request error" prompt, and curl calls to the model interface return connection timeout. Cause:
AIPROXY_API_ENDPOINTis not correctly configured to the actual service address of the local offline model, or port permissions for the model service are not enabled. - Symptom: Knowledge base queries return unorganized raw document fragments directly. Cause:
maxTokensis not configured, or its value is too large, preventing the model from integrating and organizing retrieved content. - Symptom: Vector model access fails, and document vectors cannot be generated. Cause: Vector model access parameters are not correctly configured, or an incompatible vector model format is used.
How to confirm configuration is complete
- Run the model test function. Confirm the interface shows no error prompts, and returned results include compliant policy basis text.
- Upload a single supervision inspection record document, trigger knowledge base retrieval, and confirm retrieved documents are relevant to the current query.
- Adjust the
temperatureparameter, observe changes in response rigor, and confirm the parameter configuration takes effect. - Check FastGPT v4.17.0 backend logs, and confirm no error messages related to model access are present.
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.