What this category of data looks like
This category of data originates from engineering quality and safety supervision systems, construction site real-name platforms, supervision logs, and test reports. It updates on a weekly basis. Daily documents include supervision plans, supervision inspection records, hidden danger rectification notices, quality problem handling forms, and completion acceptance supervision reports. Most document fields contain project number, participating unit name, inspection time, hidden danger description, rectification requirements, closed-loop status, and other items. Some documents attach on-site images or test value content. A single service inquiry usually requires retrieving multiple related documents.
Constraints Imposed by These Characteristics on Model Access and Configuration
The weekly data update rhythm requires the vector index refresh cycle to match this frequency. Too short a refresh cycle causes resource waste, while too long a cycle leads to data lag. Diverse document types including images and value fields require adaptation to multimodal processing and structured data parsing logic. Large individual document sizes and the need to associate multi-source data put high requirements on the model’s context window capacity. The service inquiry scenario requires fast response to business requests, so model call delay must match on-site office response expectations. At the same time, retrieval accuracy of content related to hidden danger rectification and completion acceptance must be guaranteed to avoid impacting business processes.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 12000–15000 token | Inquiries in this category often require association with multiple supervision records and rectification forms, needing space for extensive context content, and complying with the context limit specifications of FastGPT v4.17.0 |
maxTokens | 4000–6000 token | Service inquiry replies need to cover details such as hidden danger descriptions and rectification requirements, to avoid truncating critical business information |
temperature | 0.3–0.5 | Ensures the accuracy and business standardization of replies, avoiding irrelevant or divergent generated content |
quoteMaxToken | 800–1000 token | Needs to extract sufficient document fragments as reply basis, while controlling total context length to avoid excessive load |
AIPROXY_API_ENDPOINT | Fill in the exclusive address provided by the platform | Adapts to the large model call link for this category, ensuring interface connectivity and data transmission security |
Three Common Configuration Mistakes
- A
504 Gateway Timeouterror occurs after calling, with response time exceeding 30 seconds. The cause is failure to adjust themaxContextparameter for the multi-document association scenario of this category, leading to excessive context loading volume and overloaded model processing. - The model call works normally during testing, but returns an error after official deployment. The cause is incorrect configuration of access permissions for
AIPROXY_API_TOKEN, or failure to match the input format requirements of the target model. - The reply content does not match actual business, such as incorrectly marking the closed-loop status of hidden danger rectification. The cause is failure to set
temperaturewithin a reasonable range, leading the model to generate content that does not conform to actual business and damaging reply accuracy.
How to Verify Successful Configuration
- Initiate a single service inquiry test, check whether the returned result contains key business fields of the corresponding document, such as project number, rectification requirements, and other items.
- View the model call log, confirm that the interface response time meets business expectations, and no timeout occurs.
- Switch different model configurations, verify the connectivity of the call link, and confirm that no errors are returned.
- Upload a single supervision inspection record, test whether the number of retrieved results matches the preset retrieval logic.
The values provided here are common starting points, and should be evaluated against the corresponding deployment 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.