What the data for this category looks like
Refinery production declaration materials and case review data come from MES production logs of refinery units, material balance reports, safety supervision filing dossiers, and project feasibility study documents for declaration. Update rhythm proceeds in real time aligned with single declaration project cycles or daily review processes. Single case document length ranges from dozens to hundreds of pages, and includes structured and unstructured fields such as unit number, material brand, energy consumption quota (unit: ton standard coal/ton product), approval node status, and compliance check items.
What constraints these characteristics impose on model access and configuration
The wide range of document lengths for refinery declaration materials and review cases requires model context configuration to cover inputs of different lengths. This avoids truncation of core compliance check content. Professional field units and terms such as ton standard coal/ton product and unit operating rate require the model to understand refinery industry terms. This prevents professional deviations in review conclusions. Mixed structured and unstructured data from multiple sources requires configuring parsing adaptation rules for multiple types of data. A single review involves a large number of compliance check items. The tool call trigger and return logic must adapt to multi-task processing requirements.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the text length of hundreds of pages of refinery case documents, avoids truncation of core compliance check content |
maxTokens | 2000–3000 token | Controls the output length of review conclusions, ensures professional judgments are clear and complete |
toolChoice | auto | Automatically adapts to tool call requirements such as compliance checks and data comparison in declaration materials |
temperature | 0.2–0.4 | Reduces random output, ensures consistency of professional review conclusions |
AIPROXY_API_ENDPOINT | Exclusive enterprise intranet node | Adapts to the network security requirements of refinery enterprises, ensures compliance of data transmission |
AIPROXY_API_TOKEN | Exclusive key assigned by role | Differentiates operation permissions of different review personnel, complies with enterprise data security specifications |
Three common errors
- Phenomenon: Tool call returns empty results or status code 500. Cause: No parameter check rules for tool calls configured for refinery professional fields, causing the model to fail to recognize the input format of compliance check items.
- Phenomenon: The review conclusion output by the model does not match the professional units in the case. Cause: The
temperaturevalue is too high, causing deviations in professional terms generated by the model. - Phenomenon: Model configuration is lost after deployment restart. Cause: The model configuration file is not mounted to the persistent storage directory, causing the temporarily stored configuration to be cleared after restart.
How to confirm configuration is effective
Under FastGPT v4.17.0, configuration effectiveness can be confirmed via the following actions:
- Upload a typical refinery project declaration case. Check that the model input text is not truncated, and confirm that the context configuration adapts to the document length.
- Initiate a compliance check tool call. Check that the match rate between the tool return result and the data in the case meets preset requirements, and confirm that the tool call configuration takes effect.
- Restart the deployment environment. Check that the original model configuration and application settings are not lost, and confirm that the persistent storage configuration is correct.
- Adjust the
temperatureparameter and regenerate the review conclusion. Check that the consistency of the conclusion meets expectations, and confirm that the parameter configuration takes effect.
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.