Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Business data for refinery and chemical production mainly comes from production real-time control systems, equipment inspection logs, material flow records, and safety compliance manuals. Real-time process parameter data is updated every second. Equipment inspection and material flow logs are synced daily. Safety compliance documents are updated as needed. Single business processing documents (such as hot work permits, equipment maintenance applications) usually include work area code, approval node sequence, validity period, and risk level fields. Parameter data uniformly uses MPa, ℃, and cubic meters per hour as units of measurement.
Constraints imposed by these characteristics on model integration and configuration
Real-time second-level updated process parameters require configuring a reasonable context refresh interval during model integration. This avoids cached expired data causing guidance deviations. Multi-field business document structures require the model to accurately match fields and approval nodes, and limit the redundancy of generated content. Differences in update rhythms across data sources require configuring layered recall rules to distinguish call priorities between real-time parameters and static documents. Unified units of measurement require the model to automatically adapt to the on-site unit system when generating guidance, to avoid unit conversion errors.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers the context requirements of multi-node approval processes and real-time process parameters for refinery and chemical business, to avoid truncation of critical information |
maxTokens | 2000–3000 token | Limits the length of single-round guidance output, adapts to the reading rhythm of on-site operators, and avoids information overload |
quoteMaxToken | 1500 characters | Limits the total length of recalled business documents, ensuring that matched compliance clauses and process parameters are accurate and not redundant |
temperature | 0.3–0.5 | Reduces generation randomness, ensures standardization and accuracy of business guidance, and meets the rigorous requirements of refinery and chemical production |
AIPROXY_API_ENDPOINT | Fill in the private proxy address according to the deployment environment | Adapts to the offline deployment needs of refinery and chemical scenarios, ensuring that core data does not leave the factory |
AIPROXY_API_TOKEN | Exclusive key bound to the proxy service | Ensures the security of access permissions, avoiding unauthorized calls |
Three common misconfigurations
- Post-deployment call error
do_request_failed, with prompt of request timeout or invalid address. The cause is failure to correctly configureAIPROXY_API_ENDPOINTto the actual private proxy address, or failure to open the corresponding port in the firewall. - Generated guidance content has unit conversion errors, such as displaying MPa as bar. The cause is failure to explicitly require adaptation to the legal unit system used at the refinery site in the model configuration, and failure to standardize output format through preset constraints.
- Recalled business documents do not match the current processing business, with too many or too few results. The cause is failure to reasonably set
quoteMaxToken, leading to too large or too small recall range, and failure to match the business classification tags of the documents.
How to confirm proper configuration
- Manually enter a typical question about refinery and chemical production business processing, check whether the guidance returned by the model includes correct approval nodes and parameter requirements.
- View the model call log, confirm that the request address of
AIPROXY_API_ENDPOINTmatches the actual deployment address, with no spelling errors. - Test calls from data sources with different update frequencies, confirm that the recall results of real-time parameters and static documents meet expectations.
- Adjust the
temperatureparameter, verify whether the randomness change of generated content meets the business rigor requirements.
The values provided are common starting points and should be evaluated 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.