What the data for this category looks like
Refinery and chemical production data sources include distributed control system (DCS) real-time operating parameters, equipment ledgers, inspection records, safety operation procedures, and internal office notices. Update frequencies fall into three categories: real-time, scheduled, and on-demand. Real-time operating parameters update every few seconds. Equipment ledgers and safety procedures are updated quarterly or at revision nodes. Office notices are released on demand. Document structures include structured parameter tables (with fields such as temperature, pressure, operating duration, with units ℃, MPa, hours), semi-structured inspection logs, and unstructured text documents.
What constraints these characteristics impose on model access and configuration
Mixed multi-source data and structural differences require adapting content parsing rules for different types during model access. High-frequency updates of real-time parameters require the context window to hold enough latest data to avoid missing key scene information during model calls. Special units and fixed formats for structured fields require clear field mapping logic during configuration to ensure the model correctly identifies production-related parameters. Mixed scenarios of long and short texts require adjusting the model's token limit parameters to balance content completeness and call efficiency. Additionally, internal office and employee service scenarios require the model to associate both production data and administrative processes. Configuration must balance the recall weights of both types of data to avoid bias toward one category.
How to set the configuration
| Config Item | Recommended Value | Basis for This Value |
|---|---|---|
maxContext | 8000–12000 token | Refinery and chemical production scenarios include multiple types of content such as real-time operating parameters, inspection records, safety regulations, etc. Sufficient context is required to associate production scenarios with office needs |
maxTokens | 2000–3000 token | Balance the completeness and fluency of production problem responses, avoid excessive single output leading to timeout |
quoteMaxToken | 1500 token | Restrict the single-segment length of production documents and office files used for recall, ensuring recalled content does not occupy too much context space |
temperature | 0.3–0.5 | Production scenarios require stable and accurate responses, avoid setting too high to reduce irrelevant content generation |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address according to the deployment environment | Adapt to the deployment requirements of internal network isolation in refinery and chemical enterprises, ensure data transmission security |
AIPROXY_API_TOKEN | Generate dedicated tokens according to the enterprise permission system | Strictly control model access permissions, avoid unauthorized access to internal production and office data |
Three common mistakes
- Empty content is returned after model calls. Investigation finds that the refinery and chemical production-specific document index and reranking model are not bound. The cause is that the recall range was not specified as internal production and office datasets during configuration, so the model cannot obtain valid reference data.
- The locally deployed language model cannot be accessed via intranet IP. Investigation finds that the
AIPROXY_API_ENDPOINTconfiguration does not match the actual intranet address and port listened to by the server. The cause is that a public network address was mistakenly used during configuration, or the correct port was not filled in, causing the request to fail to reach the deployment node. - After upgrading FastGPT to v4.17.0, rule trace symbols appear at the end of dialogue outputs. The cause is that the rule display switch of the system prompt word was not turned off. By default, built-in rules are appended to the end of the output content.
How to confirm configuration is complete
- Submit a test request containing specific production parameter queries to the model, check whether the returned content associates fields and values from internal documents.
- View the model call log, confirm that the verification status of
AIPROXY_API_TOKENis passed, and there are no permission-related error messages. - Adjust the
temperatureparameter to the boundaries of the range, verify whether the stability of the output content meets the requirements of production scenarios. - Test the long text input scenario, confirm that the
maxTokensconfiguration does not trigger content truncation, and the output completeness meets expectations.
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.