What the data for this category looks like
Data sources include DCS/APC control systems, LIMS laboratory information management systems, tank farm metering systems, crude oil evaluation databases, equipment management systems, and energy management systems. Update frequencies include three categories: real-time, shift-based, and daily. Daily documents include operating procedures, production plans, material balance sheets, distillate export quality reports, interlock ledgers, shift handover records, and change management documents. Data fields include process parameters, material flow rates, product quality indicators, equipment status, energy consumption, and others. Corresponding units include tons per hour, cubic meters, ppm, kg/m³, and others.
What constraints these characteristics impose on the model access and configuration link
Multi-source heterogeneous data sources require the model access link to support adaptation to different system output formats. This prevents data parsing errors. Ledger data with varying update frequencies requires configuring synchronization tasks with matching trigger frequencies. This ensures real-time data is accessed promptly, and shift-based and daily reports are aggregated on schedule. Diverse document types require the model to support mixed processing of structured tables, semi-structured records, and plain text. Diverse data fields and units require the model to have basic unit identification and conversion capabilities. This avoids calculation deviations from cross-system data. Multi-unit coupled business logic requires the model to associate ledger entries across systems. This ensures the logical integrity of aggregated reports.
How to set the configuration
This document applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirements of multi-source petroleum refining ledger data, prevents truncation of critical process parameters and cross-device association records |
maxTokens | 4000 token | Controls the output length of a single report, matches the content scale of shift-based and daily aggregated reports |
quoteMaxToken | 1500 token | Limits the length of referenced original ledger data, ensures referenced content is complete and non-redundant during report generation |
temperature | 0.2–0.4 | Reduces generation randomness, ensures accuracy of numerical content such as process parameters and material flow rates |
AIPROXY_API_ENDPOINT | Fill in the proxy address actually deployed by the enterprise | Adapts to the internal network environment, resolves external API access restriction issues |
AIPROXY_API_TOKEN | Fill in the exclusive allocated secret key | Ensures identity legitimacy for model calls, prevents unauthorized access |
Three Common Configuration Mistakes
- Symptom: When adding a third-party model channel, the channel is marked as unavailable, even if testing passes in a standalone proxy tool. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not filled correctly in the FastGPT configuration, or the proxy address does not have access permissions enabled for the enterprise internal network. - Symptom: When calling the model to generate a report, the error "No available channels for the current group" is returned, with a specified request ID. Cause: The configured model channel is not bound to the corresponding business group, or the channel key has expired or its quota is exhausted.
- Symptom: Cross-device data association errors or unit confusion appear in the generated material balance report. Cause: The
maxContextparameter is not adjusted to the length suitable for multi-device ledgers, resulting in original associated data being truncated, and unit conversion rules cannot be identified.
How to Confirm the Configuration is Complete
- Run the built-in test function of the model channel, review the returned results, and adjust the filled content of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENbased on the test results. - Import a typical petroleum refining ledger sample, trigger the generation of an aggregated report, and check whether the generated content includes complete process parameters, material flow rates, and quality indicators.
- View the model call logs, confirm that the context length and output token count do not exceed the configured range, and adjust the values of
maxContextandmaxTokensbased on the logs. - Simulate a cross-device data association scenario, verify whether the generated report can correctly match ledger entries across systems, and adjust the context configuration based on the verification results.
The values provided are common starting points and should be measured against the reader's own samples.