What the data for this category looks like
Coal chemical production ledger data sources include real-time operating parameters from distributed control systems (DCS), raw material and product quality inspection reports from the laboratory, equipment operation and maintenance logs, and raw material inbound and product outbound documents. Update frequency is divided into three categories: real-time parameters are updated every 15 minutes, daily ledgers are automatically generated at midnight each day, and monthly summary ledgers are archived before the 5th of each month. The structure of a single ledger document includes fields such as equipment number, operating temperature, pressure, flow rate, laboratory test values, operator, and timestamp. Most units follow industry standards, including ℃, MPa, m³/h, and ton.
What constraints these characteristics impose on the "model access and configuration" link
Multi-source heterogeneous data sources require support for cross-data-source field mapping during configuration, to prevent model recognition errors caused by differences in field names across systems. Mixed update frequencies (real-time, daily, monthly) require context length configuration to cover ledger data across different time spans, to avoid incomplete recalled data. Specific field and unit requirements require preset field mapping rules during configuration, to ensure the model can accurately recognize parameter units and prevent unit confusion in generated content. Long-text ledger aggregation needs require sufficient context and token limits, to avoid truncation of critical data.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single coal chemical ledger records are relatively long, and multi-source data recall requires sufficient context to cover complete parameter information |
maxTokens | 4000–6000 tokens | Monthly summary reports need to process 30 days of historical ledger data, sufficient token limits prevent truncation of critical content |
quoteMaxToken | 1500–2000 tokens | Recalled ledger fragments must retain complete key parameters and units, to ensure quoted content in generated reports is accurate and credible |
temperature | 0.1–0.3 | Ledger aggregation and report generation require strictness, to avoid generating redundant content that deviates from factual details |
AIPROXY_API_ENDPOINT | Fill in according to the official node of the accessed model | Adapt to interface protocols of models from different vendors, prevent call failures caused by incorrect node configuration |
AIPROXY_API_TOKEN | Fill in according to the applied key | Complete identity verification for model calls, ensure interface call permissions are legitimate |
Three common mistakes
- Phenomenon: A 400 status code is returned when calling an Azure OpenAI model, prompting an interface protocol mismatch. Cause:
AIPROXY_API_ENDPOINTwas not configured to the Azure-specific API node, and the general OpenAI interface address was still used. - Phenomenon: Parameter unit confusion appears in generated reports, for example, writing the pressure unit MPa as the temperature unit ℃. Cause: No preset field mapping rules were configured, so the model cannot recognize the field and unit correspondence across different ledger systems.
- Phenomenon: Monthly summary reports are missing 7 days of real-time ledger data. Cause:
maxContextwas set too small, failing to cover the context length of 7 days of real-time data, leading to incomplete recalled data.
How to confirm the configuration is complete
- Call the model interface, view the returned debug logs, confirm that the field mapping rules have been correctly applied, and parameters match ledger fields.
- Import a single piece of historical ledger data, test generating a short summary, check whether all required parameters and their corresponding units are retained.
- Simulate batch import of monthly ledger data, verify that the generated summary report is not truncated prematurely and fully covers all input content.
- Replace the test model key, confirm that the interface call can complete identity verification normally, with no authentication-related errors.
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.