What the data for this category looks like
Urban gas ledger data is sourced from station SCADA operation systems, pipeline inspection apps, user safety inspection files, and pressure regulating equipment monitoring terminals. Real-time operation data is refreshed and archived every 5 minutes. Inspection records are submitted daily. Monthly summary ledgers are generated within 3 working days after the end of the natural month. Each ledger document includes fields such as station number, inspection time, equipment pressure value, leak detection result, and inspector ID. Pressure values are measured in kilopascals, leak detection results are measured in ppm, and station numbers are 6-digit numeric codes.
Constraints for Model Access and Configuration
The differing update rhythms of multi-source urban gas ledger data require that model access support batch synchronization of different data types, to avoid overloading single requests. Numeric fields with fixed units and standardized coded fields require explicit specification of parsing formats during model calls, to prevent mismatches between output results and actual data formats. The large single-batch data volume of monthly ledgers requires adaptation to large language model context window limits, to avoid data truncation. The high-frequency updates of real-time operation data require that model call response delays be controlled within a reasonable range, to ensure the timeliness of summary reports.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The single-batch data volume of urban gas monthly ledgers is large, so the context window must accommodate complete multi-type data including station, inspection, and safety check data to avoid context truncation |
maxTokens | 2000–3000 token | The output length of ledger report generation must cover summary data and analysis conclusions, while controlling resource consumption for single calls |
temperature | 0.1–0.3 | Ledger summary requires maintaining data accuracy, avoiding analysis content that deviates from actual data |
AIPROXY_API_ENDPOINT | http://[Xinference deployment address]/v1 | When connecting to a locally deployed large language model via Xinference, the API interface path format must be matched |
AIPROXY_API_TOKEN | Access key generated by the user | Ensure identity verification security for model calls, preventing unauthorized access |
quoteMaxToken | 500 token | Extract key inspection records from ledgers as reference basis, controlling the length of referenced content |
Three Common Configuration Mistakes
- The interface displays "No available models". This occurs because
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENis not configured correctly, causing the proxy service to fail to connect to the model instance deployed via Xinference. - The re-ranking model test returns empty results or a 500 status code. This occurs because the correct name of the re-ranking model deployed via Xinference is not matched, or the dedicated access path for the re-ranking model is not specified in the configuration.
- A single ledger summary triggers a context limit exceeded error. This occurs because the
maxContextparameter is not adjusted, preventing the complete monthly ledger data from being loaded by the model.
How to Verify Correct Configuration
For FastGPT v4.17.0, complete the following verification steps:
- Use the built-in model connectivity test tool in FastGPT to check whether basic information of the model deployed via Xinference is returned, confirming that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly. - Upload a single urban gas inspection ledger record, trigger a model call, and check whether the output includes correct field information such as station number and pressure value unit, confirming that parameters adapt to the data format.
- Import monthly bulk ledger data, trigger a summary task, and check for context truncation or incomplete output, confirming that
maxContextandmaxTokensparameter settings are reasonable. - Trigger a re-ranking model test, check whether sorted results of the specified number of entries are returned, confirming that the re-ranking model access configuration matches the model deployed via Xinference.
The configuration values listed above are common starting points. Adjustments should be made based on the user’s own specific samples and operational requirements.
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.