What the data for this category looks like
Rail transit equipment ledger data originates from on-board operation sensors, offline maintenance systems, parts lifespan databases, and operation and maintenance management platforms. Data updates follow two rhythms: on-board real-time operation data updates every second, while offline maintenance ledgers sync daily or weekly. A single ledger document includes structured fields and unstructured attachments. Structured fields include equipment number, cumulative operating duration (unit: hours), maintenance cycle (unit: days), fault code, and replacement time. Unstructured attachments are PDF-format maintenance reports and troubleshooting records. The data is multi-source and heterogeneous, including structured records stored in relational databases as well as manually filled text content.
What constraints these characteristics impose on the "model access and configuration" workflow
High-frequency on-board real-time data updates require model calls to limit context length, preventing timeouts from excessive data volume. Structured ledgers with multiple fields and parameters with specific units require the model to accurately recognize semantics and formats, preventing incorrect SQL statement generation. The length of unstructured maintenance reports varies widely, so long-text processing logic must be adapted. Cross-system data source access requires configuring a unified model proxy address. Under compliance requirements, domestically deployed large models are prioritized to avoid cross-border data transmission risks.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers historical ledgers and real-time operation data of multiple rail transit equipment, avoids context overflow |
maxTokens | 2000–3000 token | Fully outputs the results of ledger summarization and report generation, prevents key statistical information from being truncated |
toolChoice | auto | Automatically triggers SQL generation and text extraction tools, adapts to mixed processing scenarios for multi-source data |
temperature | 0.1–0.3 | Reduces generation randomness, ensures stability of SQL statement formatting matching ledger fields |
AIPROXY_API_ENDPOINT | Fill in the standard address of the domestic model proxy | Adapts to domestically deployed large models, complies with data compliance requirements |
AIPROXY_API_TOKEN | Fill in the access key for the corresponding model | Completes identity verification for model access, guarantees call permissions |
Three common configuration mistakes
- Generated SQL statement execution fails. Log checks show no spaces between keywords and fields, with only line breaks removed. The cause is failing to adjust the
temperatureparameter for the multi-field structure of rail transit ledgers, leading the model to simplify formatting output. - Long-text maintenance report generation is truncated. Task status checks show the output length exceeds the limit. The cause is failing to adjust the
maxTokensvalue based on the estimated length of a single report. - Unable to access the specified domestic model. The interface prompts API authentication failure. The cause is incorrectly using the access key for overseas models, without replacing it with the
AIPROXY_API_TOKENfor the corresponding domestic model.
How to confirm the configuration is complete
- A test call for a single piece of equipment’s ledger is initiated, and the returned SQL statement is checked for correct space-separated keywords and fields.
- Batch ledger data containing multiple pieces of equipment is submitted, and the generated summary report is confirmed to fully cover preset statistical dimensions with no content truncation.
- The configured domestic model proxy address is called, and normal return of model responses is verified with no authentication or connection errors.
- The
temperatureparameter is adjusted to 0.2, three repeated tests are initiated, and the formatting of the generated SQL statement is checked for consistency.
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.