What the data for this category looks like
Data related to document and material drafting for urban gas mainly comes from station inspection logs, pipeline network operation archives, user installation application forms, safety inspection reports, and similar sources. Data update rhythm follows these rules: routine inspection records are updated daily, user installation work orders are updated in real time when applications are submitted, and annual operation archives are synced and archived quarterly. Document structure includes structured fields and attachment content. Structured fields include pipeline network ID, operating pressure, inspection time, user address, and similar items. Units include megapascals, cubic meters, hours, and similar units. Attachments are mostly on-site photos or scanned paper documents.
What constraints these characteristics impose on model integration and configuration
Data includes long-text operation reports and real-time parameters with units, which occupy large model context windows. Total length of input content must be limited to avoid exceeding the model's supported upper limit. Some data is updated in real time with work orders. When calling the model, latest work order data must be associated. A reasonable context refresh mechanism must be configured to ensure generated documents use the latest business data. Gas-related documents have fixed standard formats and dedicated fields. Model output must strictly match preset field requirements. Generation constraint parameters of the model must be adjusted to avoid non-compliant content generation.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | A single urban gas operation document often reaches thousands of characters. This range covers at least 3 recent inspection reports and 1 core work order data set |
maxTokens | 4096 | Document drafting requires complete standard documents to be output, avoiding truncation of critical business parameters and formatted content |
quoteMaxToken | 2000 characters | Core business fields such as pipeline network pressure and user address need to be referenced. Controlling the reference content length prevents interference with main text generation |
temperature | 0.3–0.5 | Gas documents require rigor and consistency, avoiding generation of format deviations or non-compliant content |
AIPROXY_API_ENDPOINT | Official access address provided by the large model service provider | Ensures stability and correctness of the model call link |
AIPROXY_API_TOKEN | Exclusive key generated by the service provider | Ensures identity legitimacy of model calls and prevents unauthorized access |
Three Common Errors
- Phenomenon: After calling the large model, no corresponding call log appears in the platform backend, or a "request timed out" error is returned. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, causing requests to fail to reach the target model service.
Reference only; verify against the target FastGPT release and deployment documentation.
- Phenomenon: When calling an application released by FastGPT, requests from pad devices return empty results or have insufficient associated data. Cause:
toolChoiceis not explicitly set toauto, preventing the model from triggering knowledge base recall of associated gas work orders and inspection data.
How to Confirm Proper Configuration
- Submit a request to generate a notice based on a gas inspection report to the FastGPT backend, and check whether the model call log contains complete input context and return results.
- Manually trigger a document generation task, and verify whether the output content includes preset dedicated gas fields and standard formats.
- On the model management page of FastGPT v4.17.0, test calling the Qwen3 model to generate a gas installation document, and confirm that no format errors occur.
- View the platform's call statistics panel to confirm that the actual usage values of the
maxContextandmaxTokensparameters match the configured values.
The values given 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.