What the data for this category looks like
The data for coal transportation and sales declaration materials and case review mainly comes from purchase and sales contracts, weighing slips, road and railway transportation documents, value-added tax special invoices, and approval circulation records. Data is generated and updated upon completion of each transportation and sales transaction. A single case typically includes 3 to 5 documents of different types. The overall structure is primarily structured tables, with supplementary free text descriptions. Core fields include coal type name, shipment tonnage, transportation mileage, invoiced tax-included amount, and approval node status. Units include standard measurements such as tons, kilometers, and yuan. Some documents use custom field formats.
Constraints Imposed on Model Access and Configuration
The multi-document combination feature of coal transportation and sales declaration cases requires the model access link to support long-context processing, and prevent key information from being truncated. The high share of structured fields requires configurations to balance text extraction accuracy and format compliance. A single review batch may involve parallel processing of multiple cases, so concurrency-related settings must be adjusted to handle business peaks. Some documents use custom formats, so parameter adjustment space must be reserved to support recognition of non-standardized fields. The stability of domestic model call links must also be ensured to avoid cross-border delays that reduce review efficiency.
How to Set Configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Coal transportation and sales declaration cases usually include multiple related documents, with a total long context length that needs to cover all key review information |
maxTokens | 2000–3000 token | A single review result needs to include compliance judgments, rectification suggestions and other content. Control the generation length to ensure readability |
quoteMaxToken | 1500–2000 token | The review process needs to reference key fields of original documents as a basis, retain sufficient original fragments to support judgments |
temperature | 0.1–0.3 | Review scenarios require stable judgment results, avoid deviations in review conclusions caused by randomness |
toolChoice | auto | Allow the model to independently choose whether to call structured extraction tools, adapt to different types of case content |
AIPROXY_API_ENDPOINT | Fill in the dedicated endpoint for the deployed domestic large model | Adapt to the domestic model call link, avoid cross-border delays affecting review speed |
Three Common Misconfigurations
- Phenomenon: Timeout errors occur when a deployed vLLM instance processes batch review requests. Cause: Concurrency settings for model access are not adjusted based on concurrent business volume, or the
maxContextvalue is set too large, leading to excessive model loading delay. - Phenomenon: Missing spaces in review SQL statements generated by the model, causing execution failures. Cause: The
temperatureparameter is set too high, resulting in non-standard code formatting and missing required delimiter characters. - Phenomenon: Interface authentication failures occur when calling domestic models. Cause:
AIPROXY_API_ENDPOINTis incorrectly configured to point to external service providers, rather than using the dedicated endpoint for domestic models.
How to Verify Successful Configuration
- Upload a complete coal transportation and sales declaration case, and verify that the model's review results accurately extract core fields such as coal type, tonnage, and amount.
- Send batch test requests for 3 to 5 cases, check whether interface return delays meet business expectations, and confirm that concurrent processing works correctly.
- Upload the same standard case multiple times, confirm that review results remain consistent, and validate the stability configuration of the
temperatureparameter. - Review the FastGPT v4.17.0 interface call logs, confirm that the requested
AIPROXY_API_ENDPOINTand identity verification details are configured correctly, with no abnormal 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.