What Data for This Category Looks Like
Data originates primarily from gas station POS systems, oil product inventory and sales management platforms, contract approval modules, and compliance quality inspection systems. Update frequencies fall into three categories: real-time (single sales record), daily (same-day sales summary), and monthly (settlement and inventory check reports). Document structures include structured forms (such as sales orders containing oil product number, sales volume, unit price, and settlement method), template documents (such as purchase and sales contracts and compliance notices), and semi-structured reports. Field units include liters, tons, yuan per liter, contract numbers, quality inspection report numbers, and others. Some documents require association with oil product models and customer main body information.
Constraints Imposed on Model Access and Configuration
Structured forms and semi-structured reports have high field density. Model access must adapt to multi-field associated context input to avoid truncating key business parameters. The difference in update frequencies between real-time sales data and monthly reports requires clear configuration of the vector database synchronization cycle to match business data update frequencies. Format differences across document types (sales orders, contracts, compliance documents) require distinguishing call scenarios in model configuration. This ensures generated content complies with the typesetting and clause requirements of corresponding documents. Special oil product-related units (liters, tons, yuan per liter) and numbering rules require specifying in configuration that the model must strictly follow business field unit specifications. This prevents incorrect measurement descriptions.
How to Set Configurations
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Refined oil sales documents often include multi-field sales ledgers and complete contract clauses, requiring sufficient context to associate business data |
maxTokens | 2000–3000 token | The output length for drafting a single document usually covers complete clauses and business parameters; this range balances generation efficiency and content completeness |
quoteMaxToken | 1500 token | Referenced sales data and contract template fragments usually do not exceed this length, avoiding redundant references from occupying context space |
temperature | 0.3–0.5 | Document drafting requires consistency in format and business logic; a lower temperature value reduces random deviations |
AIPROXY_API_ENDPOINT | Fill in according to the deployed large model service address | Must match the external access address of the accessed large model service to ensure correct request routing |
AIPROXY_API_TOKEN | Fill in according to the authentication token generated by the service | Used to verify call permissions and ensure interface access security |
Three Common Configuration Errors
- Abnormal return volume after vector model access, with only 1 result or no results. Cause: The
quoteMaxTokenvalue range was not configured correctly, causing referenced business data to be truncated and unable to match. - Long-term GPU usage below 30% after deployment, with single-core CPU occupancy reaching 100%. Cause: The
maxContextparameter was not adjusted based on business data context length, causing model computing load to concentrate on CPU for long text processing. - Incorrect measurement units in generated documents, such as using "tons" instead of "liters". Cause: Business field unit rules were not specified in configuration, so the model did not recognize specialized measurement term specifications.
How to Verify Proper Configuration
- Upload a single refined oil sales contract template and the same-day sales ledger, trigger a drafting test, and verify that generated content includes all required business fields.
- View the model service monitoring panel to confirm GPU load and CPU occupancy are within a reasonable range, with no single-core full load.
- Input a query containing specialized measurement terms, and verify that generated content strictly follows business unit specifications.
- Test calls for different document types (sales orders, compliance notices) to confirm generated format matches corresponding document typesetting requirements.
The values provided here are common starting points, and should be evaluated 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.