What this category of data looks like
The data for refined oil sales declarations and file review mainly comes from gas station inventory and sales systems, retail ledgers, tax declaration forms, and on-site law enforcement file archiving systems. Data updates are triggered on a fixed schedule aligned with monthly and quarterly declaration cycles. A single declaration package contains two types of content: structured forms and unstructured attachments.
Structured fields include gas station number, fuel grade, actual sales volume, declared sales amount, tax payable, and similar items, with units corresponding to liters and yuan. Unstructured attachments are mostly invoice scans, on-site inspection records, rectification notices, and similar materials. A single attachment can reach several thousand characters in length.
What constraints these characteristics impose on model integration and configuration
Fixed-cycle updated data is suitable for incremental indexing logic, which reduces resource consumption from full synchronization operations. Structured fields have clear enumerated values — for example, fuel grades only support 92#, 95#, and 98# — which requires model outputs to strictly match preset rules to avoid custom deviations.
The need for long-text attachments and multi-field associated verification means the model must retain sufficient context-related information, while also controlling the total token count of a single output. The requirement to process multi-modal attachments means configuring model capabilities that support visual parsing.
How to configure settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Refined oil declaration packages usually include multiple inventory and sales details and file attachments, requiring sufficient context for associated verification logic |
maxTokens | 2000–3000 token | A single review result needs to output verification items, anomaly prompts, and rectification suggestions to avoid truncating critical information |
temperature | 0.1–0.3 | Reviews require strict matching of declaration rules and data consistency, and excessive randomness should be avoided |
toolChoice | auto | Automatically select structured field verification or unstructured text parsing tools based on input content |
AIPROXY_API_ENDPOINT | Business-specific proxy address | Isolate general large model calls and refined oil review business traffic to ensure sensitive data security |
AIPROXY_API_TOKEN | Exclusive token generated by the proxy platform | Verify call permissions and prevent unauthorized access to business data |
Three common misconfigurations
- An "invalid token" prompt appears in the interface, and a 401 status code is returned when calling the model. The cause is that extra line breaks or spaces are included in the
AIPROXY_API_TOKENconfiguration, causing token verification to fail. - The review result for long file attachments is truncated, showing only the first half of the content. The cause is that the value of
maxTokensis lower than the total token amount required for the output, and does not adapt to the output needs of long-text review. - The model incorrectly identifies 92# fuel as 95#, leading to deviations in verification results. The cause is that the value of
temperatureis too high, causing the model to generate outputs that do not comply with preset enumerated rules.
How to verify proper configuration
- Submit a single standard refined oil sales declaration file, and check whether the model's output verification items cover all required fields.
- View the model call logs in FastGPT v4.17.0, and confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfiguration parameters match the actual call address and token. - Adjust
temperatureto around 0.2, submit multiple test data sets containing enumerated fields, and confirm that the fuel grade output by the model matches the input enumerated values. - Upload a single inspection record attachment with a length of approximately 5000 characters, and confirm that the model's output is not truncated.
The values provided here 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.