What the data for this category looks like
Official documents including refined petroleum product circulation management measures and retail operation standards, issued by the National Energy Administration, the Ministry of Commerce, and local commercial and market supervision departments, are the primary source of refined petroleum product sales policy data. The data update rhythm adjusts irregularly alongside policy revisions. A single policy document ranges from hundreds to thousands of characters in length. All documents include fixed fields such as release document number, effective date, applicable region, and core clauses. Core field units include measurement and management units directly tied to business scenarios, such as liters, tons, yuan per liter, square meters, and others.
Constraints on Model Connection and Configuration
Irregular policy document updates require the knowledge base to support on-demand incremental updates. This prevents outdated policies from being returned in search results. Significant variation in policy document length requires adjustment of context length parameters to fully load individual policy clauses. Documents include multiple measurement and management units. The model must accurately distinguish applicable scenarios for similar units during retrieval and generation, to avoid answers with unit confusion. Document numbers and effective dates act as core identifiers. These must be prioritized when matching against policy numbers or time ranges mentioned in user queries during retrieval.
How to Set Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average length of single policy documents, to avoid truncating core clauses |
maxTokens | 2000–3000 token | Controls the output length of a single response, meeting the conciseness requirements of policy interpretation |
quoteMaxToken | 1500–2000 token | Limits the total length of quoted policy text, ensuring quoted content is highly relevant to the query |
temperature | 0.1–0.3 | Reduces randomness in responses, ensuring accuracy and rigor in policy interpretation |
toolChoice | auto | Allows the model to independently determine whether to call retrieval tools to obtain policy basis |
vision | false | Most policy documents are in plain text format, so image understanding functionality does not need to be enabled |
Three Common Configuration Errors
- Retrieval results only return a small number of policy clauses, failing to cover all required basis for the user's query. Cause: The
maxContextparameter is set too low, preventing complete loading of policy document fragments, or thequoteMaxTokenlimit is too strict, filtering out relevant content. - Image understanding model configuration options do not appear in the interface. Cause: Corresponding function permissions are not enabled, and the currently bound policy documents have no image resources. The
visionfunction is disabled by default in this scenario. - The model directly refuses to answer policy-related questions outside the knowledge base. Cause:
toolChoiceis forcibly set tonone, prohibiting the model from calling retrieval tools. This makes it impossible to obtain unrecorded policy content.
How to Verify Successful Configuration
- Upload a local refined petroleum product policy document, initiate a query that includes the document number and effective date, and check whether the returned results accurately reference the corresponding clauses.
- Adjust the value range of the
temperatureparameter, observe changes in the rigor of the model's responses, and confirm that the parameter has taken effect. - Test policy documents of different lengths, confirm that responses do not have content truncation, and verify the adaptability of the
maxContextparameter. - Trigger a knowledge base incremental update, confirm that the system can normally synchronize newly released policy documents, and verify the effectiveness of the update configuration.
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.