What the data for this category looks like
Service consultation data for refined oil product sales mainly comes from gas station POS checkout systems, inventory management systems, member service systems, and official oil price announcement platforms. Data update rhythms are divided into real-time and scheduled categories: oil prices and member consumption records are updated in real time, while inventory ledgers and business process documents are updated daily. The document structure includes daily retail price benchmarks, inventory tonnage ledgers, member card application and recharge rules, invoicing guidelines, complaint handling processes, and more. Fields include clear units: for example, oil price is measured in yuan per liter, inventory is measured in tons, and business nodes are assigned unique identification codes.
What constraints these characteristics impose on model access and configuration
Real-time updated oil price and member data require the model context window to be limited to the most recent 2 hours, to avoid generating replies using expired data. For daily updated inventory and process documents, a daily automatic synchronization trigger node must be set during configuration to keep knowledge base content consistent with business systems. Fields with clear units require the model to match the corresponding units when generating replies; during configuration, unit verification rules must be clearly specified in the prompt. Multi-branch business document structures require the model to accurately recall knowledge base content for the corresponding business node during invocation, to avoid cross-branch confusion.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 7200 seconds | Adapts to real-time updated oil price and member data, ensuring the context only contains the latest business information from the most recent 2 hours |
maxTokens | 8000 token | Covers complete knowledge base content for daily oil prices, inventory ledgers, and common service processes |
quoteMaxToken | 1500 token | Limits the length of a single recalled document to avoid exceeding the model context window limit |
toolChoice | auto | Adapts to scenarios where different business data sources may need to be called during service consultation |
temperature | 0.2–0.4 | Ensures replies are rigorous and accurate, avoiding content that violates business rules |
AIPROXY_API_ENDPOINT | Fill in according to the enterprise's internal API address | Connects to internal refined oil product business data interfaces |
AIPROXY_API_TOKEN | Fill in according to the key allocated by the enterprise | Ensures the security of interface calls |
Three common mistakes
- Phenomenon: Body-related parameters are deleted when editing model configuration. After submitting and saving, reopening the configuration page shows the parameters as retained. Cause: The configuration save submission process was not completed correctly, or browser cache retains old configuration data.
- Phenomenon: The model request node returns a 500 status code or a "model call failed" prompt, with no specific error details. Cause: No error capture link was configured, and model call error information was not synchronized to the business monitoring system.
- Phenomenon: Adding the same base model from multiple vendors prevents configuration completion, with an ID duplicate prompt displayed. Cause: No unique identification suffix was configured for models from different vendors, causing FastGPT to fail to distinguish models with the same name.
How to confirm the configuration is complete
- Initiate a test request, enter the service consultation question "What is today's No. 92 gasoline retail price", and verify that the returned result includes the correct unit and latest business data.
- Simulate a model call failure scenario, and verify that the corresponding error information can be captured and a prompt compliant with business rules is generated.
- Configure two models of the same type from different vendors, and verify that the system does not prompt for duplicate IDs and can switch calls normally.
- Adjust the value range of the
maxContextparameter, and verify that the update frequency of context data meets business requirements.
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.