Reference only; verify against the target FastGPT release and deployment documentation.
What Data for This Category Looks Like
The internal business data of refined oil sales comes from four main sources: gas station POS terminals, enterprise ERP inventory modules, employee scheduling and attendance systems, and fuel card management systems. Data updates follow three rhythms: single transactions sync to POS systems in real time, inventory and scheduling data updates daily, and payroll calculation data updates monthly. Most data uses structured fields, including transaction serial numbers, fuel grades, fuel volume (liters), employee IDs, clock-in times, inventory balances (cubic meters), and more. Each field uses a dedicated business unit, and no redundant unstructured content exists.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The high-frequency updates of real-time transaction and inventory data require multi-turn dialogue to pull the latest business data each time it is triggered, to avoid outputting outdated information. Structured fields and dedicated business units require prompts to clearly specify field extraction rules and unit annotations, to prevent the AI from confusing data meanings. Data permissions are divided by station and employee hierarchy. Multi-turn dialogue must isolate different users’ contexts based on session identifiers, to prevent unauthorized access. Employees frequently use industry-specific terms. Prompts must preset term mapping rules to ensure semantic coherence in multi-turn dialogue, and avoid response deviations caused by term ambiguity.
Configuration Recommendations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Generate a unique identifier using station + employee ID | Ensure session data isolation for different employees, and comply with internal business permission control requirements |
messages | Retain the last 10 conversation records | Balance context coherence and response latency, and adapt to the short-session office scenario of refined oil sales employees |
variables | Bind real-time inventory of the current station, total daily transaction volume, and the employee’s daily schedule | Provide real-time business data support for multi-turn dialogue, and improve response accuracy |
| Maximum context length | 800–1200 characters | Avoid response timeout caused by loading redundant historical data, and adapt to the lightweight dialogue needs of internal office |
| Session persistence rule | Bind conversation records by user ID | Ensure that different employees can only view their own conversation history, and comply with data security specifications |
| Term library update frequency | Sync once daily | Adapt to the exclusive business terms of refined oil sales, and improve the accuracy of dialogue understanding |
Three Common Configuration Mistakes
- Phenomenon: The inventory data output by the AI in multi-turn dialogue does not match the real-time inventory of the current station. Cause: The bound
variablesbusiness data is not pulled in real time each time the dialogue is triggered, and only static data from initialization is used. - Phenomenon: The conversation record ID of the previous AI reply cannot be obtained. Cause: The unique identifier generation rule for
chatIdis not configured correctly, orchatIdis not passed into the dialogue interface parameters, resulting in the inability to trace the session context. - Phenomenon: The number of results returned when calling the "Get Conversation Record List" interface does not match expectations. Cause: The role of the offset parameter is not correctly understood, and the offset is incorrectly set, causing session records to be skipped or loaded repeatedly.
How to Verify Successful Configuration
- Initiate a conversation to query employee schedules, verify that the schedule data returned by the AI matches the real-time schedule of the current station, and confirm that the business variable binding configuration takes effect.
- Switch to different employee accounts to initiate conversations, verify that each account can only view its own historical conversation records, and confirm that the session isolation configuration of
chatIdtakes effect. - Call the "Get Conversation Record List" interface, adjust the offset parameter, verify that the number of returned session records meets expectations, and confirm that the offset parameter configuration is correct.
- Initiate multiple consecutive related conversations, verify that the AI can correctly associate contexts and call the latest business data, and confirm that the context retention configuration of
messagestakes effect.
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.