What the Data for This Category Looks Like
Refined petroleum product sales ledger data primarily comes from gas station POS terminals, oil tank level monitoring devices, and enterprise inventory and sales management systems. Data updates follow two rhythms:
- Transaction data generates in real time with each sale
- Oil tank inventory and inventory-sales data updates after daily stocktaking
A single ledger document includes fields such as product grade, transaction time, sales volume, settlement unit price, actual received amount, remaining oil tank stock, and affiliated station number. Units are uniformly liters, yuan, and yuan per liter. Some cross-station summary documents add regional code and statistical cycle fields.
Constraints for Multi-turn Dialogue and Prompt Engineering
Multi-source data and differentiated update rhythms of refined petroleum product sales ledgers require clear separation of transaction cycles and inventory statistical time ranges in multi-turn dialogue. Prompts must enforce matching rules for filter fields to avoid cross-system data confusion.
The multi-field structure requires gradual guidance of users to clarify filter conditions such as product grade and station number during dialogue. This prevents summary deviations caused by missing fields.
The risk of inconsistent units requires preset unit conversion logic in prompts. This ensures consistent statistical calibers across data from different sources.
The update difference between real-time transaction data and daily inventory data requires dynamic adaptation of dialogue nodes to the time interval for data pulling. This avoids calling outdated inventory information.
Configuration Settings
This configuration uses FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId | Bind enterprise station ID and user employee ID | Enable conversation data isolation across different business scenarios, comply with data permission control requirements for state-owned enterprises and public institutions |
messages | Trim historical messages by conversation stage | Only retain context fragments related to the current ledger query, avoid redundant information interfering with model inference |
variables | Preset product grade, statistical cycle, station number | Standardize required parameters in multi-turn dialogue, reduce operational costs of repeated user input |
| Input length interception | 15000 character threshold | Match the input capacity limit of current mainstream large models, intercept ultra-long requests in advance |
| Context window management | 8000–12000 token | Adapt to the volume of historical transaction data required for refined petroleum product ledger summary, prevent valid information from being truncated |
Three Common Misconfigurations
- Phenomenon: Dialogue nodes return no response or an error prompt reading "Input content is too long". Background logs show request parameters exceed length limits. Cause: No input length interception rule is configured. The context of the user's multi-turn dialogue and current query are directly spliced and submitted to the model.
- Phenomenon: Unable to trace the initiating user and affiliated operation station corresponding to a ledger query request. Cause: The
chatIdis not used to bind the user employee ID and station ID. Conversation data is not permission-isolated, making it impossible to match data access scope. - Phenomenon: AI dialogue nodes cannot parse uploaded oil tank level detection photos, and cannot extract reading data from them. Cause: No file parsing plugin is integrated into the workflow. Supported file formats and parsing logic are not specified in the prompt.
How to Verify Proper Configuration
- Initiate a cross-station refined petroleum product sales volume summary query. Confirm the conversation context only retains relevant content for the current session, with no interference from redundant historical messages.
- Submit a query request exceeding the preset length. Confirm the system intercepts the request in advance and returns a clear prompt message.
- View the conversation log. Confirm each record is bound to the user and station identification corresponding to
chatId, and data access permissions can be traced using the identification. - After configuring prompt variables, initiate a query. Confirm the system automatically fills in preset parameters such as product type and statistical cycle, reducing manual input steps.
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.