What the data for this category looks like
Ledger data for refinery and chemical production mainly comes from distributed control systems (DCS), tank farm metering systems, and laboratory information management systems (LIMS). Data update rhythms fall into two categories: process parameters collected by DCS are updated in real time, while tank farm metering and quality inspection data are updated per shift or daily. A single ledger document includes fields such as plant code, timestamp, material name, flow rate, temperature, pressure, quality inspection pass rate, and energy consumption value. Units include m³/h, ℃, MPa, kg standard coal and other professional petrochemical measurement units. A single shift ledger document usually contains dozens to hundreds of detailed entries.
Constraints imposed on multi-turn dialogue and prompt engineering
Decentralized data sources require multi-turn dialogue to confirm data scopes from different systems in sequence, to avoid mixing data across plants. Mixed real-time and batch update rhythms require prompts to distinguish between real-time process queries and historical shift summary tasks. Fields with multiple professional units need preset mapping rules to prevent unit conversion errors in the model. Long detailed entries require multi-turn dialogue to limit context length, to avoid exceeding the model's processing limits.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum context per single dialogue | First 20 historical messages | Refinery and chemical ledgers have many detailed entries. Long context easily causes model confusion. Limiting the number of messages avoids context overflow |
variables preset variables | Plant code list, shift time range, quality inspection indicator list | Aligns with ledger-specific fields, allowing prompts to accurately call corresponding business data |
chatId binding rule | Bind unique chatId according to the user's affiliated shift | Ensures session permissions are bound to the business shift, achieving data isolation |
| Quick reply configuration | Preset 8 buttons including "Summarize daily ledger", "Export pass rate report" | Adapts to the frequent operation needs of on-site engineers, reducing repeated input |
| File upload interface restrictions | Single file maximum 1000 MB, only supports CSV format | Adapts to the format and volume requirements for batch import of refinery and chemical ledgers |
| Historical dialogue storage duration | 30 days | Aligns with the business cycle of shift handover in refinery and chemical enterprises |
Three common configuration errors
- Preset quick button clicks have no response. Background logs show the
messagesarray is empty. The cause is that the preset message template for the quick button is not bound, causing conversation messages to fail to load correctly into the conversation context. - Calling the dialogue API to upload ledger files returns status code 413. The cause is that single file upload volume is not restricted, exceeding the default file size threshold of FastGPT v4.17.0.
- Cross-shift accounts can view ledger data from other shifts. The cause is that users' affiliated shifts are not bound via
chatId, resulting in ineffective isolation of session permissions.
How to verify successful configuration
- Initiate a conversation, click a preset quick button, and check that the corresponding question is automatically filled in the conversation input box. Send the message, and confirm that the ledger data for the corresponding plant is retrieved correctly.
- Upload a single 1200 MB ledger CSV file, and confirm that the interface returns a success status and the file can be parsed normally.
- Use two accounts from different shifts to initiate conversations, and confirm that each account can only view historical conversation records for its own shift.
- Initiate 25 consecutive conversations, and confirm that the model’s responses do not show logical confusion caused by context overflow.
The values provided here are common starting points, and should be evaluated 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.