What the Data for This Category Looks Like
The data for the basic chemical continuous production category comes primarily from DCS/SIS systems, online analyzers, LIMS systems, equipment condition monitoring modules, energy metering devices, and raw material tank data collection nodes. Data update frequencies fall into three categories: real-time, per-shift, and daily. Regular associated documents include operating procedures, process index cards, DCS trend charts, laboratory reports, interlock bypass records, special work permits, and shift logs. Data fields include process parameters such as pressure in MPa, flow rate in m³/h, laboratory indicator concentrations, work approval status, and shift production tonnage. Most data formats are a mix of structured time-series data and semi-structured documents.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Multi-source heterogeneous mixed data formats require prompts to clearly distinguish processing logic for structured time-series parameters and semi-structured documents, to avoid confusion of field units. Real-time updated DCS data requires reasonable limitation of historical dialogue turns, to prevent context overload that reduces response speed. Per-shift and daily updated shift logs and work permit data require multi-turn dialogue to support dynamic recall of knowledge base documents, while also passing business variables to adapt to multi-device and multi-shift scenarios. Production scenarios with strong interlocks and hazardous media require prompts to forcibly filter irrelevant content, ensuring output only relates to compliance verification and summarization of the current production ledger.
Configuration Settings
This guide is compatible with FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind to the unique identifier of the business system user | Isolate multi-user chat records and avoid confusion of session data |
messages | Retain the first 10 rounds of dialogue + structured data from the last 24 hours | Match the real-time and shift-based data update rhythm of basic chemical continuous production, and avoid context overload |
variables | Pass current shift, device number, and raw material type | Adapt to multi-device and multi-shift ledger summarization scenarios, and accurately match production data |
| Single document recall limit | Top 6 entries | Correspond to the common number of daily associated documents, and avoid information overload interfering with summarization logic |
| Dynamic prompt loading | Read from a specified classification directory in the knowledge base | Support quick switching of ledger summarization prompts for different devices, and adapt to multi-production line scenarios |
Three Common Misconfigurations
- A new conversation opens with a version update prompt. The cause is failing to disable non-essential version update pop-ups in global configuration, which interrupts the dialogue flow in production scenarios.
- After connecting the business system to the API, user chat records cannot be distinguished. The cause is failing to bind
chatIdto the unique identifier of the business system user, resulting in shared session data across different users. - The unit of ledger data fields returned by dialogue is inconsistent. The cause is failing to explicitly specify the unit mapping rules for structured data in the prompt, leading to confusion between pressure and flow parameters from DCS systems.
How to Verify Successful Configuration
- Initiate a new conversation, check if a version update prompt appears, and confirm the pop-up has been disabled.
- Use two different business user IDs bound to
chatIdto initiate conversations, and check that their chat records do not interfere with each other. - Call the prompt interface, pass different
variablesparameters, and check that the returned ledger summarization content matches the corresponding shift and device information. - Upload a test prompt to the specified directory in the knowledge base, initiate a conversation, and confirm that the prompt content is loaded correctly.
The values provided here are common starting points and should be measured against the reader's own samples.