What This Category of Data Looks Like
Data sources for this category include online composition analysis systems, DCS distributed control systems, laboratory test data, kiln and grinding equipment operation logs, raw material and fuel ledgers, and energy metering reports. Data update frequencies cover real-time (such as kiln temperature and grinding sound data), per shift (such as patrol records), and daily (such as quality control notices). Daily documents include raw material batching plans, kiln operation records, laboratory reports, and equipment patrol ledgers. Fields involved cover material ratios, thermal parameters, strength indicators, and equipment vibration values. Units include degrees Celsius, tons per hour, megapascals, and others.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Real-time data requires conversation contexts to retain kiln and grinding parameters from the last 15 minutes. This prevents recommendation deviations caused by delayed data. Field differences across multiple data sources require prompts to explicitly specify that only documents from the corresponding department be called. For example, when asking about grinding equipment inspection, only retrieve equipment patrol ledgers. Documents updated per shift or daily must have their update times marked during retrieval. This helps the model match the batching plan for the current production cycle. Long-context multi-turn dialogue requires limiting the length of historical messages. This avoids exceeding the model’s processing limit. For professional indicators such as cement clinker mineral composition and grinding fineness, the units and calculation logic of the fields must be fixed in the prompt.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chatId | Bind the exclusive session ID for the current production shift | Ensure multi-turn dialogue context only associates with inspection and operation records for the corresponding shift, to avoid mixing data across shifts |
messages | Retain the last 10 rounds of historical messages, truncate early conversations exceeding 2000 characters | Adapt to the timeliness of real-time cement production data, avoid model response delays caused by overly long context |
variables | Preset current kiln number, grinding table number, and inspection cycle variables | Allow the model to automatically match the inspection standards and maintenance documents for the corresponding equipment |
| Single search limit | Top 6 retrieval results | Match the volume of daily documents for this category, avoid redundant information interfering with professional judgment |
| Maximum context length | 8000–12000 token | Adapt to professional dialogue needs across multiple links such as raw material batching and thermal control systems, adjust based on actual measurements |
| Question optimization | Enable professional term standardization | Unify expressions for terms related to cement production, avoid ambiguity |
Three Common Misconfiguration Issues
- Symptom: When using a strict Q&A template to retrieve the knowledge base, the system returns "No answer found", even though the knowledge base contains image addresses. Cause: The strict Q&A template only matches knowledge base entries in text format by default, and does not support retrieval and parsing of image-based patrol records or equipment drawings.
- Symptom: When sending multiple questions in a short period, subsequent requests must wait for previous requests to complete. Cause: No session concurrency limit configured for
chatId, leading to blocked request queues for the same session. - Symptom: The API call returns "No permission to operate this conversation record". Cause: The
variablespermission identifier for the corresponding shift is not bound in the request parameters, or the session ID exceeds the authorization scope of the current application.
How to Verify Proper Configuration
- Send a question that includes kiln temperature and grinding table number, confirm that the model’s returned retrieval results only associate with equipment documents corresponding to the currently preset
variablesparameters. - Send three different inspection questions consecutively, confirm that the multi-turn dialogue context retains historical messages and no response delays occur.
- Upload an equipment patrol image to the knowledge base, use the strict Q&A template to ask a question, confirm that the model can correctly parse the patrol standards associated with the image.
- Check the API request logs, confirm that the
chatIdandvariablesparameters are correctly included, and no permission error messages appear.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.