What the data for this category looks like
Production data for cement clinker and grinding comes from online composition analysis, DCS systems, laboratories, kiln and grinding equipment, raw and fuel ledgers, and energy metering devices. Data update cycles fall into three categories: real-time, shift-based, and daily. Daily generated documents include raw material batching plans, kiln operation logs, laboratory reports, quality control notices, equipment inspection records, and energy reports. Document fields cover raw material batching parameters, clinker free calcium oxide content, grinding specific surface area, equipment operating energy consumption, and other relevant metrics. Corresponding units include %, m²/kg, kWh/t, and similar units.
Constraints Imposed by These Characteristics on the Multi-Turn Dialogues and Prompt Engineering Link
Data sources for this category are scattered, and update cycles vary significantly. Multi-turn dialogues must match update frequencies by data type. This avoids calling expired inspection records or outdated batching plans. Multiple document types have dedicated field and unit specifications. Prompt engineering must explicitly specify the target document type and corresponding fields during retrieval. This prevents confusion between different parameter meanings. Core review workflows include batching adjustments amid raw and fuel fluctuations, and stable thermal control of rotary kilns. These require retaining historical parameter context from multi-turn interactions. This ensures each query can link to earlier production data, and avoids repeated input of duplicate information.
Configuration Settings
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Session Identifier | Bind chatId | Distinguish review sessions for different shifts and equipment, avoid context confusion |
| Dialogue Context Retention | Retain the most recent 15 turns of interaction content in messages | Cement production parameters are closely correlated, so recent historical dialogue content for batching and kiln temperature data must be retained |
| Variable Injection | Bind real-time data fields in variables | Directly call real-time parameters from online composition analysis and DCS systems into dialogue context |
| Single Retrieval Limit | Top 5 matching documents | Cover multiple production document types including raw material batching plans and laboratory reports |
| Single Document Truncation | 800–1200 characters | Adapt to the length of various production documents, control model input load |
| Question Optimization Trigger | Calibrate based on actual measurements | Adapt to scenarios such as batching adjustments amid raw and fuel fluctuations, and kiln thermal system optimization |
Three Common Misconfigurations
- Symptom: Workflow output includes text wrapped in uncleaned thinking tags. Cause: The
messagesfield from model outputs was not fully stripped of tags in the workflow's code run node, and processing was only completed in the debug environment. - Symptom: The number of documents returned by retrieval does not match the configured limit, or returned content exceeds the expected length. Cause: Truncation rules were not set based on the category's document length, leading to loss of critical parameters after long documents are truncated.
- Symptom: Context becomes disorganized after multiple turns of dialogue, with review data from different shifts interfering with one another. Cause:
chatIdwas not bound as the session identifier, leading to confusion between the context of different sessions.
How to Confirm the Configuration Is Properly Set
- Initiate multiple rounds of queries targeting raw material batching plans, check whether the dialogue context retains the most recent 15 turns of interaction content.
- Verify that the session's
chatIdcorresponds one-to-one with the current shift and equipment under review, to avoid cross-session data confusion. - Trigger the question optimization function, confirm that it can automatically adjust queries to adapt to scenarios such as batching adjustments amid raw and fuel fluctuations.
- Check that documents returned by retrieval are truncated to 800–1200 characters as configured, with no overflow of overly long content.
The values provided here are common starting points, and should be evaluated against the reader's own samples.