What the data for this category looks like
Data for refractory material research reports comes primarily from the official monitoring platform of the China Refractory Materials Industry Association, professional refractory material technical journals, securities firm building material industry special research reports, public technical documents from upstream and downstream production enterprises, and research reports released at industry exhibitions. Data update cycles fall into three categories:
- Monthly industry operation monitoring data is updated monthly
- Annual industrial development research reports are updated annually
- Enterprise customized technical documents are updated irregularly alongside product formula iterations
Each single document typically includes four core modules: raw material ratio parameters, preparation process parameters, performance test results, and application scenario matching analysis. Fields include physical performance indicators such as refractoriness (unit: ℃), bulk density (unit: g/cm³), and thermal conductivity (unit: W/(m·K)). There is no unified format template for these documents.
What constraints do these characteristics impose on workflow orchestration?
The scattered and unformatted nature of refractory material research report data requires pre-configuring multi-source data access nodes in the workflow. These nodes adapt to formats from association monitoring data, securities firm special reports, enterprise technical documents, journal papers, and other sources. The varying update cycles of different data sources require setting layered synchronization rules. Configure scheduled trigger tasks for monthly industry data, and event-triggered synchronization for real-time updated enterprise documents. Long single documents and inconsistent naming of professional fields require adding standardized field mapping nodes in the workflow. These nodes unify fields such as "refractory temperature" and "high-temperature strength" from different sources into standard fields. Also split documents by chapter instead of using fixed character length as the splitting basis. This avoids splitting professional test data paragraphs.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Refractory material research reports include multiple sets of professional test data, so parsing takes a long time. 600 seconds covers the complete parsing process |
maxContext | 8000–12000 characters | The core test data paragraphs of a single research report are long. Sufficient context must be retained for the model to accurately match professional parameters |
recall count | Top 8–12 entries | Refractory material research reports have a large number of professional data entries. A sufficient number of relevant fragments must be recalled to cover different test conditions |
similarity threshold | 0.72–0.78 | Semantic matching accuracy for professional terms is high. A threshold that is too low will introduce irrelevant data, while a threshold that is too high will miss relevant research report fragments |
LLM_TEMPERATURE | 0.1–0.3 | Research report retrieval and Q&A requires maintaining answer accuracy. A lower temperature reduces redundant content generated by the model |
AUTO_FETCH_KNOWLEDGE_BASE_ID | Bind to the knowledge base associated with the workflow | Avoid configuration errors caused by manually entering the knowledge base ID, and adapt to knowledge base switching requirements across scenarios |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After modifying a global variable in the workflow, subsequent nodes read an empty variable value. Debug logs show the input field is empty. Cause: The conversation-level persistence switch for global variables was not enabled. Variable modifications within a single conversation are not retained by the system.
- Phenomenon: When calling a large model using variable reference in the AI chat component, the temperature parameter setting option does not appear in the interface. Cause: The advanced configuration panel was not expanded. The temperature parameter is hidden in the collapsed advanced settings area by default.
- Phenomenon: Automatic retrieval of the knowledge base ID fails. The workflow node throws an error stating "no matching knowledge base found". Cause: The workflow is not bound to the target knowledge base. The automatic retrieval logic cannot identify the associated knowledge base ID.
How to confirm the configuration is complete
- Run a single test workflow, check the logs of the file parsing node, and confirm that the parsing time does not exceed the configured timeout period and there are no format errors.
- Call the AI chat component, enter a professional question, and verify that the research report fields referenced in the returned results match the configured recall count and similarity threshold.
- Manually modify the global variable value, switch to the next workflow node, and check the variable debugging panel to confirm that the variable value has been correctly updated and read.
- Trigger the knowledge base automatic synchronization task, check the update logs in the knowledge base backend, and confirm that the research report data has completed synchronization according to the configured trigger rules.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.