Data Characteristics of This Category
Data sources for special steel research reports include industry statistical reports released by a national special steel industry association, production and sales briefings from key special steel manufacturers, and spot price quotation data from third-party bulk commodity information platforms. Update cycles focus on monthly core production and sales data updates, with weekly spot price data synchronized. Most documents are in PDF or Word format with structured tables. They contain fields such as grade classification, output, ton price, downstream application categories, and import and export volume. The unit for price fields is yuan/ton. The unit for output fields is ton or ten thousand tons. There is no unified fixed typesetting format.
Constraints for Workflow Orchestration
The multi-source and multi-format nature of special steel research reports requires workflows to support multi-data source access and format verification. This avoids parsing errors caused by differences in document typesetting. Differences in update cycles across data sources require workflows to use differentiated scheduled trigger rules. These rules adapt to the synchronization rhythms of weekly spot data and monthly production and sales data respectively. The large number of special steel sub-categories and clear field units require workflows to add precise filtering parameters such as grades and application scenarios. They also require unit verification logic to prevent deviations from cross-unit calculations. The high proportion of structured tables requires the RAG link of the workflow to prioritize extracting structured data from tables. This improves retrieval accuracy.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
rag_recall_top_k | Top 10 entries | There are many sub-categories in special steel research reports. Excessive recall increases context length, while insufficient recall misses key sub-category data |
rag_chunk_size | 800–1200 characters | Special steel research reports contain a large number of structured table paragraphs. This length fully retains the core data of a single table |
workflow_trigger_cron | 0 0 2 * * 1,5 (weekly), 0 0 4 * * 1 (monthly) | Adapts to the update rhythms of weekly spot data and monthly production and sales data for special steel |
mcp_param_auto_fill | Enabled | Special steel research report retrieval requires supplementing multiple parameters such as grades and application scenarios. Automatic filling reduces manual input errors |
parse_table_enable | Enabled | Special steel research reports contain a large number of structured production and sales and price tables. Enabling this option accurately extracts field data |
rag_similarity_threshold | 0.75–0.85 | Special steel data has high granularity. A higher similarity threshold filters out irrelevant general steel research report content |
The parameter values provided on this page are conventional recommendations used as a starting point 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 Configuration Mistakes
- Phenomenon: An error of missing parameters is triggered when calling the MCP node, and parameters cannot be automatically collected and supplemented through the interactive link in the workflow. Cause: The
mcp_param_auto_fillconfiguration is not enabled, or the parameter collection branch is not added in the workflow. - Phenomenon: The same input returns correct results in local workflow debugging, but the actual call results after online release have large deviations. Cause: Configuration values such as
rag_recall_top_kandrag_similarity_thresholdin the online environment are inconsistent with local debugging parameters, or the knowledge base has not completed full synchronization. - Phenomenon: The ton price data of a specified grade cannot be accurately extracted from the special steel research reports in the knowledge base, and a large amount of irrelevant text is included in the returned results. Cause: The
parse_table_enableconfiguration is not enabled, or the retrieval prompt does not clearly specify the extraction fields and units.
How to Verify Proper Configuration
- Trigger the scheduled task of the workflow, and check whether the nodes of the corresponding data sources are called according to the preset cycle in the task log.
- Enter special steel grades and application scenario keywords, and verify whether the retrieval results only contain matching special steel research report content.
- Call the MCP node, and verify whether the parameter collection interface pops up automatically and the supplemented parameters are correctly passed.
- Check the knowledge base parsing records, and confirm that the structured tables in the special steel research reports have been correctly extracted as retrievable fields.
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.