What This Category’s Data Looks Like
Thermal coal data primarily comes from domestic major coal port spot price systems, monthly quality inspection reports from origin mines, and weekly supply and demand monitoring reports from industry associations. Marketing content targeting the financial sector focuses on data such as trade quotes and supply and demand analysis.
Update cycles fall into two categories: spot trading data updates daily, while data related to long-term contracts updates monthly. Document structures typically include fields such as origin identifier, received base low heating value, total sulfur content, ash content, tax-included truck-board price, and delivery location. Common units are kcal/kg and yuan/ton.
The length of single real-time quote documents varies widely, as does the length of industry analysis documents. Calculate or test with own samples to determine appropriate handling.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall
Thermal coal data characteristics impose multiple constraints on the retrieval and recall process for the financial sector.
Daily updated spot data requires matching incremental synchronization frequency for the knowledge base. Otherwise, recalled price data cannot support real-time marketing content for trade clients.
Document lengths vary significantly. Short documents must avoid truncating key professional fields. Long documents require proper segmentation to retain complete semantic logic.
Professional fields have high distinctiveness. Targeted retrieval rules must be configured to avoid confusion with fields from other coal categories.
Marketing and customer acquisition scenarios for the financial sector require linking different data dimensions. Prioritize corresponding fields upfront to ensure recalled content aligns with scenario needs.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | Top 10-15 | Thermal coal professional fields have high distinctiveness. Too many recalled results cause redundant marketing content, while too few fail to cover the multi-dimensional data required for the scenario |
Similarity Threshold | 0.72-0.85 | Thermal coal has many professional terms. A threshold that is too low recalls irrelevant documents, while a threshold that is too high fails to cover relevant sub-scenarios such as quotes from different delivery locations |
Segment Length | 800-1200 characters | Balances the integrity of short documents and semantic coherence of long documents, avoids truncating key fields such as heating value and price |
Knowledge Base Incremental Sync Cycle | Once Daily | Spot trading data updates daily. Sync cycle matches data update rhythm to ensure timeliness of recalled content |
Rerank Return Count | Top 5-8 | Marketing content needs to accurately focus on core data. Reranking retains the most relevant professional information, adapting to content output for customer acquisition scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Industry analysis documents have long lengths, requiring sufficient parsing time to complete field extraction and segmentation |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Analyze specific cases individually, and test with own samples before finalizing.
Three Common Mistakes
- Issue: Output after workflow run includes reference fields such as
Knowledge base searchinput：xxxandKnowledge base searchresponse：xxx. Cause: The toggle to show reference content from retrieval results was not disabled in workflow configuration, so intermediate tool call steps are included in the final output. - Issue: Retrieval results include coal data from non-thermal coal categories. Cause: Targeted retrieval rules for professional fields were not configured, and documents from unrelated categories were not filtered, leading to mixed recall results.
- Issue: Field extraction empty error occurs when parsing long documents. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted to a sufficient duration, leading to field extraction failure due to long document parsing timeout.
How to Verify Proper Configuration
- Upload one thermal coal spot price document and one coal document from another category, run a retrieval test, and confirm that recall results only include content related to thermal coal.
- Trigger a workflow run, check that the final output does not include intermediate input and response fields from knowledge base retrieval.
- Upload a long document, review parsing logs to confirm complete field extraction and no timeout errors.
- Adjust relevant parameters, run multiple rounds of retrieval tests, and confirm that recall results align with the needs of marketing and customer acquisition scenarios.
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.