What the Data for This Category Looks Like
The data sources for thermal coal marketing content include public industry monitoring platforms, spot trading market quotation systems, production and sales announcements from coal production enterprises, and organized industry information. Update rhythms vary: spot price documents are updated daily, product manual documents are updated quarterly, and industry analysis reports are updated monthly or quarterly. Document fields include origin, calorific value, ash content, volatile matter, total moisture, transaction price, delivery cycle, and applicable downstream scenarios. The corresponding units are MJ/kg for calorific value, g/kg for ash content, volatile matter, and total moisture, CNY/ton for transaction price, and natural days for delivery cycle. No units are specified for origin and applicable downstream scenarios.
Constraints Imposed on Vector Models and Indexing
The multi-source heterogeneous characteristics of thermal coal marketing content create multiple constraints for the vector models and indexing workflow. Multi-source data has widely varying update frequencies: spot price documents update daily, product manuals update quarterly. This requires the index to support incremental synchronization and batch refreshing, eliminating resource consumption from full indexing operations. The content contains a large number of coal-specific professional metrics, so the vector model must adapt to industry terminology encoding to ensure accurate mapping of professional semantics. In scenarios where structured transaction data and unstructured analysis text are mixed, the index must support both vector similarity retrieval and structured field filtering to improve retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | text-embedding-v3 | Adapts to long text and coal industry professional terminology, with coding accuracy meeting the semantic matching requirements of marketing content |
chunk_size | 800–1200 characters | Thermal coal marketing documents contain professional metrics and long sentence descriptions. Segments that are too long will lose semantic connections, while segments that are too short will damage metric integrity |
retrieve_top_k | Top 10–15 results | Relevant matching results for marketing content need to cover supply, demand and quotation information across different dimensions. An excessive number of results will increase retrieval time |
similarity_threshold | 0.75–0.85 | Balances retrieval recall and precision, avoiding irrelevant content from being included in retrieval results |
index_refresh_interval | 2 AM daily | Matches the daily update rhythm of thermal coal spot prices. Incremental refresh mode reduces system resource usage |
rerank_top_k | Top 5–8 results | Reduces the calculation load of the reranking step, alleviating retrieval response latency |
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. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: After configuring
embedding_modelastext-embedding-v3and entering the API key, the interface prompts "No available channels". Cause: Access permission for this model has not been enabled in the corresponding group, or the mapping rule for this model has not been added in the third-party API gateway configuration. - Phenomenon: Retrieval response time exceeds expectations after enabling reranking in the knowledge base QA workflow. Cause:
chunk_sizeis set too large, resulting in overly long single segments, orretrieve_top_kis set too high, increasing the calculation load for vector retrieval and reranking. - Phenomenon: The latest thermal coal quotation data does not appear in retrieval results after index update. Cause:
index_refresh_intervalis set to weekly or monthly updates, which does not match the daily update rhythm of thermal coal spot prices, and the incremental sync switch is not enabled.
How to Confirm Proper Configuration
- Go to the model management page of FastGPT v4.8.21, verify that the
embedding_modelconfiguration matches the currently used model, and the API key status is normal. - Upload a thermal coal marketing document, check whether the segmentation results match the
chunk_sizesetting, and no obvious professional metrics are truncated. - Initiate a retrieval test, confirm that the number of returned results matches the
retrieve_top_ksetting, and the results include the target thermal coal professional metrics. - Wait for the
index_refresh_intervalto trigger, check the index update log to confirm that the incremental sync task is executing normally.
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.