Data Characteristics of This Category
Thermal coal investment research data primarily comes from port spot prices released by industry self-regulatory organizations, listed contract data from futures exchanges, monthly procurement ledgers of power generation enterprises, policy documents from energy regulatory authorities, and third-party institution investment research reports. Update frequencies vary: spot prices are updated daily, futures contracts update during trading hours, policy documents have no fixed release cycle, and investment research reports are released based on project progress.
Document structures include structured price and inventory tables, semi-structured supply and demand analysis paragraphs, and unstructured policy interpretation texts. Fields include calorific value, sulfur content, origin, port inventory, flat price, etc., all with clear professional units.
Constraints Imposed on Vector Models and Indexing
The above data characteristics create multiple constraints for the vector models and indexing workflow. First, structured data accounts for a large share and has clear professional units. This requires vector models to support associative encoding of structured fields and non-text content, to avoid losing structured information in pure text vectors. Second, update frequencies differ significantly across data sources. This requires indexes to support mixed scheduling of incremental and full updates, to prevent index lag for high-frequency updated spot data. Third, document length varies widely, from short price tables of a few lines to long supply and demand research reports of tens of thousands of words. This requires chunking strategies to adapt to different document types while preserving contextual connections.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | aliyun-emb3 | Optimized for energy industry terminology to encode semantics, supports associative processing of structured fields and non-text content |
chunk_size | 800–1200 characters | Adapts to the average length of thermal coal research reports and policy documents, avoids contextual breaks, and balances vector storage and retrieval overhead |
chunk_overlap | 100–150 characters | Preserves contextual connections between chunks, prevents structured tables or continuous price data from being split apart |
recall_top_k | Top 8–12 results | Balances recall accuracy and response speed for multi-dimensional thermal coal investment research data, covers core dimensions including prices, inventory, and policies |
similarity_threshold | 0.72–0.80 | Filters low-correlation non-target data, avoids redundant results from generic semantic matching |
index_refresh_interval | 300 seconds | Adapts to the high-frequency update needs of spot data; low-frequency data sources can be adjusted to 3600 seconds |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: When multiple indexes are associated with the same document chunk, retrieval results do not return associated data as expected. Cause: Joint recall rules for multiple indexes are not configured, causing vectors from different data sources to not be correctly associated and matched.
- Issue: Index tasks run continuously beyond the preset timeout period, and the
INDEX_TIMEOUTerror code is displayed in the interface. Cause:chunk_sizeandchunk_overlapparameters are not adjusted for long thermal coal documents, leading to an excessive number of chunks per document and index time exceeding system thresholds. - Issue: Retrieval results include coal category data unrelated to thermal coal. Cause: The
similarity_thresholdparameter is not set, or the threshold is set too low, causing non-target data from generic semantic matching to be recalled.
How to Verify Proper Configuration
- Check the vector model configuration items, confirm that the
embedding_modelparameter value isaliyun-emb3, and verify that the model version matches the official released version. - Upload a single thermal coal research report and structured table, view the chunk preview results, confirm that the chunk length matches the
chunk_sizeparameter, and no critical contextual breaks occur. - Initiate a retrieval test, verify that the number of recall results matches the
recall_top_kparameter, and adjust thesimilarity_thresholdto filter non-target data. - View index task logs, confirm that the execution interval of incremental update tasks matches the
index_refresh_intervalparameter, and there are no abnormal timeout records.
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.