What this category's data looks like
Coking coal investment research data primarily comes from industry association monthly supply and demand reports, futures exchange delivery daily reports, mine production weekly reports, spot market daily quotes, and professional research reports. Update cadences vary: spot quotes and delivery data are updated daily. Industry supply and demand reports are released monthly or quarterly. Policy documents are updated randomly based on regulatory timelines. Document structures include structured tables, plain-text research report bodies, and policy notice files. Core fields include coking coal grade, production origin, and statistical cycle. Units include yuan/ton, ten thousand tons, and other standard units.
Constraints on vector models and indexing workflows
Multi-source, multi-structure, and differentiated update cadences of coking coal data create clear constraints for vector model and indexing configuration. Mixed scattered structured and unstructured data requires indexes to support unified embedding and retrieval of multimodal content. Data sources with different update frequencies need adapted indexing refresh strategies. These strategies prevent lag for real-time data and avoid redundancy for periodic data. Professional terminology and exclusive fields require vector models to capture industry-specific semantics. Indexes must support filtering by core fields to exclude irrelevant coal category data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Coking coal research report bodies often contain long-form data explanations. This length preserves the integrity of supply and demand logic within a single segment, avoiding splitting that breaks data associations |
chunk_overlap | 100–150 characters | Balances contextual continuity between segments, preventing core information such as prices and grades from being split across different segments |
embedding_model | text-embedding-3-large (or open-source models with equivalent dimensions) | Coking coal data includes professional terminology and structured fields. High-dimensional models better capture industry-specific semantics and field associations |
recall_top_k | Top 8–12 results | Coking coal investment research requires balancing multi-dimensional information including supply and demand, prices, and policies. This recall volume covers reference bases for multiple scenarios |
filter_fields | coking coal grade, production origin, statistical cycle | Matches core filtering dimensions for investment research scenarios, filtering out coal data from non-target categories |
index_refresh_interval | Real-time (spot data) + daily (report data) | Adapts to the update cadences of different data sources, ensuring index consistency for real-time data and periodic reports |
Three common configuration mistakes
- Symptom: The knowledge base returns non-coking coal data unrelated to the query. Recall results displayed in the interface include anthracite and steam coal content. Cause:
filter_fieldsfor filtering non-target category fields is not configured, and the index does not apply filtering based on coking coal-specific dimensions. - Symptom: The index status shows as not ready. Knowledge base retrieval cannot be triggered, and the backend log returns
504 Gateway Timeout. Cause:index_refresh_intervaladapted to coking coal bulk data is not set. Full index refresh timeout causes the index status to remain unupdated. - Symptom: Entire structured data tables are imported directly as the knowledge base. Retrieval results include a large number of unrelated redundant field contents. Cause: No field filtering and segment processing are performed on structured data tables. Importing full raw data directly causes vector embedding redundancy.
How to confirm correct configuration
- Execute a single retrieval test with a query containing a coking coal grade. Confirm the
sourcefield of the resulting recall entries only includes coking coal-related documents. - Check the status indicator in the index management interface. Confirm the
index_readyfield returnstrue, or that the interface displays the index as ready. - Import a single structured data table. Verify the embedded segments retain core fields and data associations, with no forced splitting that breaks logical connections.
- Adjust the
recall_top_kparameter. Validate that the number of retrieval results matches the configured value range.
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. It is recommended to test on your own samples before finalizing settings.
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.