What the data for this category looks like
Domestic coal trading centers, port spot price systems, futures exchanges, and industry monitoring institutions provide source data. Sources include publicly available transaction and position data, daily industry statistics, and more. Updates follow a daily cadence. Full data for the previous day typically publishes by 16:00 on the current day. Documents use structured tables as their primary format. Fields include thermal coal origin, specifications (such as Q5500, Q5000 calorific value), flat price, car-board price, freight, financing credit limit, financing interest rate, and additional relevant metrics. Most units are yuan/ton, 10,000 yuan/10,000 tons, and percentage. Some fields include daily change annotations.
Constraints for Vector Models and Indexing
The structured table format, daily update cadence, and specialized financial field characteristics of this category create multiple constraints for the vector models and indexing workflow. First, multi-field structured documents cannot use generic text vectors to accurately capture numerical correlations and financial term semantics. Vector encoding logic adapted for structured data is required. Second, the daily update frequency requires the indexing system to support incremental synchronization. This avoids full index rebuilding that consumes excessive computing resources. Third, the semantic specificity of specialized fields such as financing interest rates and credit limits requires vector models optimized for the financial domain. Fourth, each daily report includes a large number of fields. Proper vector dimension and index sharding rules must be set to balance recall accuracy and storage costs.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
enable_multi_vector_for_table | Enabled | Thermal coal financing daily reports use structured tables as their core carrier. Multi-vector support separately encodes the semantics of table titles, row data, and column fields, improving recall accuracy |
embedding_model | Doubao-embedding-large | This model is optimized for financial domain text and structured numerical fields, and supports semantic encoding for specialized fields such as thermal coal prices and financing interest rates |
chunk_size | 800–1200 characters | Each daily report includes a large number of fields. Chunking must cover complete business units to avoid semantic fragmentation, while adapting to the input length limits of the vector model |
recall_top_k | Top 8–12 results | Daily report data includes many specialized terms. A sufficient number of chunks must be recalled to cover complete business logic, while avoiding redundant chunks that slow response speeds |
similarity_threshold | 0.75–0.85 | Filters low-relevance recall results, balancing the semantic matching accuracy of specialized fields and recall coverage |
enable_incremental_index | Enabled | Data for this category is updated daily. Incremental indexing reduces computing overhead from full index rebuilding and improves daily data update efficiency |
The parameter values listed on this page are general recommendations that serve as a starting point for configuration. Actual values vary based on data format, volume, and business rules. Each scenario requires individual analysis. Testing with internal test samples is recommended before finalizing settings.
Three Common Mistakes
- Phenomenon: After entering a custom request address and API key on the embedding model configuration page, clicking test returns a
401 Unauthorizederror. Cause: Access permissions for the model are not configured correctly, or the permission scope of the API key does not cover the vector generation interface. - Phenomenon: After uploading a table file for a thermal coal financing daily report, recall results do not include semantic information from table column fields. Cause: The
enable_multi_vector_for_tableconfiguration is not enabled. Only single-vector encoding is performed for the entire table, which cannot distinguish semantic differences between titles and row data. - Phenomenon: After completing the embedding model and knowledge base configuration, refreshing the page still displays "No available index model detected". Cause: Configuration items were not saved before refreshing the page, or there is a network connectivity issue between the indexing service and the embedding model service, and model loading verification was not completed.
How to Confirm Configuration is Complete
- Navigate to the embedding model configuration page for the knowledge base. Verify that the target vector model is selected, and that parameters including the custom request address and API key are filled correctly. A success prompt displays after clicking test connection.
- Upload a test table file for a thermal coal financing daily report. Review chunk preview results to confirm that table titles, row data, and column fields are separately encoded as multiple vector chunks.
- Initiate a conversation test using the knowledge base. Enter a query that includes thermal coal prices and financing interest rates. Confirm that recall results include matching daily report content.
- Access the indexing service monitoring dashboard. Verify that incremental indexes for the corresponding knowledge base have been generated, and that index update frequency matches the daily update cadence of the reports.
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.