Vector Models and Indexing for Steel Trade Investment

What the data for this category looks like

Data sources for steel trade investment research include supply and demand analysis reports published by industry associations, daily market data from spot trading platforms, steel mill ex-factory price ledgers, traders’ inventory and sales records, maritime freight rate data, and import and export policy documents. Update rhythms vary widely across data types. Spot market data updates daily. Inventory and sales records sync in real time or daily alongside transactions. Industry reports release monthly or quarterly. Policy documents update irregularly. Document structures cover structured tables, semi-structured analysis documents, and unstructured policy texts. Core field units are mostly yuan/ton and ton.

Constraints imposed on vector models and indexing

Steel trade data characteristics create multiple constraints for the vector models and indexing workflow. First, structured price, inventory, and sales data includes clear category and specification metadata. The workflow must support both vector semantic recall and precise metadata filtering to avoid irrelevant data being included in results. Second, document lengths vary widely. Short entries contain only tens of characters, while long analysis reports can reach tens of thousands of characters. A flexible segmentation strategy is needed. Third, frequently updated spot data requires indexes to support incremental synchronization. This avoids excessive resource consumption from full index rebuilding. Finally, field unit semantics must be preserved. This prevents result deviations caused by unit confusion during matching.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Covers the complete semantic meaning of single price entries, while adapting to long text splitting for industry analysis reports and avoiding semantic breaks
`metadata_filter_enabled`	`Enabled`	Steel trade data includes structured metadata (variety, specification, origin). Enabling this combines vector recall and metadata filtering to improve result accuracy
`recall_top_k`	`Top 10–15 entries`	Investment research scenarios require coverage of multi-dimensional market and analysis data. This range balances recall coverage and result relevance
`index_refresh_interval`	`Every 24 hours`	Adapts to the daily update rhythm of spot market data. Using incremental indexing instead of full rebuilding reduces resource usage
`similarity_threshold`	`0.72–0.80`	Category and specification matching for steel trade requires a high semantic similarity threshold to prevent low-match irrelevant data from being recalled
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large industry analysis reports takes significant time. This duration covers complete parsing steps and avoids premature timeouts

The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

Scenario: When answering questions, data cannot be called according to preset knowledge base priorities, and results only return from the default knowledge base. Cause: The knowledge_base_priority parameter is not configured, or the parameter format is incorrect, causing the system to fail to load priority rules.
Scenario: A 504 Gateway Timeout error occurs when calling knowledge base question answering. Cause: Incremental indexing is not configured for frequently updated spot data, and full index rebuilding is mistakenly executed, causing execution time to exceed the system timeout threshold.
Scenario: The knowledge base status remains "Training in progress" or "Rebuilding in progress" for more than 24 hours without completion. Cause: A reasonable index_refresh_interval is not set, full synchronization is enabled, and incremental updates are not turned on, causing excessive system resource consumption.

How to Verify Proper Configuration

Upload a single price entry and an industry analysis report, then check the segmented text blocks. Confirm that the split length matches the chunk_size configuration requirements.
Enter keywords that include category and specification to initiate a question answering request. Check whether the recall results combine metadata filtering and vector similarity matching logic to verify that the metadata_filter_enabled configuration takes effect.
Submit an incremental indexing task, then check the index refresh records in the system logs. Confirm that the refresh cycle matches the index_refresh_interval setting.
Call the batch index addition interface, verify that the returned status code is 200 OK, and that index data for the corresponding collection can be retrieved 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.