Vector Models and Indexing for Carbon Steel Investment

What data for this category looks like

Carbon steel investment research data primarily comes from steel mill ex-factory price monitoring systems, monthly reports from the iron and steel industry association, futures exchange market databases, and supply chain logistics tracking platforms. Data update cadences cover multiple dimensions: ex-factory prices and futures market data are updated daily, industry capacity reports are updated weekly, and macro policy interpretations are updated monthly.

Three types of document structures are included: structured tables (marked with information such as grade, specification, tonnage price, inventory, etc.), long-text analysis (such as capacity adjustment policy interpretations, regional supply and demand reports), and scattered industry news updates. Fields and units have clear industrial attributes; for example, grades are identified with the Q series, specifications are marked with diameter and length in mm, price units are yuan/ton, and inventory and capacity units are ten thousand tons.

What constraints do these characteristics impose on the vector models and indexing workflow

The multi-dimensional characteristics of carbon steel data create multiple constraints for the vector models and indexing workflow. First, high-frequency daily price data requires the index to have rapid refresh capabilities; static full indexes cannot meet real-time requirements. Second, structured tables account for a large proportion of the dataset, and contain a large number of numerical fields with units. General vector models have difficulty distinguishing semantic differences between the same numerical values with different units, so targeted processing of table splitting and field alignment is required. Third, long-text industry analysis contains a large number of professional limiting conditions; vector segmentation must retain complete logical units to avoid truncating key policy or capacity clauses. Finally, format differences across multiple data sources require the index to support mixed vector storage, distinguishing vector dimensions between structured numerical data and unstructured text.

How to set configurations

Configuration Item	Recommended Value	Rationale
`embedding_model`	`Doubao-embedding-large`	Carbon steel data contains a large number of industrial technical terms and structured numerical values. This model has more stable semantic alignment effects for industrial domain text, and adapts to vector generation requirements for multiple types of data
`chunk_size`	`800–1200 characters`	Carbon steel industry reports often contain continuous capacity analysis and policy clauses. This length can retain complete logical units and avoid truncating key limiting conditions
`chunk_overlap`	`100–150 characters`	Cross-row and cross-column associated table information in carbon steel data requires contextual coherence. The overlap length can reduce information fragmentation during recall
`index_refresh_interval`	`1 hour`	High-frequency data fluctuations such as carbon steel ex-factory prices and futures market prices affect investment research conclusions. A short interval ensures the timeliness of index data
`top_k`	`Top 8–12 results`	Carbon steel investment research needs to balance industry macro data and single-variety details. Too many recall results will increase redundancy, while too few will miss key information
`table_vector_enabled`	`Enabled`	Carbon steel data contains a large number of specification and price tables. When enabled, vectors can be generated separately for table cells and row/column titles, improving recall accuracy for structured data

The parameter values provided on this page are all conventional recommendations used as a starting point 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 local samples before finalizing settings.

Three common configuration mistakes

Symptom: After enabling Doubao-embedding-large, filling in a custom request address and apikey, and clicking test directly returns a 401 Unauthorized error. Cause: The request format for the vector model was not matched correctly, or the apikey is not bound to the access permission for the corresponding embedding service.
Symptom: No corresponding table data is found in the knowledge base retrieval results, or the matching degree of table fields is extremely low. Cause: The table_vector_enabled configuration was not enabled, and vectors were only generated for the entire table without splitting semantic units of cells and titles.
Symptom: Index construction times out after batch uploading carbon steel weekly reports. Cause: The chunk_size setting exceeds the maximum input length supported by the vector model, resulting in excessively long time for single-segment vector generation, triggering the PARSE_FILE_TIMEOUT_SECONDS threshold limit.

How to confirm the configuration is successful

Enter the vector model configuration page, click the test button, and verify that a 200 OK status code and valid vector data are returned.
Upload a test document containing carbon steel specification tables, and check whether the parsed text includes split cell and title fields.
Manually trigger an index refresh, wait for the configured index_refresh_interval duration, then search for the latest carbon steel ex-factory price keyword to verify that the recall results contain updated values.
Adjust the top_k parameter to Top 5 results, search for the same keyword, and compare the change in the number of recall results to confirm that the configuration takes effect.

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.