What Data for This Category Looks Like
Steel trade investment research data mainly comes from steel mill price adjustment notices, port spot trading platforms, traders' daily quotation sheets, industry association supply and demand reports, and maritime freight indices. Update frequencies vary significantly: spot quotation data updates daily, long-term contract data updates monthly, and industry research reports are released as needed.
The document structure includes structured quotation ledgers, with fields including product specification, origin, transaction price, transaction quantity, and settlement period. Units are yuan/ton, ten thousand tons, and days. It also includes unstructured trade contracts and regional supply and demand analysis documents, with individual document lengths ranging from hundreds to tens of thousands of characters.
Constraints Imposed on Database and Operations by These Characteristics
Structured data has many fields and inconsistent units. Preprocessing must complete field alignment across data sources. Otherwise, retrieval results will show data confusion. Different data sources have large differences in update frequency. Synchronization tasks must be scheduled in batches. Full synchronization will otherwise occupy too many server resources. Unstructured document lengths vary widely. Token consumption differences between short quotations and long-term contracts can reach dozens of times. This leads to significant fluctuations in resource usage for vector storage and parsing. Investment research has high requirements for data timeliness. Spot data delays exceeding 4 hours will affect decision-making. Database operations must ensure real-time synchronization stability while accounting for historical data storage efficiency.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
vector_batch_size | 100–200 items per batch | Steel trade structured data includes multi-specification fields per entry. Excessively large batches will cause vector generation timeouts. Small batches reduce resource usage per batch |
chunk_size | 800–1200 characters | Steel trade unstructured documents include long paragraphs of price analysis and trade clauses. Too long segments lose contextual association. Too short segments destroy logical integrity |
retrieval_top_k | Top 10–15 entries | Investment research covers steel product data across multiple regions and specifications. Too few retrievals miss key competitor and market information |
db_sync_interval | 300 seconds (spot data), 86400 seconds (long-term contract data) | Update frequencies vary significantly across data sources. Differentiated configurations balance timeliness and server resource usage |
similarity_threshold | 0.75–0.85 | Steel trade product specifications have high similarity. Too low a threshold introduces non-target data from unrelated categories. Too high a threshold filters valid matching results |
parse_timeout | 600 seconds | Large steel trade contracts include complex additional clauses. Parsing requires longer time to process text logic |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Retrieval response time exceeds 10 seconds, and the log shows an
ETIMEDOUTerror. Cause: Thevector_batch_sizeparameter was not adjusted to match the batch characteristics of steel trade data. Excessive single-processing data volume caused a timeout. - Phenomenon: Vector database storage space usage is far higher than expected, and some structured fields are empty. Cause: Differentiated synchronization intervals were not configured for different data sources. This led to repeated synchronization of expired long-term contract data, and null value fields were not filtered.
- Phenomenon: A
503 Service Unavailableerror occurs during concurrent requests after vllm deployment. Cause: Themax_batch_total_tokensparameter was not adjusted to adapt to the token consumption of long steel trade texts. Concurrent requests exceeded the model's video memory limit.
How to Confirm Configuration Is Complete
- Execute the batch synchronization script, check that there are no
chunk parse failederrors in the synchronization log, and the extraction completeness rate of structured fields meets business expectations. - Initiate a single retrieval request, verify that the response time meets business requirements, and the retrieved results include steel product data for the target region and specification.
- Check the vector database monitoring panel, confirm that the connection pool usage rate is stable below 70%, and there are no continuous connection overflow alarms.
- Adjust the
similarity_thresholdparameter, verify the relevance of the retrieved results, and confirm that a large amount of unrelated steel product data is not mixed in.
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.