What this category's data looks like
As a segmented category under supply chain service scenarios in the financial industry, marketing-related data for steel trade originates from internal quotation ledgers, regional steel supply and demand weekly reports, customized marketing script templates, and customer inquiry follow-up archives. Update frequencies vary: quotation data is updated daily, supply and demand reports are updated weekly, and the marketing script library is adjusted as needed. Individual document structures typically include fields such as steel grade, specification and model, origin, unit price (yuan/ton), delivery cycle, and minimum order quantity. Marketing-related texts are mostly regional promotion scripts and customer follow-up scripts, with no unified fixed length. Some structured data includes clear business units.
Constraints Imposed by Data Characteristics on Vector Models and Indexing
High-frequency updates to quotation data require indexes to support incremental synchronization mechanisms. This avoids resource consumption caused by full index rebuilding. Structured fields include clear business attributes such as specifications, origin, and unit price. These require association with metadata fields in the index to support subsequent multi-dimensional filtering. Marketing scripts and customer follow-up texts contain colloquial industrial terminology. Vector models must be adapted to understand steel industry-specific terms to improve matching accuracy. The length of individual texts varies widely, from tens of characters for quotation entries to hundreds of characters for regional promotion plans. Adaptive segmentation rules must be configured to avoid semantic cutting errors.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Ensures semantic integrity of steel marketing texts, avoids cutting professional terms and business fields |
chunk_overlap | 100–150 characters | Retains business-related information across segments, for example, preventing loss of specification information such as "Q235 steel plate" across segments |
embedding_model | Industry-specific vector model | Adapts to semantic understanding of steel-specific terms, improves matching accuracy |
recall_top_k | Top 10–15 results | Balances recall precision and system response speed, matches the precise matching needs of steel trade marketing content |
index_refresh_interval | 5 minutes | Adapts to the high-frequency update rhythm of quotation data, ensures the latest quotations are included in the index in a timely manner |
metadata_filter_enabled | Enabled | Associates structured metadata such as specifications and origin, supports quick filtering of recall results by business dimensions |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by data format, 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
- Symptom: After importing a structured quotation dataset, the interface displays an "indexing" status that exceeds the preset timeout threshold. Cause: Incremental indexing mode is not enabled, and existing metadata entries are not skipped during full index rebuilding, leading to duplicate vector generation and index writing.
- Symptom: Search results return similarity values outside the 0-1 range, reaching 10000+. Cause: No range validation is configured for the
filter_thresholdparameter, and an unadapted vector model output format is used, causing similarity normalization to fail. - Symptom: In a multi-replica deployment scenario, duplicate or inconsistent entries appear in knowledge base recall results. Cause: A shared index storage path is not configured, and each replica generates a local index copy independently, leading to differences in recall results returned by different nodes.
How to Verify Correct Configuration
- Submit a newly updated quotation data entry, wait for the index refresh cycle to complete, then run a matching search. Confirm that the recall results include this data.
- Input a query text containing steel-specific industry terms, verify that the ranking of recall results aligns with business expectations.
- Deploy multiple index node instances, run batch query requests, confirm that results returned by each node have no obvious conflicts.
- View vector generation logs, confirm that structured field metadata is correctly associated with vector index entries.
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.