What Data for This Category Looks Like
Iron ore-related data is divided into two categories: structured and unstructured. Structured data includes spot prices, port inventory, and futures market trends. Unstructured data covers industry research reports, marketing copy, and customer follow-up records. Data sources include commodity spot platforms, public industry association reports, and internal enterprise marketing material libraries. Structured data is updated daily. Research reports are released weekly or monthly. Marketing materials are adjusted as needed. Supported document formats include Excel price sheets, PDF industry analyses, and Word marketing plans. Fields include origin, grade indicators, pricing units, trading ports, and quote validity periods. Most pricing units are yuan per ton.
Constraints Imposed on Vector Models and Indexing
The mixed structured and unstructured nature of iron ore data requires separate vector extraction rules for structured fields. This avoids vector encoding deviations caused by mixed formatting. Differing update frequencies across data sources require incremental indexing triggers. This prevents full reindexing from consuming excessive computing resources. Marketing materials are mostly short text fragments. Adjust chunking parameters to adapt to short content encoding, preventing critical trading information from being truncated. Variations in fields across different documents require field mapping before indexing. This avoids dimensional confusion in the vector space. Large volumes of documents uploaded in batches require reasonable batch indexing thresholds. This prevents timeouts or excessive memory usage.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk length | 800–1200 characters | Adapts to the short text characteristics of iron ore marketing materials, retains complete key information such as origin and quotes, and avoids truncating core content |
recall count | Top 8–12 results | Core matching information for iron ore marketing content is concentrated. Excessive recall introduces irrelevant historical data and reduces matching accuracy |
similarity threshold | 0.72–0.85 | Significant segmentation differences exist within the iron ore category. A threshold that is too low introduces unrelated price records, while a threshold that is too high fails to retrieve valid matching content |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Bulk uploaded Excel price sheets have large data volumes. This duration allows complete parsing and vectorization without mid-run timeouts |
incremental indexing trigger condition | By file update time | Adapts to the different update frequencies of multi-source iron ore data, only synchronizes modified documents, and reduces computing resource consumption |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: A single uploaded document shows 8 chunks after upload, then changes to 13 chunks later, with duplicate indexed fragments. Cause: No deduplication rule is configured for incremental indexing, or indexed document metadata is not skipped during parsing, leading to duplicate chunked entries being stored.
- Symptom: After upgrading from version 4.9.0 to 4.9.3, previously queryable iron ore price documents can no longer be retrieved. Cause: The new version adjusts the embedding dimension of the vector model. Vectors from the old index do not match the new model’s dimensions. Vector indexes must be regenerated.
- Symptom: Bulk retraining of the vector model cannot be triggered. Only single-file adjustments followed by upload are supported. Cause: The bulk training switch for batch indexing is not enabled, or the trigger queue for bulk training is not configured, preventing bulk training task submission.
How to Verify Correct Configuration
- Upload a standard iron ore price Excel document. Check the number of parsed chunks to confirm it matches the preset
chunk length, with no excessive truncation or duplicate chunks. - Initiate a matching query. Enter the origin and quote keywords for the target iron ore. Verify that the number of recall results matches the set
recall count. - Modify an already indexed iron ore marketing document. Wait 10 minutes, then check to confirm only the modified document is reindexed, with no full index rebuild triggered.
- View the vector index dimension logs. Confirm that the vector dimensions of all documents match the current configured model’s dimensions, with no dimension mismatch errors.
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.