What the Data for This Category Looks Like
Iron ore investment research data primarily comes from public market data of commodity exchanges, port loading and unloading ledgers, steel mill purchase quotes, industry association research reports, and futures trading terminals. Update frequencies cover real-time market data, daily inventory data, weekly supply and demand reports, and monthly industry analysis. Document structures include structured forms (with origin, grade indicators, pricing units, shipment volume values), semi-structured research reports (with supply and demand logic, policy interpretations), and unstructured announcements (with port dispatch notices). Fields include unique identifiers, timestamps, cargo right ownership, and more. Pricing units are mostly yuan per wet ton or yuan per dry ton.
Constraints Imposed on Vector Models and Indexing by These Characteristics
The multiple update frequencies, high proportion of structured data, unit differences, and high proportion of long texts in iron ore investment research data create multiple constraints for the vector models and indexing workflow. Real-time market and daily inventory data require incremental indexing strategies to avoid performance losses from full index rebuilding. Unit differences in structured fields, such as wet ton and dry ton pricing, require unified unit handling during preprocessing. Otherwise, semantic deviation will occur in vector matching. Variations in the length of long-text research reports require appropriate segmentation thresholds to avoid single segments exceeding model context limits. Mixed volumes of structured and unstructured data require index structures that support multi-field combined recall to improve matching accuracy.
How to Set Configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Covers complete logical units of iron ore research reports, avoids loss of key supply and demand analysis context in single segments |
chunk_overlap | 100–150 characters | Connects semantic associations between adjacent segments, ensures consistency of vector representations for long-text research reports |
EMBEDDING_MODEL | text-embedding-3-small | Adapts to mixed semantic representation of structured quotes and semi-structured research reports, balances inference speed and matching accuracy |
index_type | IVFFlat | Meets recall requirements for hundreds of thousands of vector data entries, balances query efficiency and matching accuracy |
top_k | 10–15 entries | Covers multi-dimensional reference information required for iron ore investment research, avoids redundancy or omission of critical data |
similarity_threshold | 0.72–0.80 | Filters low-match irrelevant data, adapts to semantic matching accuracy required for iron ore supply and demand logic |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for large research report PDFs, avoids parsing failure for large documents |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on one’s own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: After indexing hundreds of thousands of data entries in the knowledge base, search responses time out or return the
ETIMEDOUTerror code. This applies to local deployment scenarios using version V4.8.20-FIX2. Cause: Incremental indexing strategy was not used. Real-time recall was not disabled during full index rebuilding, triggering redundant calculations during vector queries. - Symptom: Vector recall results include a large number of irrelevant non-iron ore category data, with some fields empty. Cause: Multi-field combined indexing was not configured. Only full-text vector recall was used, without filtering documents from non-target categories.
- Symptom: When uploading large research report PDFs, parsing tasks fail and return the
408 Request Timeoutstatus code. Cause:PARSE_FILE_TIMEOUT_SECONDSwas set too short, failing to adapt to parsing time for large research reports.
How to Verify Correct Configuration
- Run a vector ingestion test for a single structured quote document. Check that unit identifiers in the generated vector fields are unified, confirming that preprocessing configurations take effect.
- Initiate an incremental indexing test for real-time market data. Observe indexing time and system resource usage, confirming that the incremental indexing strategy adapts to the data update frequency.
- Initiate a search test for long-text research reports. Check the segmentation coherence of recall results, confirming that
chunk_sizeandchunk_overlapconfigurations are reasonable. - Initiate a batch search test. Observe response time and number of recalled entries, confirming that
top_kandsimilarity_thresholdconfigurations meet business requirements.
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.