What Data for This Category Looks Like
Industrial metals-related data comes primarily from publicly available futures exchange market data, monthly industry association research reports, spot trader price documents, and internal enterprise marketing materials. Data update schedules vary: spot prices are updated per trading day, industry research reports are released weekly or monthly, and marketing materials are adjusted as needed.
Document structures include both structured and unstructured formats. Structured documents are mostly tables with grades, specifications, and unit prices; fields include units such as yuan/ton or USD/ton. Unstructured documents include industry analysis, product promotion copy, and customer response templates.
Constraints Imposed on Knowledge Base Retrieval and Recall
The mixed structured and unstructured nature of industrial metals data requires retrieval and recall to support both precise field matching and semantic understanding. Frequently updated spot price data requires the knowledge base to support on-demand updates to accommodate daily or real-time content changes.
Long documents such as complete industry marketing manuals must avoid splitting professional terms and associated fields across segments, as this reduces retrieval accuracy. Unit differences across industrial metal categories—for example, aluminum ingots use yuan/ton while zinc ingots use USD/ton—require retrieval to match corresponding unit fields to avoid retrieving irrelevant category data. Recognition of professional terms such as "Cu-CATH-1" and "hot-rolled coil" also requires embedding models adapted to industrial domain terminology.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Aligns with the input limits of 1024 vector models, while retaining associated business context such as grades and specifications from industrial metals documents |
chunk_overlap | 150–200 characters | Prevents splitting associated information of structured tables and professional terms across segments, improving retrieval matching accuracy |
embedding_model | Access m3e-large via onapi or deploy open-source professional embedding models locally | Industrial metals data contains a large number of professional terms and structured fields; adapted embedding models improve semantic recognition performance |
retrieve_top_k | Top 6–8 results | Balances recall coverage and result accuracy, avoiding excessive low-similarity documents slowing subsequent processing workflows |
similarity_threshold | 0.72–0.78 | Filters irrelevant category data while retaining valid matching results containing professional terms |
auto_update_trigger | Triggered by file modification time | Adapts to the non-uniform update schedule of industrial metals data, supporting partial content updates for individual documents |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and testing on internal samples is recommended before finalizing settings.
Three Common Mistakes
- Issue: Retrieval results lack complete grade and price information, and "vector input length exceeded" errors appear in backend logs. Cause: Segment length was not adjusted for long industrial metals documents, and default short segments were used, truncating critical business content.
- Issue: Spot price data in the knowledge base fails to synchronize, and no synchronization record is generated after modifying a single document. Cause: Automatic update rules triggered by file modification time were not configured; fixed-cycle updates cannot adapt to the on-demand update requirements of industrial metals data.
- Issue: AI response time is excessively long after retrieval, and backend logs show the vector recall phase consumes significant resources. Cause: The number of recalled entries was set too high, and no reranking model was enabled to filter invalid results, leading to a large number of low-similarity documents participating in subsequent processing workflows.
How to Confirm Proper Configuration
- Upload an industrial metals marketing document containing both a structured market table and long-form industry analysis, and verify if chunked results retain complete associated fields such as grades and specifications.
- Trigger a modification operation for a single document, and confirm if the knowledge base completes synchronization of the document within the set time frame.
- Submit a retrieval request containing industrial metals professional terms, and check if the similarity of retrieved results falls within the preset threshold range.
- Review vector embedding logs to confirm the connected embedding model returns vector data normally, with no length exceeded or format error prompts.
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.