What the data for this category looks like
Minor metal intelligent due diligence report data comes from public industry association statistics, spot exchange quotation systems, disclosure announcements from mining and smelting enterprises, and customs import and export record data. Update frequencies fall into three categories: real-time spot quotations, monthly industry supply and demand reports, and quarterly capacity dynamics. A single due diligence document typically includes fields such as metal grade, origin, grade parameters, spot quotation, inventory scale, capacity utilization rate, and import and export volume. Quotation fields mostly use units of yuan/ton and USD/kg. Capacity and inventory fields mostly use units of tons and ten thousand tons.
Constraints on vector models and indexing workflows
Minor metal data has multiple update frequencies, including real-time spot quotations and monthly reports. Indexes must support parallel incremental updates and full index refreshes. The mixed multi-field structure includes numerical parameters and text with units. Vector models must handle mixed field vectorization without losing unit information. Single due diligence documents are lengthy and contain many professional terms. Splitting must balance semantic integrity and reasonable chunk size to avoid semantic fragmentation or chunk loss. Fields vary widely across different minor metal segments. Indexes must support custom field mapping to meet due diligence requirements for different varieties.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_length | 800–1200 characters | Minor metal due diligence reports contain professional terms and long paragraphs. This range ensures semantic chunk integrity and avoids chunk loss from excessive splitting |
max_embedding_threads | 2–4 threads | Reduces vectorization speed to avoid embedding rate limit errors, while balancing processing efficiency |
index_refresh_interval | 300 seconds | Matches real-time spot quotation update frequencies, ensuring index data stays current with source data |
recall_top_k | Top 10–15 results | Minor metal data has dense fields. Recalling enough relevant chunks covers complete information |
similarity_threshold | 0.72–0.85 | Differentiates professional terms from irrelevant text, filtering low-correlation retrieval results |
embedding_batch_size | 16–32 | Matches the number of fields in minor metal data, balancing vectorization efficiency and memory usage |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Setting
chunk_lengthto 3000 characters causes text chunk loss. Minor metal due diligence reports contain many long sentences combined with professional terms. Exceeding reasonable semantic chunk splitting boundaries leads to semantic fragmentation or chunk loss during splitting. - Embedding rate limit errors occur during vectorization. Failing to adjust the
max_embedding_threadsparameter leads to excessive thread counts. This pushes vectorization requests past interface limits. - Question-answer pair extraction entries remain stuck in the indexing queue for long periods. Unreasonable incremental refresh trigger rules cause a backlog of full index tasks that cannot complete.
How to Confirm Proper Configuration
- Upload a standard minor metal due diligence document. Check that the number of split text chunks matches expectations, with no obvious semantic fragmentation or chunk loss.
- View vectorization task logs. Confirm there are no embedding rate limit errors, and task completion duration meets expectations.
- Run a retrieval test. Input professional query terms related to minor metals. Verify returned result relevance and quantity match configuration requirements.
- View the index status dashboard. Confirm incremental refresh tasks trigger on schedule, with no unready entries stuck in the indexing queue for long periods.
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.