What data looks like for this category
The data for carbon steel intelligent due diligence reports primarily comes from steel mill factory quality inspection reports, transaction ledgers from spot trading platforms, monthly statistical reports from industry associations, and downstream purchase orders. Data update rhythms fall into three categories: factory reports are updated with production batches, spot data is updated daily, and industry statistical data is updated weekly or monthly. The document structure of a single report includes fields such as steel mill name, product grade, specification dimensions, yield strength, tensile strength, elongation, delivery cycle, and inventory surplus. Most field units follow professional metrology standards like MPa, mm, and tons.
What constraints these characteristics impose on the vector models and indexing link
The multi-field numerical features of carbon steel due diligence reports require vector models to adapt to both Chinese technical terminology and semantic encoding of structured numerical values, to avoid vector deviation caused by differences in numerical units. Frequently updated spot and batch data requires indexes to support short-cycle refreshes, to prevent index data from lagging behind business changes. Strong semantic associations between multiple fields, such as the binding relationship between grades and strength indicators, require segmentation to retain field boundaries, to avoid breaking the contextual association of core indicators. Differences in field order and format across different reports require indexes to support flexible field matching rules, to adapt to report structures of different formats.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | m3e-base or text-embedding-3-small | Adapts to semantic encoding of Chinese technical terminology and numerical fields for carbon steel, and output dimensions are compatible with mainstream vector databases |
chunk_size | 800–1200 characters | Carbon steel due diligence reports contain multi-field long text; segmentation length adapts to the semantic association boundary between fields, avoiding excessive splitting of indicators |
chunk_overlap | 50–80 characters | Retains contextual associations between adjacent fields, avoiding semantic breaks after segmentation that affect vector matching accuracy |
similarity_threshold | 0.72–0.85 | Carbon steel indicators have strong numerical correlation; this threshold range can filter irrelevant batch data while retaining valid matching items |
recall_top_k | Top 8–12 entries | Carbon steel due diligence requires covering multiple batches of data with the same grade and specification; the number of recalled entries meets multi-dimensional matching requirements |
index_refresh_interval | Every 15 minutes | Adapts to the update rhythm of daily spot data and batch factory reports, ensuring timeliness of index data |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material forms, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Vector matching results show abnormal similarity values of 10000+. Cause: The vector database dimension and the output dimension of the currently used embedding model are not aligned, leading to vector space calculation deviation.
- Symptom: The knowledge base shows unindexed after upload, or the index status stays in processing for a long time. Cause:
index_refresh_intervalis not configured to a value adapted to the high-frequency updates of carbon steel, orchunk_sizeis set beyond the system parsing limit. - Symptom: The m3e model shows no available channels, and the embedding service cannot be called. Cause: The deployment address and port of m3e are not added in the system configuration, or the corresponding embedding service container is not started.
How to confirm the configuration is complete
- Upload a single carbon steel due diligence report, review the parsed segmented content, confirm that segments are split along field boundaries, with no fragmented core indicator text.
- Initiate a search for carbon steel products of the same grade, review the distribution of recall results, confirm that the threshold filters out irrelevant non-grade matching items.
- Wait for the configured index refresh cycle to complete, review the update log of the index database, confirm that newly uploaded reports have been automatically indexed.
- Switch the embedding model to the target configuration item, verify that the numerical correlation of search results aligns with expectations, with no abnormally high similarity values.
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.