What the data for this category looks like
Original data for refractory materials marketing content comes primarily from internal enterprise product technical documents, marketing promotional materials, customer application cases, and compliance qualification documents. Updates trigger on an as-needed basis, aligned with new product research and launch, industry standard adjustments, or marketing campaign updates. No fixed update cycle exists. Each single document includes fields such as product grade, chemical composition, physical performance parameters, application scenario descriptions, marketing copy, and compliance notes. Performance parameter fields have clear units: refractoriness uses ℃ as the unit, compressive strength uses MPa as the unit. Marketing-focused documents consist primarily of paragraph text.
What constraints these characteristics impose on vector models and indexing
Data for this category mixes structured parameter text and unstructured marketing content. Some fields include clear unit identifiers. This creates constraints for vector model semantic recognition. Models must correctly associate parameters with their corresponding units. The as-needed update feature requires indexes to support incremental synchronization. This avoids resource consumption from full index rebuilding. Document lengths vary significantly. Some parameter entries are only tens of characters long. Some marketing copy spans thousands of characters. Appropriate chunking rules are needed to preserve semantic integrity during vectorization. Compliance-related fields must be filtered separately. This prevents sensitive qualification content from being incorrectly included in the general vectorization process.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Balances semantic integrity for both short refractory material parameter entries and long marketing text. Prevents splitting parameters away from their associated units. |
Chunk Overlap Rate | 10%–15% | Ensures semantic continuity between adjacent chunks. Prevents sentence breaks at the connection between parameters and their units. |
Vector Model | bge-m3 | This model performs well on semantic recognition of industrial material terminology and unit identifiers. It aligns with the parameter description logic of this category. |
Number of Retrieved Entries | 8–12 | Covers the two core types of marketing content: product parameters and application scenarios. Balances retrieval accuracy and inference resource usage. |
Similarity Threshold | 0.72–0.78 | Filters low-relevance retrieval results. Avoids non-target product parameters interfering with marketing content matching. |
Incremental Index Toggle | Enabled | Adapts to the on-demand data update feature. Only synchronizes newly added or modified documents. Reduces resource usage during index construction. |
The parameter values provided on this page are standard recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. Testing on local samples is recommended before finalizing settings.
Three Common Mistakes
- Phenomenon: The vector database only stores vector data. Retrieval results cannot be linked to specific content and location of the original document. Cause: No metadata storage fields for original documents are configured. Only vector data is written to the database. No storage path or identification information for the original text is retained.
- Phenomenon: When using the
bge-m3model, semantic retrieval similarity scores are generally high. Scores exceed the preset threshold range. Cause: No text preprocessing is performed for professional parameters and unit identifiers of refractory materials. The model incorrectly amplifies the binding semantics of parameters and units. This leads to abnormal similarity calculation. - Phenomenon: After index construction is complete, retrieval results are empty. No target marketing content can be found. Cause: The incremental index synchronization function is not enabled. Or the correct document scan path is not configured. Newly added marketing documents are not included in the index process.
How to Confirm Correct Configuration
- Upload a single refractory material parameter document. Check if chunked text retains complete association between parameters and their units. No broken sentence splits occur.
- Initiate a semantic retrieval test. Verify that similarity scores of retrieval results fall within the preset threshold range. No abnormally high or low scores appear.
- Add a new marketing document. Wait for index synchronization to complete. Initiate a retrieval. Confirm the new document can be retrieved normally.
- View metadata fields in the vector database. Confirm each vector entry is linked to the storage path and relevant identification information of the original document.
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.