What the data for this category looks like
Glass marketing-related data primarily comes from enterprise product management systems, industry standard documents, offline exhibition hall materials, and online product detail pages. Data updates are triggered by new product launches and parameter adjustments, with no fixed schedule. Individual documents are mostly structured parameter pages, containing product model, specification dimensions, physical performance parameters, application scenarios, and compliance certification information. Field units are mostly millimeters, megapascals, and percentages. Some documents include real-shot images and installation diagrams.
Constraints imposed by these characteristics on model integration and configuration
The structured parameters of glass marketing data are dense and include multi-dimensional performance indicators. This requires precise matching of field semantics during vector recall to avoid interference from irrelevant parameters. The real-shot images and installation diagrams included in documents require a text-image parsing module to be configured during model integration; otherwise, complete extraction of marketing materials is not possible. The lack of a fixed update cycle requires a knowledge base synchronization mechanism that triggers on demand. The presence of multi-unit fields increases the encoding complexity of the vector model, so the embedding dimension must be adjusted to adapt to the semantic mapping of multiple types of numerical parameters.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | shaw/dmeta-embedding-zh | Adapts to semantic encoding of Chinese structured parameters and marketing text, meeting the processing needs of glass category data |
CHUNK_SIZE | 800–1200 characters | Glass marketing documents include parameter descriptions and scenario descriptions; this range can fully preserve the semantic integrity of a single set of product parameters |
RECALL_TOP_K | Top 6–8 results | Glass products have multiple parameter dimensions, so a sufficient number of documents must be recalled to cover different performance indicators and avoid missing key marketing information |
PARSE_IMAGE_ENABLE | Enabled | Glass marketing documents include real-shot images and installation diagrams; enabling this allows extraction of text and scenario descriptions from images |
SYNC_TRIGGER_MODE | On demand | Glass product data has no fixed update cycle; on-demand synchronization avoids unnecessary resource consumption |
EMBEDDING_DIMENSION | 1024 | Adapts to the semantic encoding needs of multi-unit, multi-type numerical parameters, improving the accuracy of vector recall |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Knowledge base search responses are slow, and logs show
504 Gateway TimeoutorETIMEDOUTerrors. Cause: The segment length was not adjusted for multi-parameter documents in the glass category, and overly long single segments increase the computational load of vector encoding and recall. - Phenomenon: Text descriptions from product real-shot images are not included in split text after question answering. Cause: The
PARSE_IMAGE_ENABLEconfiguration was not enabled, and the text-image parsing module was not activated, making it impossible to extract parameters and scenario descriptions from images. - Phenomenon: Redundant parameters account for a high proportion of vector recall results. Cause: A reasonable similarity threshold was not set, resulting in the recall of too many similar glass product documents that interfere with the extraction of core marketing information.
How to confirm the configuration is complete
- Upload a glass product document containing parameters and images, check if the parsed segments fully retain core fields, and confirm that the text-image parsing module correctly extracts text information from images.
- Initiate a query for glass performance parameters, review the matching degree and coverage of recall results, and verify that the configuration adapts to the multi-dimensional parameter characteristics of the category.
- Manually trigger a knowledge base synchronization task, check the synchronization execution status, and confirm that the synchronization trigger mode matches the category's lack of a fixed update cycle.
- Test queries for parameters with different units, check if recall results accurately match corresponding fields, and confirm that the vector model configuration adapts to the semantic encoding of multiple types of numerical parameters.
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.