What the data for this category looks like
Glass intelligent due diligence data comes from internal quality inspection archives of manufacturers, public test reports from national building material testing institutions, and construction project acceptance archives. Update cycles vary significantly: production batch data is updated with each production run, third-party test data is updated on a fixed testing cycle, and project acceptance data is only generated when the corresponding project is completed. Document structures include batch identifiers, product specifications, physical performance parameters, testing institution signature pages, and more. Physical performance parameter fields include nominal thickness (unit: millimeters), bending strength (unit: megapascals), thermal conductivity (unit: watts per square meter kelvin), and others. Individual document lengths range from several pages to dozens of pages.
Constraints imposed on vector models and indexing
Glass due diligence data includes two types of content: structured physical parameters and unstructured test reports, with significant differences in update cycles. This creates multiple constraints for the vector models and indexing workflow. First, structured numerical fields require separate numerical vector mapping configurations to avoid vector space misalignment with text content. Second, frequently updated production batch data and infrequently updated project acceptance data need to be split into separate indexes, with incremental trigger rules configured for each, to reduce resource consumption from full reindexing. Third, strict unit consistency is required for physical parameters. Field units must be verified and unified before indexing, otherwise vector similarity calculation errors will occur. Finally, the wide range of individual document lengths requires flexible segment threshold configurations to avoid truncating long documents and losing critical test information.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_batch_size | 32–64 items/batch | Glass due diligence documents have wide variations in individual length. This batch size balances memory usage and vectorization efficiency |
SPLIT_CHUNK_SIZE | 800–1200 characters | Glass test reports include multiple sections of parameter descriptions. This length preserves complete associated parameter information and avoids truncation |
RECALL_TOP_K | 10–15 results | Glass due diligence requires matching multi-dimensional parameters. An appropriate number of recalls covers more candidate documents |
VECTOR_STORE_INDEX_TYPE | IVFFlat | There are many structured numerical fields. This index structure balances retrieval speed and similarity accuracy |
UPLOAD_FILE_MAX_SIZE | 500 MB | Large project glass acceptance summary documents may exceed conventional sizes. This value covers most scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex third-party test reports may require longer parsing times to avoid mid-process interruptions |
The parameter values provided on this page are common starting points for configuration. Actual values are influenced by material form, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Index creation steps stall, with
ETIMEDOUTerrors shown in logs. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and parsing of large glass acceptance documents times out, causing index interruption. - Symptom: Search tests return
500 Internal Server Error, with model loading failure shown in the console. Cause: The output dimension of the newly embedded model is not verified to match thevector_dimensionparameter of the vector storage configuration, resulting in incompatibility with the vector store. - Symptom: Search results after vectorization only show vector data, with no original document content visible. Cause: The
SAVE_RAW_DOCUMENTconfiguration item is not enabled, so original document content is not synchronized to the associated storage medium, and only vector data is written to the vector database.
How to confirm proper configuration
- View the vector store index type configuration, confirm it matches the preset
VECTOR_STORE_INDEX_TYPEvalue, and matches the mixed structured and unstructured characteristics of the current data. - Upload a single typical glass test document to trigger the vectorization process, confirm that the parsing process does not time out or interrupt, and that segmentation results meet the length requirements of business documents.
- Initiate a multi-dimensional search test, compare the number of returned results with the configured recall rules, and confirm that the recall logic meets expectations.
- Check the metadata storage associated with the vector database, confirm that the identification fields of the original documents are correctly associated with vector entries.
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.