Vector Models and Indexing for Jewelry Intelligent Due

What the data for this category looks like

Data for jewelry intelligent due diligence reports comes primarily from brand-submitted quality inspection certificates, supply chain traceability ledgers, e-commerce platform product detail pages, and industry qualification public documents. Updates trigger when new products launch, quarterly qualification reviews wrap up, or supply chains change. No fixed update cycle exists. Each report follows a fixed document structure, with fields including jewelry name, material composition, weight, dimensions, quality inspection number, authorization number, and production batch number. Most field units use grams or millimeters. Some qualification files use only plain text number formats.

Constraints imposed on vector models and indexing

Jewelry due diligence reports include many structured fields such as qualification numbers and material compositions. They also contain repeated industry-standard phrases like pure gold and 925 silver. This demands higher semantic alignment accuracy from vector models. Mixed entry of multi-source data easily causes field misalignment. Indexing requires strict matching of field boundaries. Some qualification files are text converted from scanned documents, with character recognition errors. Format checks must run before indexing. Additionally, update frequency for jewelry category data fluctuates widely. Batch indexing must adapt to non-fixed-cycle data increments to avoid index queue blockages.

How to set configurations

Configuration Item	Recommended Value	Rationale
`chunk_size`	`800–1200 characters`	Jewelry due diligence reports include structured fields and long-text qualification descriptions. This range preserves field association while avoiding semantic fragmentation
`similarity_threshold`	`0.72–0.80`	Jewelry fields such as materials and numbers have high semantic distinction. This threshold filters low-match non-target documents
`recall_top_k`	`Top 6–8 results`	Each jewelry due diligence report has few effective information segments. Too many recalls introduce irrelevant content
`PARSE_OCR_ENABLE`	`Enabled`	Some qualification files use scanned document formats. OCR is required to extract text content
`INDEX_BATCH_SIZE`	`15–20 files per batch`	Jewelry data updates have no fixed cycle. This batch size balances indexing efficiency and queue load
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Some qualification files include high-resolution scanned documents. OCR parsing takes longer

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. It is recommended to test on your own samples before finalizing settings.

Three common mistakes

Symptom: After a single jewelry quality inspection report is uploaded, it shows 8 segments, then changes to 13 segments several hours later. Repeated indexed segments appear. Cause: The INDEX_DEDUPLICATION_ENABLE parameter is not configured. The system does not perform hash checks on already indexed text segments. Duplicate content is processed during incremental updates.
Symptom: Batch triggering of vector model retraining is not supported. Only single-file adjustments allow re-uploading. Cause: The BATCH_REINDEX_SUPPORT configuration is not enabled, or the due diligence report dataset for the corresponding category is not selected in the batch task list.
Symptom: After upgrading from version 4.9.0 to 4.9.3, previously queryable jewelry due diligence report content can no longer be recalled. Cause: The new version updates the default chunk_size parameter value. Text segments indexed with the old version do not match the new model's segmentation rules. A full re-index must be performed.

How to confirm configurations are set correctly

Upload a single jewelry quality inspection report. Check backend segmentation logs to confirm segment length falls within the preset chunk_size range.
Submit a batch indexing task. Monitor index queue load to confirm no queue blockage or timeout errors occur.
Initiate a similarity query. Enter a jewelry material keyword to verify that recall result matching meets the preset similarity_threshold requirement.
Upload a qualification file in scanned document format. Confirm the backend can extract valid text content to verify the PARSE_OCR_ENABLE configuration is active.

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.