What the data for this category looks like
The data for dairy product intelligent due diligence reports comes from four main sources: enterprise public financial reports, third-party quality inspection agency spot check reports, supply chain traceability ledgers, and industry association compliance data. Data update cycles fall into three categories:
- Financial reports are updated quarterly and annually
- Spot check reports are updated monthly or per batch
- Traceability ledgers are updated in real time alongside production processes
Document structures are primarily mixed-format. They include structured detection data tables, semi-structured production batch ledgers, and packaging compliance photos combining text and images. Fields include fat content, total bacterial count, and shelf life. Corresponding units are g/100g, CFU/g, and days respectively.
Constraints imposed by these characteristics on vector models and indexing
The mixed data structure of dairy product due diligence reports requires vector models to adapt to both structured numerical fields and semi-structured text content. It also requires support for unified encoding of multiple data types. Differences in update cycles across data sources require the indexing system to flexibly switch between incremental refresh and full refresh. This avoids re-encoding static data or missing real-time updated detection results. Fixed fields and units require the indexing system to retain field semantic associations during encoding. This prevents vector matching deviations caused by unit confusion. Some documents include high-definition detection photos. An additional image indexing module must be configured to associate visual information with text information. This improves matching accuracy.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL_NAME | bge-large-zh-v1.5 | This model’s encoding effect for structured numerical semantics and industry terminology adapts to the characteristics of dairy product detection data |
INDEX_CHUNK_SIZE | 800–1200 characters | Can fully retain core detection items and ledger information for a single batch of test reports, avoiding truncation of critical data |
RECALL_TOP_K | Top 10 entries | Covers multi-dimensional detection, supply chain, and financial report data required for due diligence, ensuring comprehensiveness of recalled information |
PARSE_FILE_MAX_SIZE | 200 MB | Accommodates multi-page PDF third-party inspection reports and complete batch traceability ledgers |
IMAGE_INDEX_ENABLE | Enabled | Adapts to the visual information indexing needs of packaging inspection photos and on-site production photos in due diligence reports |
ONEAPI_EMBEDDING_TIMEOUT | 600 seconds | Matches the vector encoding time required when uploading multiple inspection reports in batches, avoiding timeout interruptions |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, 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 configuration mistakes
- Issue: No image indexing model option appears when creating a new knowledge base in the locally deployed v4.9.0 version. Cause: The locally deployed instance does not load commercial-exclusive image indexing plugins by default. Manual configuration of
IMAGE_INDEX_PLUGIN_PATHis required, followed by a service restart. - Issue: Numerical fields such as fat content and total bacterial count appear empty in indexing results after uploading dairy product detection Excel files. Cause: The
PARSE_STRUCTURED_DATAparameter is not enabled. Structured data within tables is not correctly extracted and encoded as a result. - Issue: The Embedding model returns a
504 Gateway Timeouterror when called via OneAPI. Cause:ONEAPI_EMBEDDING_BASE_URLis not configured to the correct OneAPI gateway address, or the timeout threshold is set lower than actual encoding time.
How to confirm configurations are complete
- Navigate to the knowledge base management page, check the
IMAGE_INDEX_ENABLEswitch status, and confirm it matches the configured requirements. - Upload a single dairy product detection report, wait for indexing to complete, and review the vector encoding details to confirm numerical fields and text content are correctly embedded.
- Call the OneAPI Embedding test interface, initiate a request using text related to dairy product detection, and confirm the returned vector data is normal.
- Review system operation logs to confirm no error messages of the
EMBEDDING_CONNECT_FAILEDorINDEX_PARSE_FAILEDtype are present.
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.