What the data for this category looks like
Data for auto parts intelligent due diligence reports primarily comes from supplier qualification documents, bill of materials (BOM), third-party test reports, compliance certification documents, and quarterly production capacity statistics. Update rhythms vary by data type: Supplier qualifications and compliance certifications are updated annually or at qualification expiration dates. BOMs are updated alongside vehicle model project changes. Batch test reports are updated in real time with production batches. Document structures are mostly semi-structured, including fields such as part number, supplier name, material parameters, compliance level, and batch number. Physical units are mostly engineering units such as millimeters (mm), newton meters (N·m), and megapascals (MPa). Some documents include structured tables and unstructured test description text.
What constraints these characteristics impose on vector models and indexing
Mixed semi-structured and unstructured data formats require vector models to support semantic encoding of both text passages and parameterized content, to avoid disconnecting parameter descriptions and numerical values. Data sources with different update rhythms require indexes to support flexible switching between incremental and full updates, to avoid re-indexing expired qualification or test data. The feature of multiple fields with dedicated engineering units requires retaining unit information during vector encoding, to prevent misjudgment of parameters with the same name but different units as similar content. Batched document structures easily produce duplicate entries, requiring indexes to have deduplication capabilities based on part numbers and batches, to reduce index storage redundancy. Large total volumes of batch-imported documents require index shard configuration to adapt to data scale, to ensure retrieval response efficiency.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Auto parts documents often include parameter tables and unit descriptions. Excessively long segments will break parameter associations, while excessively short segments will lose semantic context |
similarity_threshold | 0.72–0.85 | Semantic similarity of component parameters is relatively high. A threshold that is too low will introduce irrelevant entries, while a threshold that is too high will miss matching compliance documents |
dedup_enable | Enabled | Multiple batch reports for the same component easily produce duplicate text. Enabling this avoids duplicate indexing consuming resources |
recall_top_k | Top 8–12 results | Due diligence reports need to cover multiple dimensions including supplier qualifications, material compliance, and production capacity data. Excessive recall will increase context redundancy |
vector_model_api_timeout | 30–60 seconds | Third-party test report text is relatively long, leading to longer interface response times. Timeouts will cause indexing failures |
index_shard_num | 1–3 shards per million records | Auto parts data is mostly imported in batches. Too few shards will cause retrieval delays, while too many shards will increase index maintenance costs |
The parameter values provided on this page are all common recommended 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.
Three common mistakes
- Phenomenon: A large number of duplicate entries appear after the knowledge base index is merged. Cause: The
dedup_enableconfiguration is not enabled, or the deduplication field based on part number is not set. - Phenomenon: A 401 error is returned when calling the vector model. Cause: The API key of the vector model is not configured correctly, or the used custom channel has not correctly bound the vector model interface permissions.
- Phenomenon: After custom document splitting, the index order does not match the original document. Cause: Automatic deduplication is enabled and the original document's order identification field is not retained, resulting in disruption of the original order after duplicate entries are deleted.
How to confirm the configuration is correct
- Upload a single auto parts test report, check the index segmentation results, and confirm that the segment length falls within the configured
chunk_sizerange. - Submit two duplicate documents for the same component, check the index list, and confirm that duplicate entries have been automatically deduplicated.
- Call the vector model interface, check that the status code of the returned result is 200, with no 401 or timeout errors.
- Retrieve parameters for a specified part number, check that the number of recall results falls within the configured
recall_top_krange.
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.