What the data for this category looks like
Data primarily comes from industry association public reports, OEM supporting announcements, annual reports of parts manufacturers, supply chain cooperation agreements, patent databases, and public materials from industry exhibitions. Updates occur irregularly, tied to OEM model iterations, new part launches, and industry policy adjustments. There is no fixed update cycle. Most documents include structured tables, long-text analysis reports, and accompanying part specification files. Common fields include OE part numbers, material grades, tensile strength, compatible model years, supplier production capacity, and more. Units include MPa, units/month, model years, and others.
Constraints imposed by these characteristics on vector models and indexing
High proportions of structured tables and large numbers of standardized fields require vector models to support structured data encoding and field semantic differentiation. This prevents confusion between similar parameters with different units. Irregular update cycles require flexible incremental index triggering mechanisms. This avoids performance losses caused by full index rebuilding. Precise identification fields such as compatible model years and OE part numbers need hybrid retrieval logic that combines exact matching and vector recall. This ensures retrieval accuracy for specific parts. Long-text analysis reports must be split by supply chain hierarchy and technical parameter dimensions. This avoids semantic interference across categories.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Use ali-embedding-v3 | Supports structured field encoding, adapts to standardized parameter semantics for auto parts, and has high matching degree with text features of industry public data |
chunk_size | 800–1200 characters | Balances the completeness of part parameters and contextual relevance, and avoids losing associated information about compatible models and supply chain hierarchy after splitting |
retrieve_top_k | Top 10–15 results | Covers multi-dimensional retrieval needs for supply chains, parameters, and vehicle models, and avoids missing key content due to insufficient number of recalled results |
similarity_threshold | 0.72–0.85 | Filters low-relevance cross-model and non-matching parts search results, and improves the hit rate of precise retrieval |
index_update_strategy | Triggered by file updates | Adapts to industry data with no fixed update cycle, reduces overhead from full index rebuilding, and ensures retrieval timeliness of new data |
structured_field_weight | 0.3–0.5 | Improves the retrieval weight of precise fields such as OE part numbers and compatible models, and balances the effects of vector semantic recall and exact matching |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by data format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common misconfigurations
- Phenomenon: Search results return matching parts documents, but the large language model returns no relevant content found. Cause: The token limit for retrieved references is not configured, or the set value is too high. This causes the retrieved documents to exceed the large language model's input window and cannot be loaded and parsed correctly.
- Phenomenon: Retrieval accuracy is insufficient, and it cannot accurately match part parameters for a specific OE code. Cause: The structured field weighting logic for enhanced indexing is not enabled. Only pure vector recall is used, and no additional weight is set for high-value precise fields.
- Phenomenon: Incremental index updates are delayed. Cause:
index_update_strategyis set to fixed-cycle triggering, which does not adapt to the irregular update rhythm of auto parts data. It cannot complete index updates immediately after new data is uploaded.
How to confirm the configuration is complete
- Upload a standard auto parts specification document, check if the field semantics after vector encoding match the original content. Adjust related configurations until the matching results meet expectations.
- Initiate a search for a specific OE part number, verify that the number of retrieved results matches the
retrieve_top_kconfiguration, and low-relevance results have been filtered bysimilarity_threshold. - Upload an updated parts document, check if the index completes updates according to the configured triggering rules, and confirm that new data can be retrieved normally.
- Configure the token limit for retrieved references, initiate a long-document search, check if the large language model can load normally and generate responses based on the retrieved content, and no prompt of no relevant content found appears.
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.