What Data for This Category Looks Like
Auto parts investment research data comes from several sources: original equipment manufacturer (OEM) supporting technical documents, public financial reports of supply chain enterprises, supply chain standards released by industry associations, parts performance reports from third-party testing institutions, and process patent documents published by patent offices. Update frequency varies by data source. OEM new model supporting documents update alongside mass production plans. Supply chain financial reports release quarterly. Industry standards update irregularly to meet compliance requirements. Patent documents publish in real time. Documents include structured bill of materials (BOM), semi-structured process parameter tables, and unstructured performance analysis reports. Fields cover part numbers, material performance parameters, compatible vehicle models, mass production cycles, and other relevant details. Most units are specialized mechanics, length, and mass measurement units.
Constraints Imposed on Knowledge Base Retrieval and Recall
Multi-source, heterogeneous data structures require retrieval systems to support both structured field matching and unstructured semantic retrieval. This prevents missing critical parameter information. Differing update rhythms across data sources require incremental update tasks to adapt to varying cycles. This ensures latest supply chain information is separated from outdated documents. Specialized fields and specific unit combinations require retrieval logic to support unit matching. This stops cases where a query for "tensile strength 800" fails to match documents labeled "800MPa". Long documents and multiple continuous parameter sets require retaining context associations between parameters during segmented recall. This avoids breaking process logic.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
recall count | Top 6-10 results | Auto parts investment research data mostly consists of specialized parameter combinations. Too many recall results increase context processing load. Too few will miss information about multiple parts compatible with the same vehicle model |
similarity threshold | 0.72-0.85 | High precision is required for matching specialized terms and parameters. A threshold that is too low will introduce irrelevant non-part documents. A threshold that is too high will miss associated information applicable to the same product category |
segment length | 800-1200 characters | Parts documents often contain continuous process parameters or BOM entries. Segments that are too long will lose the logical association between parameters. Segments that are too short will disrupt the integrity of information within a single document |
knowledge base collection search nesting depth | 2-3 levels | The collection search function added in version 4.9.13 is suitable for auto parts supply chain hierarchies. Nesting that is too deep increases retrieval time. Nesting that is too shallow fails to cover supporting data from secondary and tertiary suppliers |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Third-party testing reports are mostly multi-page PDF files, which take longer to parse. A timeout will cause failure to parse large documents and prevent them from being added to the knowledge base |
maxContext | 6000-8000 characters | Investment research analysis requires associating multiple sets of parameters such as materials, processes, and compatible vehicle models. The context window must cover at least three complete parameter combinations to ensure the relevance of retrieval results |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Failing to set nesting depth when configuring knowledge base collection search. Retrieval results only return data from first-tier suppliers, and do not cover secondary supporting parts information. Cause: Lack of understanding of auto parts supply chain hierarchical associations, default nesting depth is insufficient, and the newly added nesting configuration item in version 4.9.13 is not utilized.
- Large numbers of unlabeled parameter entries appear in retrieval results, failing to match user queries for "tensile strength 800MPa". Cause: Unit matching configuration for structured fields is not enabled, and field extraction and unit labeling are not completed for parameter documents.
- 504 timeout errors occur when parsing large parts testing reports. Cause: The value set for
PARSE_FILE_TIMEOUT_SECONDSis lower than the actual parsing time required for the document, causing large document parsing to interrupt.
How to Verify Correct Configuration
- Upload an auto parts BOM document, run a retrieval query for "suppliers of front bumpers compatible with a specific vehicle model", and confirm returned results include complete information for the corresponding supplier.
- Initiate a knowledge base collection search, and verify that parts data within the nested hierarchy can be retrieved across multiple supplier knowledge bases.
- Review the document parsing log, confirm no timeout errors appear for large testing reports, and that parsing status is successful.
- Adjust the
similarity thresholdto run a comparative retrieval, and verify that the relevance of returned results aligns with the expected adjustment direction.
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.