What the data for this category looks like
Auto parts investment research data comes from multiple sources: internal enterprise BOMs, monthly supplier quotation sheets, original equipment manufacturer (OEM) supporting requirement documents, industry association specification reports, and patent structure files. Data update cycles vary widely. OEM supporting requirements are adjusted quarterly. Supplier quotations are updated monthly alongside raw material prices. Patent files are added in real time.
Documents fall into two structural categories: structured BOMs, which include fields such as part number, material, tolerance, unit price, and weight. Unstructured specification manuals contain long-form content including parameter charts and process descriptions. Field units follow standard industrial formats. For example, tolerance uses millimeters as the unit, and unit price uses yuan per piece.
What constraints these characteristics impose on model access and configuration
A high share of structured data and dedicated industry-specific fields require models to accurately extract structured fields. This prevents generic models from misclassifying industrial terminology. Long-text specification manuals carry the risk of exceeding context window limits. Adjust segment length and context window parameters during model access. Multi-source data has inconsistent update frequencies. Configure switching logic between incremental synchronization and full updates. Part numbers act as unique identifiers. Configure entity alignment rules to ensure accurate cross-document entity matching.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the paragraph length of auto parts specification manuals, balances context integrity and embedding efficiency |
recall_top_k | Top 8–12 results | Covers multi-dimensional parameters required for parts investment research, avoids missing key specification information |
similarity_threshold | 0.75–0.85 | Filters low-match irrelevant parts data, ensures relevance of recall results |
embedding_model_provider | Open-source community mirror | Adapts to deployment requirements of open-source vector models such as m3e, reduces call costs |
LLM_MODEL | qwen-max | Balances speed and accuracy of semantic SQL generation, meets real-time requirements of investment research scenarios |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses parsing time for large BOMs or multi-page specification manuals, prevents parsing interruptions |
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
- Issue: When configuring a text understanding model in version 4.9.0, selecting
qwen-maxand clicking test triggers the error "cannot read properties of undefined". Cause: The API key and access permissions for the corresponding model are not configured in the platform backend, causing a break in the model call chain. - Issue: No available supplier options appear when selecting the m3e vector model. Cause: The platform's open-source model mirror source configuration is not enabled, or the local deployment address of the model is not filled in.
- Issue: The table structure returned by semantic SQL generation does not match the fields of the parts BOM. Cause: No prompt words for industrial exclusive fields are configured, causing generic models to fail to recognize exclusive parameters such as part number and tolerance.
How to confirm successful configuration
- Upload a standard auto parts BOM, check if the parsed structured fields fully extract exclusive content such as part number, material, and tolerance.
- Input "query the raw material cost of auto parts with model number XX", test if the semantically generated SQL correctly matches the corresponding table structure and fields.
- Call the vector recall test interface, check if the number of returned results matches the
recall_top_kconfiguration, and if the similarity falls within the preset range. - Upload a parts specification manual exceeding 1000 characters, check if it is correctly segmented and embedded according to the
chunk_sizeconfiguration.
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.