What the data for this category looks like
Auto parts-related data mainly comes from automotive manufacturer bill of materials (BOM) lists, supplier qualification documents, third-party test reports, and industry compliance standard documents. Data is synchronized in quarterly batches, and temporary change push is also supported. Documents mainly use structured fields paired with long-text descriptions, including fields such as part number, material type, supplier identification code, compliance certification number, etc. Units mostly use physical measurement units such as piece, kilogram, millimeter, and the length of individual documents varies widely.
What constraints these characteristics impose on the "knowledge base retrieval and recall" link
The mixed characteristics of structured fields and long texts require the retrieval link to support both exact matching and semantic recall, to avoid missing core field information caused by relying solely on semantic recall. The quarterly update rhythm requires the knowledge base to be configured with an incremental synchronization mechanism to prevent recalling expired supplier qualification or compliance standard content. The wide variation in individual document length means that context association must be retained during segment processing, to avoid losing the binding relationship between part numbers and corresponding technical parameters after splitting.
How to set the configuration
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
topK | 100–800 items | Auto parts documents often contain long-text technical parameters, so enough retrieval results need to be covered to match core needs, while avoiding excessive irrelevant content from interfering |
similarity_threshold | 0.72–0.85 | Balance core semantic matching and accuracy, avoiding recalling irrelevant documents with too low a threshold, and missing key technical details with too high a threshold |
chunk_size | 800–1200 characters | Balance the semantic integrity of a single segment of text and context relevance, adapting to the long paragraph structure of parts technical documents |
refresh_interval | 7 days | Match the industry's quarterly update rhythm, while covering the rapid synchronization requirements of temporary change pushes |
rerank_topN | Top 3–5 items | Focus on the most relevant technical parameters and compliance content, avoiding redundant results affecting due diligence report generation |
exact_match_fields | part number, supplier ID | Force matching of core identification fields to ensure that retrieval results are bound to correct part information |
The parameter values provided on this page are common recommendations used as a starting point 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.
Three common mistakes
- Phenomenon: When retrieving questions related to a specified part, the recall results contain a large amount of document content from non-target parts. Cause: The
exact_match_fieldsconfiguration for mandatory binding of core identification fields is not set, and relying solely on semantic recall causes irrelevant documents with similar semantics to be selected. - Phenomenon: The
topKparameter cannot be set in the 100–900 range, and only the two fixed values of 100 or 900 can be selected. Cause: The custom parameter configuration mode is not enabled, and the system locks the recall count settings with discrete gears by default. - Phenomenon: After configuring the minimum
similarity_thresholdas 1, the response still contains a large amount of non-target reference content; or after segmented retrieval, the correspondence between recalled segments and the technical parameters of the original document deviates greatly. Cause: Paragraph-level context association recall is not enabled, and only single-segment semantic matching is used, causing the split segments to lose the binding relationship with core fields.
How to confirm the configuration is correct
- Upload a single core parts document, initiate a targeted retrieval request, and check whether the recall results only contain relevant content from this document.
- Adjust the
similarity_thresholdparameter to verify that the number of retrieval results follows the expected trend as the threshold changes. - Check the reference sources of the retrieval results to confirm whether the correct part number and supplier information are bound.
- Trigger an incremental synchronization task to verify whether newly submitted parts modification documents can be retrieved within the specified time.
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.