What the data for this category looks like
The data for auto parts marketing content used in automotive finance scenarios comes from four main sources: original equipment manufacturer official supporting manuals, after-sales spare parts catalogs, dealer marketing material libraries, and supply chain BOM lists. Update timelines are tied to new vehicle launches and spare part iterations, with no fixed schedule. A single update covers supporting parts for one or multiple vehicle models. Common document formats include structured tables with part numbers, compatible vehicle models, and specification parameters, long-form marketing copy, and excerpts from product manuals. Fields included are part numbers, compatible vehicle model years, materials, physical unit fields such as millimeters and kilograms, and marketing selling point descriptions.
What constraints these characteristics impose on knowledge base retrieval and recall
The large number of structured tables with complex fields requires precise matching of part numbers or compatible vehicle models during retrieval, to avoid retrieving unrelated parts. The high share of long-form marketing copy requires controlling segment length to prevent context fragmentation. Updates have no fixed schedule, so incremental updates must be supported to avoid delays in retrieval results caused by full re-scans. Physical fields with units must retain unit information during vector encoding, otherwise matching accuracy will drop. The compatible vehicle model year field must support range matching, otherwise compatible parts across multiple model years cannot be retrieved.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Auto parts documents include structured tables and long parameter descriptions. This range fully preserves a single set of spare part information and marketing copy excerpts, avoiding content truncation |
retrieve_top_k | Top 10 entries | A single auto part is linked to a large volume of marketing material and spare part information. 10 entries cover core associated content and reduce redundant recall |
similarity_threshold | 0.72–0.85 | Precise fields such as part numbers require high matching accuracy, while marketing copy needs balanced semantic relevance. This range balances precise retrieval and coverage |
PARSE_TABLE_ENABLE | Enabled | Documents contain many structured spare part tables. Enabling this preserves table field structure and improves vector encoding accuracy |
incremental_update_interval | 24 hours | Spare part updates have no fixed schedule. Daily incremental updates ensure retrieval data timeliness and reduce resource use from full scans |
embedding_model | High-dimensional model that supports long-text encoding | Auto parts documents include multi-field parameters and long descriptive text. This type of model retains detailed semantics and improves retrieval matching accuracy |
The parameter values provided on this page are common recommendations used as starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Retrieval latency increases significantly after multiple rounds of questions in the same conversation window. Single retrieval times out after 60 seconds, and normal retrieval resumes in a new window. Cause: No automatic cleanup policy for conversation context is configured. Context vectors accumulated across multiple conversations occupy retrieval resources, causing queue blocking.
- Phenomenon: Semantic matching scores for recall results are too high under semantic retrieval mode, but actual content has low relevance to the query. The issue persists after replacing the embedding model. Cause:
similarity_thresholdis set too low. Low-relevance recall results are not filtered, causing redundant content with high scores to be returned first. - Phenomenon: When referencing part number fields in marketing content, retrieval results do not match corresponding spare part information. Writing the knowledge base ID directly allows normal recall, but an error is triggered when calling via application variables. Cause: Application variables are not correctly bound to the knowledge base field mapping rules. The precise matching identifier for part numbers is lost during variable transfer, causing the retrieval engine to fail to recognize the query condition.
How to Confirm Configurations Are Set Correctly
- Upload a single auto parts spare part manual, and verify that parsed document fragments retain core fields such as part numbers and compatible vehicle models, with no content truncation.
- Submit a query containing a specific part number, and confirm that the number of retrieval results matches the configured setting, and that the semantic relevance of matching results meets expectations.
- Trigger an incremental update task, and check that the update log only displays newly added or modified spare part documents, with no full scan initiated.
- Configure application variables to bind part number fields, then submit a query to verify that variables are correctly passed to the retrieval engine, with no errors returned.
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.