What the data for this category looks like
Auto parts investment research data mainly comes from OEM supporting technical documents, parts standard libraries released by industry associations, supplier-public BOM lists and capacity reports, and structural optimization documents in patent databases. The update rhythm adjusts with the mass production nodes of new OEM models. Industry standards are updated annually, and supplier quotation data is updated monthly. Most documents are structured tables and technical manuals, containing fields such as part OE numbers, materials, tensile strength, applicable model years, and compliance certification numbers. Units include millimeters, megapascals, units per month and other industrial standard units.
What constraints these characteristics impose on the multi-turn dialogue and prompt engineering link
The structured fields of auto parts data are numerous and units are complex. Multi-turn dialogue must accurately identify identifiers such as OE numbers and model years mentioned by users to avoid cross-category confusion. Data update nodes are not fixed, so prompts must explicitly require calling the latest version of parameters and certification information in the knowledge base. Long document structures lead to lengthy single knowledge base entries. Context splicing in multi-turn dialogue needs to filter redundant fields and only retain technical parameters relevant to the current conversation. Parameters of similar parts from different suppliers vary, so prompts must mandate labeling data sources and update times to ensure answer accuracy.
How to configure
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single auto parts technical documents are lengthy, so key OE numbers and model information in multi-turn dialogue must be retained to avoid context overflow |
recallTopK | Top 8–12 entries | Parts have multiple parameter dimensions, so relevant entries from different suppliers and applicable models must be recalled to cover users' potential comparison needs |
similarityThreshold | 0.75–0.85 | Part model numbers have similar identifiers, so low-matching irrelevant entries must be filtered to avoid confusing parts with different OE numbers |
promptTemplate | "Please combine the matching parameters of {oenum} and {caryear} in the knowledge base, label the data source and update time, and answer the user's question" | Must forcibly bind core identifiers specified by users to avoid calling irrelevant data |
streamResponse | Enabled | Users need to obtain long parameter reply content in real time to avoid excessive waiting time |
workflowTimeout | 120 seconds | Auto parts knowledge base search needs to load multi-dimensional table data, so sufficient processing time must be reserved |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: The workflow stops without subsequent output after reaching the knowledge base search or AI dialogue link, which can be reproduced in specific part parameter query scenarios. Cause: The
workflowTimeoutis not set long enough, and the loading time of multi-dimensional table data for parts exceeds the default timeout threshold. - Phenomenon: The API call to the dialogue interface does not return streaming content, and the front end cannot display long parameter replies step by step. Cause: The
streamResponseconfiguration item is not enabled, and the synchronous return mode is enabled by default. - Phenomenon: Answers confuse similar part parameters from different suppliers and do not label data sources and update times. Cause: The prompt template does not mandate binding the OE number or model year identifier specified by the user, resulting in the recall of irrelevant knowledge base entries.
How to confirm the configuration is correctly set
- Trigger a query that includes a specific OE number and model year, check whether the core identifiers are retained in the multi-turn dialogue context, and no irrelevant fields are mixed in.
- Call the API interface for testing, confirm that the returned content conforms to the streaming output configuration, or see gradually updated reply fragments in the interface.
- View the workflow execution log, confirm that no timeout-related errors occur, and the time consumption of each link meets business requirements.
- After adjusting the similarity threshold, test the query results of parts with similar numbers, confirm that low-matching entries are filtered out.
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.