What the data for this category looks like
Data related to auto parts profit margins comes primarily from public financial reports of listed companies, supply chain monitoring databases of industry associations, and supporting announcement documents from original equipment manufacturers (OEMs). There are three update frequencies: monthly financial report data is updated monthly, supporting quotation and shipment data is updated weekly, and industry guidance prices are updated every ten days. A standard single document typically includes fields such as part number, supplier entity, statistical cycle, revenue scale, direct cost, shipment volume, and supporting vehicle model series. Revenue scale is measured in ten thousand RMB, shipment volume is measured in sets, and direct cost is measured in ten thousand RMB.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
Data sources for this category are scattered and have different update frequencies. Multi-turn dialogue must distinguish between statistical data of different cycles such as monthly financial reports and weekly shipments to avoid information confusion. Documents contain multiple associated fields, and multi-turn interactions must retain context such as part model and supplier entity, otherwise subsequent questions cannot be accurately associated. Document formats vary across different data sources. Prompt engineering must clearly specify data sources and field rules, otherwise recall results may easily include information from unrelated categories. Frequently updated data requires the ability to quickly trigger knowledge base refreshes during dialogue, otherwise returned content may lag behind the latest market trends.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 tokens | Auto parts profit margin documents contain multiple sets of associated fields. Multi-turn dialogue needs to retain 3-5 rounds of context, and this range covers the context requirements of conventional interactions, avoiding irrelevant responses caused by exceeding model support limits |
chunkSize | 1000–1500 characters | A single document has a large number of fields. The segment length adapts to the field association characteristics. Excessively long segments will lose field context, while excessively short segments will destroy the semantic integrity of a single document |
recallTopK | Top 6–8 entries | Sufficient segments must be recalled to cover complete statistical information, while avoiding excessive redundant information interfering with large model generation results |
conversationMaxHistory | 10–15 entries | Multi-turn dialogue needs to retain key context such as part model and statistical cycle. This range balances context window usage and information completeness |
fileParseChunkOverlap | 100–200 characters | Auto parts documents have closely associated fields. Overlapping segments prevent key fields from being truncated during segmentation, ensuring semantic coherence of recalled content |
promptTemplate | Fixed prefix: "Please answer questions based on the following auto parts profit margin data, and associate the part model and statistical cycle mentioned in the context" | Clearly specify the data category and context association rules, preventing large models from confusing profit margin data of different cycles or models |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Starting from the second question in multi-turn dialogue, responses become irrelevant and fail to associate the part model or statistical cycle mentioned in the previous round, even after upgrading to version 4.9.7. Cause: The
conversationMaxHistoryparameter is not configured, or the value is set too low, resulting in key context not being retained. - Phenomenon: Uploaded long auto parts documents are truncated, and some fields are not successfully recalled. Cause: The
chunkSizeandfileParseChunkOverlapparameters are not adjusted, and the segment length or overlap value does not match the field association characteristics of this category of documents. - Phenomenon: Conversation logs are automatically lost after exceeding storage limits, or cannot be cleaned up as required. Cause: Reasonable conversation log retention rules are not configured, or the corresponding manual cleanup function is not enabled.
How to confirm proper configuration
- Initiate two consecutive conversations. First, ask for the monthly profit margin of a specific part model, then ask for the same-period data of another model from the same supplier. Verify whether the large model associates the supplier and model information mentioned in the first round.
- Upload an auto parts document containing multiple sets of fields, and check whether the segmented segments retain complete part number and statistical cycle information.
- After configuring field filtering rules for vector retrieval, initiate a query containing a specified part model, and verify whether the recall results only include data for the corresponding model.
- View the conversation history, confirm that the number of retained entries matches the value of the configured
conversationMaxHistoryparameter, and that key context is not omitted.
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.