What the Data for This Category Looks Like
Data sources include publicly available daily retail monitoring datasets and publicly disclosed terminal sales ledger information from vehicle manufacturers. Full data for the prior business day is updated daily on a T+1 schedule, and detailed model-level data has a 1-2 business day delay. Each individual data entry includes vehicle identification code, full vehicle model name, manufacturing firm, terminal transaction average price, per-vehicle cost, per-vehicle gross profit amount, regional sales share ratio, and cumulative monthly license plate registration volume. Transaction average price, cost, and gross profit amount are measured in yuan. License plate registration volume is measured in units. Regional sales share ratio is a unitless proportional coefficient.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
This category of data updates on a T+1 daily schedule, and detailed model-level data has a delay. In multi-turn dialogue, the time range of retrieved data must be limited to the last 48 hours. The prompt must explicitly prioritize calling publicly monitored datasets. Delayed terminal ledger data must not be included in priority calls. The data includes multi-dimensional fields such as vehicle identification, finance, sales, and regional share. Strong correlations exist between fields. In multi-turn dialogue, the model must be guided to actively ask for missing parameters related to vehicle model, region, or time dimension after the first interaction, to avoid generating cross-category or untargeted content. Additionally, each single data entry has many fields. The prompt must limit the number of fields the model outputs per response, to avoid information overload that reduces readability.
How to Set Configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Vehicle yield rate data includes multi-dimensional fields. Longer context retains parameter memory across multiple interactions, avoiding repeated questions |
recallTopK | Top 6–8 entries | Each vehicle data entry has many fields. Too many recalled entries causes context overload. Too few fails to cover full information for target vehicle models |
similarityThreshold | 0.75–0.85 | Similar vehicle model names exist. This threshold filters low-match irrelevant data while retaining detailed regional data for the same vehicle model |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Vehicle dataset files have large size and cover multiple models and dimensions. Sufficient parsing time must be reserved |
fileChunkSize | 1000–1500 characters | Vehicle data has many fields. Too long a chunk prevents the model from accurately matching fields. Too short a chunk splits correlated fields |
promptTemplate | Retrieve corresponding data based on user-specified vehicle model, region, and time range. When outputting, strictly organize data in the order of [vehicle identification code, vehicle model name, terminal transaction average price, per-vehicle gross profit amount, regional sales share ratio]. Clearly inform if data is missing | Fixed data fields for this category. A fixed template prevents the model from generating disorganized output formats |
The parameter values provided on this page are common recommended 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 values.
Three Common Mistakes
- Phenomenon: Extra spaces or uppercase letter conversions appear in model output results, causing field formats to fail requirements. Cause: The prompt template does not explicitly prohibit format modifications, and the model follows its trained text formatting rules by default.
- Phenomenon: Conversation histories from different users are shared, leading to data leakage or interaction confusion. Cause: Session key isolation configuration is not enabled, or independent session identifiers are not assigned to each user.
- Phenomenon: File parsing tool calls fail, returning timeout or empty results. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted to a value suitable for vehicle datasets, or the file size exceeds platform limits.
How to Confirm Successful Configuration
- Initiate a test dialogue with specified vehicle model, region, and time range. The fields output by the model match the retrieved dataset exactly, with no extra format modifications.
- Generate two independent session keys, initiate different test dialogues, and confirm that historical data from the two sessions does not interfere with each other.
- Upload the vehicle dataset file, and observe that the parsing status is normal with no timeout errors.
- Enable the streaming output switch, and verify that content fragments are returned word by word during the dialogue.
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.