What the data for this category looks like
Data sources for auto service revenue rate and market daily report include store POS cash register systems, automaker regional operation platforms, and third-party auto aftermarket market API interfaces. Data is generated as full reports for the previous natural day every early morning. The document uses a structured format, including fields such as service store code, service category code, average revenue per customer, regional benchmark revenue amount, and number of service completed units. The corresponding units are code, code, CNY, CNY, and units respectively. Data is classified and aggregated by store and vehicle model series. Each record corresponds to the daily revenue situation of a single service category for a single store.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The feature of data classified by store and vehicle model requires multi-turn dialogue to guide users to clarify query dimensions, to avoid result deviations caused by vague queries. The daily update schedule requires that prompts must clearly mark the data time range, to prevent users from mistaking the data for real-time market information. The structured field format requires dialogue logic to fixedly extract specified fields for calculation and broadcast, without additional parsing of unstructured content. The aggregation feature of multiple stores and multiple categories requires dialogue context to retain user-specified filter conditions, supporting users to switch query dimensions in one dialogue without repeating basic information.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000–6000 characters | Must save store and vehicle model query conditions in multi-turn dialogue, to avoid repeated inquiries to users |
recall_top_k | Top 8–12 entries | Auto service market data has many classification dimensions. Too many recalls will cause content redundancy, while too few will miss information about target stores or vehicle models |
prompt_template | Must include "Please specify the service store code or vehicle model series" and "Data is market information from the previous natural day" | Matches data classification characteristics and update frequency, guides users to clarify query dimensions, avoids vague queries |
conversation_isolation | Enabled | Achieves independent isolation of user dialogue contexts, prevents confusion of query results from different users |
stream_response | Enabled | Daily market report data has a large return volume; streaming response reduces user waiting perception |
UPLOAD_FILE_MAX_SIZE | 500 MB | Daily report files for auto services usually contain full data of multiple stores and vehicle models, with large single-file volume |
The parameter values provided on this page are conventional recommendations used as starting points 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.
Three common mistakes
- Phenomenon: A
403 Forbiddenerror is returned when calling the dialogue interface, and the prompt indicates that the application is not authorized. Cause: The API interface was not created through the application to generate the corresponding application ID, and an unregistered application ID was used directly to initiate the call. - Phenomenon: After uploading the daily market report file, parsed result fields are missing or empty. Cause: A file in an unsupported format was uploaded. Only structured data in CSV and JSON formats are supported; Excel or image format files were mistakenly uploaded.
- Phenomenon: After deploying the deepseek 32b model with ollama, no streaming response is returned for dialogue. Cause: The
stream_responseconfiguration was not enabled, or the streaming output port was not enabled during model deployment.
How to confirm the configuration is complete
- Initiate two consecutive dialogues. Specify a certain store code in the first round, and only mention the vehicle model series in the second round. Check whether the system can automatically associate the store information from the first round to complete the query.
- Upload a test structured data file, and check whether the system can correctly identify and load the field information in the file.
- Use two independent application keys to initiate the same query, and check that the context content of the two dialogues does not interfere with each other.
- Initiate a query request involving multiple stores and multiple categories, and check whether the system returns results in a segmented streaming manner, without full-load delay.
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.