What the data for this category looks like
Automotive service industry financial report data mainly comes from publicly disclosed annual and semi-annual enterprise reports, as well as monthly operation monitoring data released by industry associations. The update rhythm is as follows: quarterly financial reports are updated every 3 months, monthly industry data is updated monthly, and store-level operation data is synchronized daily. The document structure includes fields such as revenue classification details, customer unit price, service volume, cost composition, and more. Most units are ten thousand yuan, person-times, and service volumes. Some indicators will be marked with per-store average values.
What constraints these characteristics impose on multi-turn dialogue and prompting
The multi-source data attribute of automotive service financial reports requires prompts to clearly specify data source priorities, to avoid confusion between enterprise financial reports and industry monitoring data. The quarterly update cycle of financial reports requires prompting users to specify the report period at the start of a dialogue, to prevent the use of outdated data. The existence of multiple unit types requires prompts to mandate unified unit conversion, to avoid issues where ten thousand yuan and person-times are mixed. Fine-grained store-level data requires enabling context memory functions to track users' detailed query intentions without repeating the query scope explanation.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 12000–15000 characters | A single automotive service financial report document often exceeds 5000 characters, and multi-turn dialogue needs to retain more than 3 rounds of context |
recallTopK | Top 8 entries | Automotive service financial reports have many fields, so sufficient detailed data must be recalled to support multi-turn follow-up questions |
similarityThreshold | 0.72–0.78 | Financial report fields have high similarity, so the threshold must be adjusted to avoid recalling irrelevant cost item data |
UPLOAD_FILE_MAX_SIZE | 1000 MB | A single annual financial report often exceeds 200 MB, so large file uploads must be allowed |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing long financial report documents takes a long time, so the timeout period must be extended |
promptTemplate | Preset template: Please answer based on [enterprise financial report/industry data] for the specified report period, after converting all indicators to [ten thousand yuan/person-times/service volumes] units | The multi-dimensional attribute of automotive service financial reports requires unified prompt logic to avoid confusion between data from different sources |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Form inputs set in the workflow do not appear in the dialogue interface, and the interaction content is empty. Cause: The "dialogue context synchronization" switch of the workflow is not enabled, causing form input parameters to not be passed to the dialogue link.
- Phenomenon: The recalled financial report data has mixed units, such as ten thousand yuan and person-times being used together. Cause: Unit conversion requirements are not clearly specified in the prompt, or the
similarityThresholdis set incorrectly, resulting in the recall of irrelevant field data. - Phenomenon: Documents cannot be retrieved in the dialogue after calling the API to upload them, and a 413 status code is returned. Cause: The uploaded file size exceeds the
UPLOAD_FILE_MAX_SIZEconfiguration threshold, and the parameter has not been adjusted according to business requirements.
How to Verify the Configuration is Correct
- Upload a sample automotive service financial report document, call the retrieval interface, and verify that the returned document fields match the structure of the uploaded document.
- Initiate a multi-turn dialogue: first query total revenue, then query per-store revenue, and verify that the context is correctly retained.
- Adjust the
similarityThresholdparameter, test recall results under different thresholds, and confirm that the recall range meets business requirements. - Call the API to upload a test file, verify that the returned status code is 200, and that the document can be normally retrieved in 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.