What the Data for This Category Looks Like
Profitability and market data for the auto service industry comes primarily from store financial accounting systems, automaker rebate backends, and auto aftermarket market APIs. Data updates follow a daily T+1 schedule, with full synchronization completed in the early morning. Full synchronization of a single batch takes no more than 2 hours. Most data is delivered in structured CSV or JSON format, with fields including store unique identifier, vehicle model code, service category code, accounting date, per-vehicle revenue contribution, monthly cumulative store revenue, and service visits. Field units are string, string, string, date format, yuan, ten thousand yuan, and visits. No percentage-based standardized units are used.
Constraints Imposed by These Characteristics on Model Integration and Configuration
The daily T+1 update schedule requires model task triggers to occur after data synchronization completes. This avoids calling incomplete, unupdated data. Structured fields with multi-dimensional codes require precise field mapping rules. These rules ensure the model can correctly associate store, vehicle model, and revenue data. Single batch data volume requires adjustment of vector database batch processing parameters. This prevents single-run processing timeouts. Multi-data source access requires parameters compatible with different system authentication methods. It also requires data validation rules to filter fields with abnormal formats. Daily report data has a strong time dimension correlation. This requires configuring filtering parameters by accounting date during the recall stage, to ensure the model only calls market data for the corresponding period.
Configuration Settings
| Config Item | Recommended Value | Rationale |
|---|---|---|
scheduleTriggerTime | Daily 03:00 | Auto service data typically completes T+1 synchronization by 02:00 daily. This time ensures access to the latest complete data |
fieldMappingRule | Associate and match by store ID + vehicle model code + accounting date | The core association dimensions for this category's data are store, vehicle model, and accounting cycle. This avoids data misalignment |
vectorBatchSize | 50 items/batch | Single batch data volume aligns with FastGPT's vector database batch concurrency limit, preventing processing timeouts |
dataSourceAuthType | Configure API keys separately per data source | Multi-system access requires independent authentication to secure data access |
recallFilterRule | Only retain entries with matching accounting dates for the target cycle | This scenario supports profitability daily report broadcasts, so only the latest daily market data is needed |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single batch data processing requires full parsing of multi-dimensional fields. This reserves sufficient parsing time |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: Model integration test returns
404 status code (no body)error. Cause: API interface path for the data source is not configured correctly, or interface access permissions have not been opened for FastGPT call nodes. - Symptom: After uploading auto service revenue data files, some batches are not automatically parsed by the large model. Cause: The
PARSE_FILE_EXTENSION_WHITELISTparameter is not configured, or file formats are not within the allowed whitelist range. The automatic parsing logic is not triggered in FastGPT 4.8.9 Simple Mode. - Symptom: In non-tool call mode, the large model output does not include auto market data obtained from web searches. Cause: The
enable_web_searchconfiguration item is not enabled, or the system prompt does not explicitly require calling external data sources to supplement market information.
How to Verify Successful Configuration
- Run a manual data synchronization task, check the data synchronization logs for field format exceptions or authentication failure prompts, to confirm the data source configuration is valid.
- Initiate a model call test, verify that recall results only include auto service revenue data for the corresponding accounting date, to confirm the filtering rule is active.
- Check the vector database import records, confirm that the number of batch processed items matches the configured
vectorBatchSizeparameter, with no timeout or import failure records. - Enable non-tool call mode and trigger a test, verify that the model output includes preset market data references, to confirm the web search configuration is active.
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.