What the data for this category looks like
Auto service investment research data sources include auto manufacturers’ public financial reports, industry association after-sales monitoring data, parts supply chain quotation ledgers, terminal store operation logs, new energy vehicle three-electric system test reports, and more. Update rhythms vary across sources: manufacturer financial reports are updated quarterly, supply chain quotations are updated weekly, and store operation logs are updated daily. Document structures include structured parts parameter tables, unstructured industry research reports, and semi-structured after-sales work order ledgers. Many fields have dedicated units: for example, cruising range uses km, and warranty period uses months.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data sources require model access to support parsing and adaptation of multiple document types. This avoids conflicts between parsing logic for structured parameter tables and unstructured research reports. Different update rhythms require distinguishing data source update cycles when configuring incremental synchronization tasks. This prevents excessive synchronization or missed updated data. Fields with dedicated units require models to retain unit information during vector extraction. This avoids confusion of cross-unit parameters. Field mapping rules must also be configured to ensure correct identification of professional fields. There are many professional terms in auto services. A custom term dictionary must be configured to improve the accuracy of vector recall.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Auto service technical documents and research reports often contain long professional explanations. Segments that are too long will lose context association, while segments that are too short will damage the integrity of professional terms. |
topK | Top 8–12 entries | Auto investment research data includes multi-dimensional supply chain, after-sales, and financial report data. A sufficient number of related entries must be recalled to support cross-dimensional analysis. |
Similarity threshold | 0.72–0.85 | There are many professional terms in auto services. A low threshold will introduce irrelevant parts parameter entries, while a high threshold will miss after-sales data of the same category. |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Structured ledgers for auto services may contain thousands of parts data entries, leading to long parsing times. |
Incremental Sync Trigger Interval | 1–7 days | Different data sources have different update frequencies. Supply chain data can be set to 1 day, while financial report data can be set to 7 days. |
UPLOAD_FILE_MAX_SIZE | 500–1000 MB | Batch parts parameter tables for auto services may have large file sizes. |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: After upgrading to version 4.8.21, vector model calls return empty results. Cause: Channel permission verification is enabled by default in the new version, and the corresponding model channel is not added to the available model list of the knowledge base.
- Symptom: After configuring a third-party model, an error "400 Bad Request" is returned during calls. Cause: The model's API key and access domain name are not configured correctly. Models in some regions require a dedicated access address.
- Symptom: After batch uploading parts ledgers, some fields are not indexed correctly. Cause: Structured data field mapping configuration is not enabled, so the model cannot identify units and meanings of professional fields such as "cruising range (km)".
How to Verify Successful Configuration
- Go to the model configuration page of the knowledge base, check if the added model channels include the target vector model and generation model, and confirm that the status is "Enabled".
- Upload a structured auto after-sales ledger file, check if the parsed segmented content retains complete professional fields and units, with no truncation or garbled characters.
- Initiate a test query, enter "cruising range parameters of a certain brand of electric vehicle", check if the number of recalled results is within the preset 8–12 range, and if the similarity meets expectations.
- Manually trigger an incremental synchronization task, check if the synchronization log shows "Synchronization completed" with no timeout or failure errors.
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.