What data for this category looks like
Vehicle industry research data primarily comes from vehicle manufacturer public financial reports, Ministry of Industry and Information Technology motor vehicle announcements, third-party vehicle test reports, industry association inventory statistics, patent technical documents, and similar sources. Update cadences cover multiple dimensions: vehicle manufacturer financial reports are released quarterly, new model test reports are updated with iteration cycles, industry inventory data is updated monthly, and some real-time channel data such as dealer quotes is updated weekly. Document structures include long technical white papers (single files can span dozens of pages), structured parameter tables with fields like vehicle model code, battery capacity, CLTC range, and scattered industry commentary text. Fields and units must strictly follow industry standards. For example, battery capacity uses kWh, driving range uses km, torque uses N·m. Some documents also include multilingual parameter comparisons.
What constraints do these characteristics impose on deployment and upgrade?
The characteristics of vehicle industry research data directly constrain the configuration logic for deployment and upgrade processes. A high proportion of long documents requires parsing timeout and chunk length settings to adapt to large file sizes. A large number of structured parameters with strict unit requirements means the knowledge base’s field mapping and metadata parsing configuration must precisely match industry standards. Mixed update frequencies require the incremental update mechanism to flexibly adapt to quarterly, monthly, and weekly update cadences. Diverse data source formats require pre-installed plugins compatible with PDF table and financial report structured parsing during deployment. The upgrade process must retain existing field mapping configurations to avoid losing structured parameter units and fields during version updates. It must also maintain compatibility with parsing formats for multiple data sources to ensure consistent knowledge base retrieval after the upgrade.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the long text parsing needs of vehicle technical white papers, prevents single-file parsing from timing out and interrupting |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports large-file storage requirements for single vehicle test report PDFs, compatible with configuration limits for v4.8.0 and later versions |
PARSE_FILE_CHUNK_SIZE | 1500 characters | Retains complete context for vehicle parameters, avoids truncating long fields that would reduce retrieval accuracy |
Number of retrieved results | Top 8 results | Covers multi-dimensional parameters and industry data required for research, prevents missing critical information due to insufficient results |
Similarity threshold | 0.75–0.85 | Improves matching accuracy for structured parameters, filters low-relevance non-target data |
SYNC_INCREMENTAL_INTERVAL | 6 hours | Balances industry data update cadences and system resource usage, adapts to mixed weekly and monthly update rhythms |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue:
500 Internal Server Erroroccurs when runningdocker compose up. Cause: Structured table parsing plugin was not installed correctly, preventing extraction of PDF tables from vehicle test reports and triggering parsing failure errors. - Issue: Running
docker compose downafter Docker Compose deployment does not clear conversation records. Cause: The--volumesparameter was not added for volume cleanup, or the temporary storage path for conversation records was not configured correctly. - Issue: Unable to call the frontend page using official embedded code after deployment. Cause: The
ENABLE_EMBED_PAGEconfiguration item was not enabled, or the configuration parameter was incorrectly set tofalse.
How to verify configurations are properly applied
- Upload a vehicle technical white paper PDF larger than 500 MB, check if the parsing task status is marked as completed, confirming that the timeout and file size configurations are active.
- Submit a query that includes vehicle model parameters, check the number of returned results and field completeness, confirming that the number of retrieved results and similarity threshold configurations meet expectations.
- Run an incremental update task, check if new industry data has been synchronized to the knowledge base, confirming that the incremental update interval configuration is active.
- Attempt to call the knowledge base interface via API, check if structured parameter results are returned normally, confirming that API access configurations are correct.
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.