What This Category of Data Entails
Vehicle industry investment research data sources include: vehicle manufacturer public financial reports, official national new vehicle regulatory announcements, vehicle performance test reports, parts price disclosures from supply chain enterprises, and industry association statistical data. Update cadence falls into three categories:
- Financial reports released quarterly
- New vehicle test reports updated irregularly alongside model iterations
- Supply chain data updated monthly
Document structure combines structured parameter entries and unstructured research reports, policy documents. Fields include:
- Vehicle model code
- Curb weight measured in kg
- Battery capacity measured in kWh
- Maximum power measured in kW
- Official guide price measured in ten thousand yuan
- Time fields such as launch date and financial report release date
Constraints on Database and Operations
Multi-source heterogeneous data is integrated, requiring support for both structured relational queries and unstructured vector retrieval. This creates requirements for the database's hybrid storage capabilities. Update frequencies are inconsistent: some data such as financial reports is updated in bulk quarterly, while new vehicle data is updated irregularly. This requires a mechanism that combines incremental synchronization and scheduled full synchronization. Fields carry specific units, so a unified unit standard must be maintained to avoid retrieval bias. This increases configuration needs for data cleaning. Document lengths vary widely: from parameter entries of tens of characters to research reports of tens of thousands of characters. This requires flexible segmented storage and context configuration. Bulk data synchronization generates high read-write load, which creates requirements for the database's IO buffering and operational elasticity.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Vehicle industry research data includes short parameter entries and long-text research reports. This range balances retrieval accuracy and storage overhead |
RECALL_TOP_K | Top 10–15 entries | Investment research retrieval needs to cover multi-dimensional parameters and industry trends. Too many recalled entries increase inference latency, too few risk missing critical information |
AUTO_SYNC_INTERVAL | Every 6 hours | Financial reports are updated quarterly and supply chain data monthly. Scheduled synchronization balances data freshness and operational pressure |
DB_WRITE_BUFFER_SIZE | 2048 MB | Single vehicle test report data volume is large. Buffered writing avoids overload from random disk read-write operations |
UNIT_CONVERSION_ENABLE | Enabled | Vehicle data includes multi-unit fields such as kWh, kW, and ten thousand yuan. Enabling this unifies retrieval standards |
MILVUS_INDEX_TYPE | IVF_SQ8 | Vector data includes parameter features and text features. This index type balances retrieval speed and storage cost |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on internal samples before finalizing settings.
Three Common Misconfigurations
- Symptom: High and persistent disk read-write usage after Docker deployment, with single-disk read-write occupancy consistently exceeding 80%. Cause: The
DB_WRITE_BUFFER_SIZEparameter was not adjusted. Small-batch writes frequently trigger disk IO, compounded by read-write pressure from bulk vehicle data synchronization. - Symptom: Milvus container fails to start, with logs displaying "undefined field pg". Cause: A generic Compose file with PostgreSQL dependencies was used, instead of a dedicated deployment configuration adapted for vector retrieval.
- Symptom: A 500 error is returned when the conversation record deletion API is invoked. Cause: The
DELETE_BATCH_SIZEparameter was not configured correctly. Bulk deletion requests exceed the database connection limit.
How to Confirm Proper Configuration
- The database real-time monitoring dashboard can be reviewed to confirm that read-write IO fluctuations align with the load pattern of scheduled synchronization, with no persistent overload.
- Vehicle parameter data including unit fields can be retrieved to confirm that all results use unified units, with no mixed-unit retrieval results.
- Milvus container logs can be reviewed to confirm there are no unidentified configuration item errors, and that vector indexes have loaded normally.
- The dataset collection creation API can be invoked with a collection name that follows naming rules, to confirm that a 200 status code is returned and the collection is created successfully.
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.