What the data for this category looks like
Vehicle sector investment research data comes from publicly available automaker financial reports, Ministry of Industry and Information Technology (MIIT) motor vehicle announcements, third-party vehicle test reports, supply chain company disclosure documents, and in-depth industry research reports. Update cadence varies significantly by content type. Automaker financial reports are updated quarterly and annually. MIIT announcements are released in real time as new vehicle declaration submissions are received. Test reports are updated alongside vehicle model iterations. Document structures include structured parameter tables, long-form analysis sections, and supply chain detail lists. Fields cover parameters such as curb weight, driving range, maximum power, with most using standard physical units like kilograms, kilometers, kilowatts.
Constraints imposed on model access and configuration
Vehicle sector investment research data includes large volumes of structured parameters and long-form analysis content. Model access must support structured data parsing to prevent mismatches between parameter values and their units. Long-form research reports and test reports have significant length. Adjust model context window and document chunking configuration parameters to adapt to long content processing. Real-time updated MIIT announcements and vehicle iteration data require configuration of real-time recall trigger logic, to ensure the latest data is included in the knowledge base. Supply chain detail lists have high requirements for field consistency. Configure field mapping and validation parameters to ensure imported data meets format requirements during ingestion.
Configuration Recommendations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Balances context coherence and recall accuracy for vehicle documents, which include both long-form research reports and structured parameter data |
recallTopK | Top 10–15 results | Vehicle investment research requires coverage of multi-dimensional parameters and supply chain information. Too many recall results increase token consumption, while too few results omit critical data |
similarityThreshold | 0.72–0.80 | Structured parameter matching has high precision requirements. A threshold that is too low introduces irrelevant data, while a threshold that is too high fails to retrieve accurate parameters |
UPLOAD_FILE_MAX_SIZE | 500 MB | Vehicle test reports and research reports have large file sizes. This setting adapts to single-file upload upper limit requirements |
parseTimeout | 120 seconds | Long-form research reports take longer to parse. This setting prevents file parsing failures due to timeout |
rerankTopN | Top 5–8 results | Reranking filters low-quality recall results. Vehicle data requires precise parameter matching, and this range balances accuracy and processing efficiency |
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 with your own samples before finalizing values.
Three Common Configuration Errors
- Unexpectedly high token consumption occurs when orchestrating multi-model conversations in workflows. This happens when context isolation parameters between models are not configured, causing multi-model sessions to share the global token pool.
- An "undefined is not valid json" error is returned when accessing the knowledge base via a channel link. This occurs when channel parameter format compliance is not validated, or when there are unserialized JSON fields in workflows.
- Empty field parsing occurs when importing vehicle structured parameter documents. This happens when structured data field mapping rules are not configured, causing the parsing model to fail to recognize custom parameter fields.
How to Verify Successful Configuration
- Upload a vehicle test report document, and verify that the number of parsed chunks matches the preset
chunkSizevalue. - Submit a query that includes vehicle structured parameters, and check that the recalled result fields and units meet expectations.
- Submit multiple queries via channel links, and verify that the returned results use valid JSON formatting.
- After configuring real-time recall trigger logic, upload the latest MIIT new vehicle announcement, and verify that the knowledge base includes the latest data.
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.