What the Data for This Category Looks Like
Commercial vehicle research report data mainly comes from industry public disclosure documents, announcements from vehicle and component manufacturers, and public research reports released by professional consulting institutions. The update rhythm adjusts with the industry disclosure cycle. Special research reports are added around major policy releases or quarterly sales statistics nodes. Documents usually include modules such as core market indicators, segmented vehicle model data, regional distribution, and policy impact analysis. Fields include vehicle classification (heavy, medium, light), statistical cycle, sales, vehicle cost, market share values, and corresponding units such as units, RMB yuan, monthly/quarterly, etc.
Constraints Imposed on Vector Models and Indexing
Research reports contain both structured fields and unstructured long-form text. This requires indexing to support embedding and retrieval for mixed data types. Multiple classification fields such as segmented vehicle models, regions, and statistical cycles require indexing to support precise filtering of recall results by field. The update rhythm of research reports is irregular, with temporary special documents. This requires indexing to support incremental updates and flexible document ingestion cycles. There are many industry-specific terms, so vector models must adapt to professional semantic embedding effects to avoid semantic deviation. Individual research reports have long length, so a reasonable segmentation strategy is needed to ensure context integrity and retrieval accuracy.
Configuration Recommendations
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
embedding_model | Doubao-embedding-large | Commercial vehicle research reports contain a large number of professional terms. This model adapts to general industry semantic embedding, and supports custom request address and apikey configuration |
CHUNK_MAX_SIZE | 800–1200 characters | Commercial vehicle research reports contain long-form analysis and structured tables. This range balances context integrity and vector retrieval accuracy |
chunk_overlap | 100–150 characters | Prevents loss of logical association information across paragraphs after long text segmentation, and adapts to coherent analysis content of research reports |
index_batch_size | 50–100 | Individual commercial vehicle research reports have long length. Batch ingestion balances index construction efficiency and system resource usage |
filter_field_enable | Enabled | Research reports contain classification fields such as vehicle models and statistical cycles. Enabling this supports precise filtering of recall results by field |
similarity_threshold | 0.75–0.85 | Filters low-relevance general industry text, and retains retrieval results that strongly match commercial vehicle segmented scenarios |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: An error is returned directly when testing
Doubao-embedding-largeon the vector model configuration page. Cause: The path suffix of the custom request address is not filled correctly, or the apikey contains invalid characters leading to authentication failure. - Phenomenon: Table data in the knowledge base cannot be correctly split and generate multiple vectors. Cause: The table multi-vector parsing switch of the knowledge base is not enabled, or the embedding mapping rules for table fields are not configured.
- Phenomenon: After the knowledge base is configured and the agent test is normal, the page prompts "No available index model detected" after refresh. Cause: The cache of the index model is not refreshed in time, or the system fails to synchronize the available model list, leading to status abnormalities.
How to Verify Successful Configuration
- Access the vector model configuration page of the knowledge base, confirm that the target embedding model is selected, and the custom request address and apikey configuration are correct.
- Upload a test commercial vehicle research report, check the parsed segmentation results, and confirm that the segmentation length and overlap rate match the expected configuration.
- Initiate a retrieval test, enter a query containing commercial vehicle professional terms, and verify whether the field filtering logic of the recall results takes effect.
- Check the system operation logs, and confirm that there are no error messages such as index construction failure or model call abnormality.
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.