What this category’s data looks like
Commercial vehicle investment research data covers multiple sources, including Ministry of Industry and Information Technology vehicle manufacturing enterprise and product announcements, commercial vehicle industry association monitoring data, terminal vehicle registration information, automaker public vehicle model technical parameter documents, after-sales operation and maintenance logs, and industry research reports. Update rhythms vary across data sources. Industry association data is updated monthly. Automaker model parameter documents are updated with model facelifts. Registration data is updated weekly. After-sales operation and maintenance logs are generated in real time. Document structures include structured parameter tables (with fields such as curb weight, wheelbase, rated load capacity, mostly using units kg, mm), semi-structured industry analysis documents, and unstructured operation and maintenance case texts.
What constraints do these characteristics impose on the citation source and traceability link?
The multi-source nature, varied update rhythms, and structured characteristics of commercial vehicle investment research data create multiple constraints for the citation traceability process. First, structured parameter fields have different units and naming conventions across segmented categories. Heavy commercial vehicles and light commercial vehicles, for example, use different labeling logic. Traceability must precisely match field names and units to avoid cross-category confusion. Second, different data sources update at different rates. Traceability must mark data collection times to avoid using outdated registration or operation and maintenance data. Third, when semi-structured research reports and structured parameter documents are stored together, traceability must distinguish data types. It must clearly show the specific source document and entry location for each parameter, not just the file name. Finally, multi-channel operation and maintenance and registration data may include duplicate submissions. The traceability process must support deduplication and multi-source association verification.
How to configure the settings
| Configuration Item | Recommended Value Range | Basis for This Setting |
|---|---|---|
similarity threshold | 0.65–0.75 | Commercial vehicle parameter content has high semantic similarity. This range filters non-target vehicle model matching results and improves accuracy |
recall count | Top 8–12 results | Commercial vehicle investment research needs to cover multi-region registration and operation and maintenance data for the same model. Too many results exceed token limits, too few will miss key information |
maxContext | 4000–6000 token | Single commercial vehicle research report or parameter document has a long length. Sufficient context must be retained to trace corresponding entries |
reranked return count | Top 3–5 results | Prioritize returning the most matching core data sources to avoid redundant citations interfering with investment research judgments |
PARSE_FILE_TIMESTAMP_FIELD | Configure according to the built-in timestamp field of the uploaded file | Commercial vehicle data has different update frequencies. Timestamps must be used to mark source timeliness to avoid referencing outdated content |
SOURCE_DISPLAY_MODE | Full path + field name | Commercial vehicle data fields must be accurately traced to the corresponding entry in a specific document, not just the file name |
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 case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: After setting the
similarity thresholdto 0.4, non-target commercial vehicle model parameter content is still frequently recalled. Reason: There are many segmented commercial vehicle model categories. A threshold of 0.4 is too low to filter content that is semantically similar but does not match the model, resulting in insufficient matching accuracy. - Phenomenon: After uploading 100 5kb commercial vehicle operation and maintenance documents, half of the training jobs fail. Reason: Some documents do not contain compliant timestamp fields or parameter fields. Parsing cannot correctly mark traceability information, leading to training failure.
- Phenomenon: After re-uploading the training-failed commercial vehicle documents, duplicate entries still appear when the knowledge base cites content. Reason: The
DUPLICATE_REMOVE_ENABLEparameter is not enabled. Commercial vehicle data sources have multi-channel duplicate submissions, and no automatic deduplication is performed.
How to confirm the configuration is complete
- A query targeting specific commercial vehicle model parameters is initiated, and the source tags of returned results are checked to confirm they include the full path and field name of the corresponding document.
- The parsing status of uploaded commercial vehicle documents in the knowledge base is checked, and all documents are confirmed to have correct timestamps and field information marked.
- The
similarity thresholdis adjusted, and recall results under different thresholds are compared to confirm matching accuracy meets investment research requirements. - The duplicate deduplication function is enabled, and content from the same data source is queried to confirm no duplicate entries are displayed.
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.