What the Data for This Category Looks Like
Commercial vehicle investment research data mainly comes from the Ministry of Industry and Information Technology road motor vehicle manufacturing enterprises and product announcements, official technical white papers from vehicle manufacturers, terminal registration data, and parts supply chain quotation systems. Update cycles are divided into monthly (registration data), quarterly (announcement updates), and annual (vehicle manufacturer financial reports and annual supply chain reports).
Document structures include long-text announcement interpretations, structured table parameter lists, and JSON-formatted bulk vehicle model data. Fields cover curb weight (kg), wheelbase (mm), rated load mass (kg), power type, cruising range (km), production batch number, and more. Units strictly follow industry standards.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering
Commercial vehicle investment research data mixes long-text announcements, structured parameters, and bulk data, with inconsistent update cycles. This creates three constraints for multi-turn dialogue and prompt design.
First, multi-turn dialogue must distinguish three types of requests: vehicle parameter queries, supply chain data association, and announcement interpretations. It must retain core context from the previous round, such as vehicle model and batch number.
Second, data units and timeliness requirements are strict. Prompts must clearly specify unit rules and data update ranges.
Third, mixed recall of structured and unstructured text requires clear priority for recalled content in prompts, to avoid irrelevant data interfering with query results.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Commercial vehicle investment research documents have long individual lengths. This range retains core context such as vehicle parameters and batch numbers during multi-turn interactions, preventing information truncation |
recallTopN | Top 10–15 results | Commercial vehicle data has many fields and close associations. A sufficient number of structured and unstructured content must be recalled to cover the parameter scope required for queries |
similarityThreshold | 0.75–0.85 | Commercial vehicle parameters have strong uniqueness. A relatively high matching threshold must be set to avoid recalling irrelevant data from non-target vehicle models |
parseChunkSize | 800–1200 characters | Commercial vehicle announcement documents have long lengths. Chunk lengths adapt to the paragraph structure of industry documents, avoiding damage to the integrity of parameter tables during splitting |
forceResponseFormat | Enable JSON Schema validation and specify field units | Structured output of data is required for investment research scenarios. Clear format and unit requirements prevent chaotic results |
promptTemplate | Fixed inclusion of "Current data updated to YYYY-MM, units must comply with commercial vehicle industry standards, multi-turn queries must associate previous round vehicle model/batch parameters" | Adapts to the timeliness and unit requirements of commercial vehicle data, strengthens context association for multi-turn dialogue |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: Setting
maxContextto 6 prevents the model from associating previous round vehicle model parameter queries. Cause: The context window is not bound to interaction rounds, only the character count is limited, leading to truncation of core context for multi-turn interactions. - Phenomenon: Investment research data generated based on JSON Schema has mixed units or garbled text. Cause: The prompt does not explicitly require field units to align with commercial vehicle industry standards, and mandatory matching for format validation is not enabled.
- Phenomenon: Commercial vehicle data recalled from the knowledge base is irrelevant to the current query, and the number of results exceeds expectations. Cause: The number of recalled entries is set too high, or the similarity threshold is too low, introducing vehicle model data from non-target batches.
How to Confirm Correct Configuration
- Initiate two consecutive queries. First, query the rated load mass of a heavy-duty truck, then query the wheelbase of that vehicle model. Check whether the model associates the vehicle model information from the previous round.
- Submit a query with clear unit requirements. Check whether returned result fields are marked with correct commercial vehicle industry units.
- Trigger a structured output request based on JSON Schema. Check whether returned data complies with preset field formats and unit requirements.
- Adjust the context window parameter, then test the context retention duration of multi-turn dialogue, confirm that no content loss occurs.
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.