Multi-turn Dialogue and Prompt Engineering for Vehicle Due

What This Category of Data Looks Like

Vehicle due diligence report data mainly comes from automaker public announcements, Ministry of Industry and Information Technology (MIIT) vehicle product announcements, third-party testing institution reports, and dealer registration ledgers. Data update rhythm follows new vehicle batches, with monthly new model and model year updates. Document formats include structured Excel parameter sheets, PDF compliance test reports, and bulk vehicle ledgers. Core fields include curb weight (unit kg), CLTC range (unit km), emission standards, and wheelbase (unit mm). A single bulk ledger can contain over 10,000 model entries.

Constraints for Multi-turn Dialogue and Prompt Engineering

The bulk volume and field specificity of vehicle due diligence data require multi-turn dialogue to first clarify data source type and vehicle batch, to avoid mixing parameters from different model years. The long document and multi-field structure require prompts to explicitly specify field units and data priority, to prevent the AI from mixing parameters from different models. The high-frequency update feature requires adding a latest data verification logic in the dialogue flow, to avoid returning outdated compliance information. At the same time, the context window must be adapted for long text parsing, to prevent core parameters from being truncated.

How to Configure Settings

Configuration Item	Recommended Values	Rationale
`maxContext`	8000–12000 characters	Adapts to the long context requirements of bulk vehicle ledgers, covering core parameters of at least 3 full vehicle batches
`UPLOAD_FILE_MAX_SIZE`	2000 MB	Supports bulk import requirements for Excel files with 15,000+ rows or Chinese document files with 100,000 characters
`chunkSize`	800–1000 characters	Preserves the integrity of vehicle parameter fields and units, avoiding field information loss caused by segment splitting
`recallTopK`	Top 6 entries	Covers compliance data and after-sales records for different vehicle batches, ensuring information completeness for multi-turn dialogue
`similarityThreshold`	0.75–0.85	Differentiates parameter differences between the same model across different model years, preventing recall of incorrect older model data
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Adapts to parsing time for large Excel files and long documents, preventing dialogue from being triggered before parsing is complete

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 case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Mistakes

Front-end calls to /api/v1/chat/completions return CORS-related errors. The symptom is that the browser console displays cross-origin interception prompts. The cause is that the cross-origin whitelist has not been configured to allow the front-end access domain name.
No token consumption statistics after dialogue, or the statistics values are abnormal. The symptom is that the backend log has no token_usage field, or the value does not match the actual input. The cause is that the enable_token_usage_log configuration is not enabled, or the total token count after segmenting is not correctly counted.
No response after uploading a 100,000-character Chinese document or 15,000-row Excel file. The symptom is that the interface returns a 504 timeout error. The cause is that PARSE_FILE_TIMEOUT_SECONDS is set too short, and the parsing process for large documents is not completed.

How to Confirm Configuration Is Valid

Call the /api/v1/chat/completions interface, check that the response header includes the Access-Control-Allow-Origin field, to confirm that the cross-origin configuration takes effect.
Upload a test Excel document with 10,000 rows, check the token_usage field value in the backend log, to confirm that the token statistics function works normally.
Initiate a multi-turn dialogue, sequentially ask for the CLTC range of different vehicle batches, check that the reply includes the correct units and parameter information for the latest batch.
Monitor the response time of the dialogue interface, confirm that single dialogue response time meets the preset threshold, and no excessive delay 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.