What the data for this category looks like
Auto service research report data sources include auto industry association public materials, original equipment manufacturer (OEM) official technical manuals, auto aftermarket service organization operational data, and third-party auto consulting and research results. Update rhythm adjusts based on the corresponding document type: industry reports are released quarterly, OEM technical documents are updated with vehicle model iterations, and aftermarket operational data is synchronized weekly. Document structures typically include modules such as vehicle basic parameters, service item details, charging standards, service timelines, and coverage areas. Fields include vehicle model, service category, charging amount, service duration, outlet address, and more. Units are mostly yuan, hours, kilometers, and similar units.
What constraints these characteristics impose on multi-turn conversation and prompt engineering
The multi-dimensional segmented fields and dynamic update rhythm of auto service research reports impose multiple constraints on the multi-turn conversation and prompt engineering link. Structured fields such as vehicle models, services, and charging require prompts to clearly specify field extraction and matching rules to avoid returning vague information. Differences in update rhythms across multiple data sources require configuring recall priorities for corresponding data sources in the conversation flow to ensure the latest content is returned. The large number of segmented service items and vehicle model combinations requires multi-turn conversations to gradually guide users to clarify demand boundaries, reducing interference from irrelevant recall results.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 characters | Auto service research reports contain multiple sections of structured data, requiring sufficient context to associate vehicle and service demands across multi-turn conversations |
similarity_threshold | 0.75–0.85 | Auto service data has a high degree of structuring; a higher threshold can filter irrelevant non-auto service recall results |
RECALL_TOP_N | Top 8–12 entries | There are many segmented service items and vehicle model combinations, balancing recall coverage and information overload |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large OEM technical documents or bulk outlet data takes a long time; this avoids mid-process interruptions |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supports uploading large files such as bulk outlet data and vehicle parameter tables |
max_history | Top 3–5 conversation turns | Confirming auto service demands typically requires 2–3 turns; retaining too much history will interfere with current queries |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test against your own samples before finalizing settings.
Three Common Mistakes
- Symptom: After linking variables in the configuration file, the conversation cannot correctly associate auto service research report attachments. Cause: The binding data source range of the variable is not clearly specified, and only uploaded auto service research report files are not designated for binding.
- Symptom: After building and deploying the image, uploaded auto service research report attachments cannot be recognized, with no error prompt. Cause: The correct file parsing temporary directory was not mounted during deployment, or the parsing rules for the corresponding file types were not configured.
- Symptom: In the advanced orchestration of version 4.6.9, the AI conversation node cannot obtain the initial user question. Cause: The judge node output does not carry the context variable of the original user query, and the
user_queryfield is not passed to subsequent AI nodes.
How to Confirm the Configuration is Properly Set Up
- Upload a sample auto service research report, trigger file parsing, and check whether the parsed fields cover the core content required by the business.
- Launch a multi-turn demand confirmation conversation, sequentially confirm the vehicle model, service type, and region, and check whether the historical context is correctly used for association in subsequent queries.
- Adjust the configuration item parameters, launch multiple sets of queries, and check whether the coverage and information density of the recall results meet the business requirements.
- Enter the advanced orchestration page, verify that the judge node can correctly pass the context variable of the original user query to subsequent AI nodes.
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.