What Data for This Category Looks Like
Data sources for auto service intelligent due diligence reports include official vehicle registration systems linked to vehicle VINs, operational ledgers from offline auto service shops, maintenance upload systems from partner repair shops, and auto insurance claims databases. Update frequency: shop operational data syncs weekly, maintenance and claims data syncs daily, and vehicle registration data is pulled in real time.
Document structure is divided into three parts: structured header (includes VIN, shop unified social credit code, service category), detailed data table (includes service date, item, cost), and unstructured attachments (work order photos, settlement vouchers). Fields include mileage (unit: kilometers), single service fee (unit: yuan), service duration (unit: hours), number of claims, etc. Field combinations must be adjusted based on service scenarios.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
First, field combinations are flexible with no unified template. Multi-turn dialogue must first confirm the due diligence dimensions the user cares about, then gradually collect core identifiers and missing fields. Second, unstructured attachments account for a large proportion. Prompts must clearly guide users to upload corresponding work orders and settlement vouchers, and configure automatic parsing nodes to process attachment content. Third, data update frequencies differ. Prompts must note the latest sync time for each data module, and allow users to adjust the query time range. Fourth, VIN is the core query identifier. Format verification must be performed in the initial dialogue step to avoid invalid calls to external data sources.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Auto service due diligence reports need to retain historical query conditions, attachment parsing results and detailed data from multi-turn dialogue. A larger context window prevents loss of critical information |
UPLOAD_FILE_MAX_SIZE | 50 MB | Unstructured attachments such as auto service work order photos and settlement vouchers typically do not exceed 50 MB per file. This value covers most upload scenarios |
WORKFLOW_MAX_RUN_TIMES | 2000 | Auto service due diligence requires multiple calls to different data sources. This value prevents interruptions to the full due diligence process due to run count limits |
recall_top_k | Top 6 entries | Auto service due diligence data sources include multiple types of detailed data. Too many recalled entries cause context overload, while too few miss critical maintenance and claims records |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing multi-page auto service work orders takes longer. This value prevents attachment parsing failures due to early timeout |
system_prompt | Template that includes identifier verification, data timeliness notes, and attachment parsing guidance | Adapts to the multi-turn dialogue constraints of auto service due diligence, and clarifies initial interaction rules and data call scope |
The parameter values provided on this page are all 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, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: When calling the API to generate a due diligence report, the final output variable B carries the result of the previous variable A. Cause: No independent context isolation is configured in the dialogue node. Historical interaction content from multi-turn dialogue is brought into the current variable generation process, resulting in result superposition.
- Phenomenon: When making 2-3 concurrent calls to MCP nodes per second, some calls return empty values. Cause:
WORKFLOW_MAX_RUN_TIMESis not adjusted to a value suitable for concurrent scenarios, or MCP concurrency current limiting rules are not configured. Requests are discarded because the system processing limit is exceeded. - Phenomenon: Timeout errors occur when parsing multi-page auto service work order attachments. Cause:
PARSE_FILE_TIMEOUT_SECONDSis not adjusted to a value suitable for long document parsing. The default timeout period is too short, causing parsing interruptions.
How to Confirm Configuration Is Complete
- Initiate a due diligence query for a single identifier. Verify that the corresponding identifier format is automatically verified in the initial dialogue step. The verification logic can be tested by modifying the identifier format.
- Upload a standard-sized auto service attachment. Verify that upload is allowed. Upload rules can be validated by adjusting upload limit parameters.
- Initiate a due diligence process that includes multiple types of data sources. Verify that the workflow run does not trigger a count limit error. The run upper limit can be verified by adding query dimensions.
- Call the API to generate a multi-turn due diligence report. Verify that output variables only include results from the current round. Variable isolation effects can be verified by initiating consecutive different queries.
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.