Workflow Orchestration for Auto Service Research Report

What this category of data looks like

Auto service research report data primarily comes from public reports released by third-party automotive consulting institutions, after-sales business disclosure documents from original equipment manufacturers, and statistical materials from automotive aftermarket industry associations. Update cycles follow monthly and quarterly main schedules. Some special policy interpretation reports are updated in real time as policies are released. Document structures include modules such as vehicle after-sales cost analysis, parts supply chain data, and regional service center coverage rate. Fields include clear units, such as "per-vehicle maintenance customer unit price (yuan)", "parts inventory turnover days (days)", and "service center density (units per 10,000 square kilometers)". Some documents include structured tables and long-text analysis paragraphs.

Constraints these characteristics impose on workflow orchestration

Dispersed data sources require workflows to connect multiple independent knowledge bases, to avoid incomplete coverage from a single data source. Differences in update cycles require configurable custom synchronization tasks, to balance timeliness and resource usage across different data sources. Documents include structured fields and long text, so workflows must support both structured data extraction and segmented context recall, to prevent loss of critical information. Fields with units require a parameter validation step in the workflow, to ensure that retrieved fields match their corresponding units, and prevent search results where numerical values and units do not align.

How to set configurations

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Single auto service research report typically contains over 100 pages, so parsing takes a long time. 600 seconds covers most long document parsing requirements.
`maxContext`	`8000–12000 characters`	Research reports include multi-chapter analysis content. This range retains sufficient context for the large language model to generate accurate responses.
`Recall count`	`Top 8 entries`	Auto service research reports have multiple detailed dimensions. 8 recalled entries can cover core relevant content, and avoid interference from redundant information.
`Similarity threshold`	`0.75–0.85`	This range filters low-relevance general industry content, and accurately matches user queries in auto service scenarios.
`Knowledge base scheduled sync cycle`	`Once per week`	Original equipment manufacturer reports are updated quarterly, while aftermarket data is updated monthly. Weekly sync balances timeliness and computing resource usage.
`Reranked return count`	`Top 3 entries`	Reduces the large language model's input load, and focuses on highly relevant search results.

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

Three common mistakes

Symptom: A 413 Request Entity Too Large error occurs when parsing a research report. Cause: The UPLOAD_FILE_MAX_SIZE parameter was not adjusted. The default configuration cannot accommodate a complete single research report file.
Symptom: Search results do not include structured field information. Cause: The STRUCTURED_PARSE_ENABLE parameter was not enabled, so the system cannot automatically extract structured table data from research reports.
Symptom: A document parsing process is triggered after a user query. Cause: QUERY_PARSE_MODE was not set to Retrieve only. The system incorrectly identifies user queries as file parsing tasks.

How to confirm the configuration is correct

Upload a local auto service research report, check if the parsed structured fields are complete, and confirm that parsing time does not exceed the configured PARSE_FILE_TIMEOUT_SECONDS.
Initiate a simulated API call, pass a preset auto service detailed query, and check that the number of returned recall entries matches the configured Recall count.
Check the knowledge base sync logs, confirm that scheduled sync tasks execute according to the configured Knowledge base scheduled sync cycle.
Enter a low-relevance general industry query, check if the results are filtered by the similarity threshold, to verify the configured search accuracy.

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.