Knowledge Base Retrieval and Recall for Automotive Service

What Data for This Category Looks Like

Data for this category primarily comes from offline automotive service shop maintenance work order systems, vehicle factory maintenance archives, local automotive service business district public quotation platforms, and industry regulatory public notification platforms. It provides data support for financial institutions conducting due diligence for automotive credit and auto insurance underwriting.

Update frequency varies by data type:

Offline work order data syncs in real time when service is completed
Business district quotes update daily
Regulatory public notification information releases irregularly alongside regulatory actions

Individual due diligence report documents include fields such as shop qualification number, service covered vehicle model range, average maintenance time per service, consumable purchase unit price, and customer positive review rate. The qualification number is an 18-digit string, the vehicle model range uses a string format, time is measured in hours, and unit price uses yuan per item.

Constraints on Knowledge Base Retrieval and Recall

The above data characteristics impose multi-dimensional constraints on the knowledge base retrieval and recall link.

Real-time synced offline work order data requires retrieval systems to support incremental index updates. This avoids delays caused by full index rebuilds and ensures the timeliness of due diligence reports.

Daily updated business district quotes require a short index refresh cycle. This ensures retrieval results match the latest market prices and meets financial institutions' real-time risk control needs.

Some fields, such as qualification numbers and consumable models, have high exact matching requirements. Adjust the exact matching weight of retrieval to reduce interference from fuzzy matching, and avoid recalling irrelevant non-target documents.

Individual documents contain fields with multiple units. Complete unit standardization during preprocessing ensures field alignment during retrieval, preventing invalid recall results caused by unit differences.

Additionally, due diligence reports require multi-field combined queries. The recall link must support multi-condition joint retrieval to improve query accuracy.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Automotive service due diligence reports often contain long-text maintenance records; 300 seconds enables complete parsing
`maxChunkSize`	`800–1200 characters`	Documents contain multi-field combined content; this length ensures a single chunk includes complete field information and avoids splitting errors
`RECALL_TOP_K`	`Top 8 entries`	Due diligence reports need to cover multi-dimensional data such as qualifications, quotes, and service capabilities; 8 entries meets conventional query needs
`SIMILARITY_THRESHOLD`	`0.75–0.85`	Fields have high exact matching requirements; a higher threshold filters low-relevance recall results
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Individual reports contain large numbers of maintenance attachments and historical records; 50 MB meets conventional upload needs
`INDEX_REFRESH_INTERVAL`	`Once daily`	Business district quote data updates daily; daily refresh ensures retrieval results match the latest market information

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 individual analysis. It is recommended to test on relevant samples before finalizing configuration.

Three Common Misconfigurations

After calling the knowledge base retrieval interface, the returned document access link displays Only support .txt when clicked. The cause is that the uploaded due diligence report includes non-pure text accessory files. The system only generates accessible parsing links for pure text files.
When setting a custom segmentation rule, the segment length parameter value does not align with the document structure. This leads to incomplete field combinations being split from a single document chunk, preventing accurate matching of shop qualification or consumable quotation information during retrieval.
A Request failed with status code 400 error occurs when uploading batch due diligence reports. The cause is either a single file exceeding the UPLOAD_FILE_MAX_SIZE configuration threshold, or the file containing a special encoding format not recognized by the system.

How to Verify Successful Configuration

Upload a standard automotive service due diligence report. Check if the parsed document chunks contain complete field information. Adjust maxChunkSize until no field splitting errors appear in single chunks.
Run a simulated retrieval. Enter a query containing shop qualification and consumable unit price. Verify that the number of recall results matches the RECALL_TOP_K configuration.
Wait for the configured INDEX_REFRESH_INTERVAL cycle to complete. Run another retrieval for a query related to latest business district quotes. Confirm that results match the latest market data.
Upload a test file that exceeds the conventional size. Verify that the system triggers a file size limit intercept prompt, confirming the configuration is active.

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.