What the data for this category looks like
Auto service investment research data comes from several sources: automakers’ public financial reports, industry association monthly and quarterly reports, after-sales maintenance operation ledgers, auto parts supply chain quotation documents, new vehicle launch announcements, and OTA upgrade logs. Documents fall into three categories: structured, semi-structured, and unstructured. Structured documents such as maintenance quotation sheets include fields including vehicle model ID, maintenance items, labor fees, and accessory unit prices. Supported units include yuan, kilometers, and newton-meters. Semi-structured documents are industry analysis reports. Unstructured documents are vehicle technical manuals. Update rhythms vary widely: after-sales data updates daily, industry reports release quarterly, and new vehicle information syncs immediately upon launch.
What constraints these characteristics impose on the knowledge base retrieval and recall link
Large volumes of structured data with clear units require the retrieval pipeline to support field-level precise matching and unit verification. This prevents mismatches between values and their associated units. Wide variation in update rhythms requires support for configuring incremental update triggers by data source type. This avoids ineffective full synchronization. Broad range of document lengths: long documents such as full vehicle technical manuals coexist with short documents such as single maintenance quotations. Recall logic must adapt to different length truncation rules. Some documents include multimodal content. The retrieval pipeline must support fusion recall of multimodal features to cover retrieval needs for image-based parameter documents.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_top_k | Top 10-15 results | Auto service investment research needs to cover multiple data sources including vehicle parameters, industry reports, and supply chain data. 10-15 results can ensure coverage of core relevant information |
similarity_threshold | 0.72-0.85 | Auto service data has high requirements for field accuracy. This interval can filter low-relevance content from non-target vehicle models and non-corresponding maintenance items |
chunk_size | 800-1200 characters | Too long segments in auto service technical manuals and industry reports will lose context. 800-1200 characters balances semantic completeness and retrieval accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing some long documents such as full-series vehicle technical manuals takes a long time. 600 seconds ensures complete parsing |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports uploading large collections of industry reports and multimodal image packages to meet batch data import needs |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, 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 configuration mistakes
- Phenomenon: Retrieval results fail to match the precise parameters of the target vehicle. A large amount of irrelevant content from other models of the same brand is returned. Cause: No field-level precise matching rules are configured. Only global full-text retrieval is used, and no limited matching is applied to fields such as vehicle model ID and maintenance items.
- Phenomenon: Knowledge base training tasks fail. The interface displays a "parsing timeout" error and returns status code 504. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. The parsing time of long documents such as full-series vehicle technical manuals exceeds the default threshold. - Phenomenon: Multimodal documents cannot be retrieved or recalled after upload. Cause: Multimodal feature extraction configuration is not enabled, and corresponding multimodal vector indexes are not generated. This prevents image documents from participating in recall.
How to confirm the configuration is properly set
- Upload a single long document. Check the execution status of the parsing task to confirm that parsing completes within the configured timeout threshold.
- Initiate a retrieval request for a specified field, such as vehicle model ID. Verify that returned results only include document content matching that field.
- Upload a multimodal document containing images. Confirm that retrieval results associate with the multimodal feature information of that document.
- Perform a full knowledge base clearing operation. Confirm that no residual documents remain in the knowledge base after the operation completes.
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.