What This Category of Data Looks Like
Data sources include offline vehicle 4S shop maintenance work order systems, used car transaction platform vehicle condition reports, and vehicle registration information from transportation management departments. Operational data is synchronized daily, and industry benchmark datasets are updated monthly. Document structure includes structured fields and unstructured attachments. Structured fields contain Vehicle Identification Number (17-character string), maintenance project name, service unit price, service duration, and service provider qualification level. Unstructured attachments include work order scans, repair photos, and similar materials. Field units are uniformly set to RMB yuan and hours. The Vehicle Identification Number uses a fixed 17-character format.
How These Data Characteristics Impact Vector Models and Indexing
The high share of structured fields and fixed-format unique identifiers requires vector models to support field-level weight configuration. This prevents irrelevant fields from interfering with semantic encoding. Unstructured attachments have large individual file sizes and a high total count. This requires chunking strategies to adapt to long-text splitting rules, and limit the number of characters per chunk to stay within the model's input limit. Daily incremental updates of operational data require the indexing system to support incremental write logic. This avoids resource waste caused by full index rebuilding. Monthly updated industry benchmark datasets require batch indexing tasks to support configurable scheduling cycles. This adapts to low-frequency batch processing requirements. The unique identifier field must be associated with vector data as metadata. This enables precise subsequent recall based on vehicle information.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
embedding_model | bge-large-zh-1.5 or open-source models with the same embedding dimension, adapted to platform configuration version v4.8.7 | Adapts to Chinese semantic coverage of automotive service due diligence data, and the embedding dimension of this model is compatible with most vector databases |
chunk_size | 800–1200 characters | Adapts to the text length of unstructured maintenance work orders and industry reports, avoids excessively long single chunks that exceed model input limits, and ensures semantic integrity |
chunk_overlap | 100–150 characters | Retains semantic connection between chunks, avoids context breaks caused by splitting, and adapts to the accuracy of vector recall for long texts |
vector_top_k | Top 8–12 results | Balances recall accuracy and response speed. The associated dimensions of automotive service due diligence data are relatively concentrated, so excessive recall results are not needed |
similarity_threshold | 0.72–0.78 | Filters low-similarity irrelevant data, and adapts to the matching accuracy of high-semantic fields such as vehicle identifiers and service projects |
enable_incremental_index | Enabled | Adapts to daily incremental synchronization requirements for operational data, and reduces resource consumption from full indexing |
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 individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Only a single vector model can be selected for a dataset, and multiple vector storage groups cannot be mapped to a single data source. Cause: The multi-vector table function is not enabled in the dataset's advanced configuration, or vector models are not bound separately for different business fields.
- Phenomenon: Vector data generated using model A locally shows deviations or empty recall results after being uploaded to a server where model B is used. Cause: The embedding dimensions and text splitting rules of model A and model B are not aligned, leading to incompatible vector spaces and inability to match valid results.
- Phenomenon: Files containing multiple repair photos and long-text work orders remain in the indexing state for a long time, with no progress feedback. Cause: Reasonable
chunk_sizeandembed_batch_sizeare not configured, leading to model inference timeout or memory overflow, and task blocking.
How to Confirm Configuration Is Complete
- Log in to the dataset's configuration interface, and verify that parameters such as
embedding_model,chunk_size, andchunk_overlapmatch the preset configuration exactly. - Upload a single typical mixed document with structured and unstructured content, and check whether the index task progress bar updates normally without abnormal stagnation.
- Construct a test query containing a Vehicle Identification Number and maintenance project, and verify whether the recall results include matching content for the target fields.
- Submit an incremental data synchronization task, and check whether the index logs only record vector generation and write operations for new data, with no full index trigger records.
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.