What Data for This Category Looks Like
The data for intelligent vehicle due diligence reports primarily comes from official manufacturer announcements, Ministry of Industry and Information Technology motor vehicle product announcements, third-party motor vehicle inspection institution reports, and dealer sales vehicle ledgers. The data update rhythm changes with vehicle model iterations. Full parameter batches are updated when new models launch, while dynamic parameters such as range and warranty for existing regular models are updated quarterly. Most documents use structured fields, including clear fields like Vehicle Identification Number (VIN), curb weight, driving range, battery capacity, and warranty period, with accompanying unstructured inspection instructions and compliance assessment content. The number of pages per document varies widely. Counting or testing with internal samples is recommended before finalizing settings.
Constraints on Vector Models and Indexing From These Characteristics
High structured field proportion and multiple numerical units require vector models to adapt to both structured numerical mapping and natural text semantic extraction, avoiding vector bias in professional parameters from single models. Dynamically updated non-fixed batches cause fluctuating indexing pressure, requiring flexible switching between incremental and batch indexing. Wide variation in single document length requires retaining context association for the same field during segmentation, avoiding damage to parameter integrity from splitting. Vehicle Identification Number (VIN) serves as the unique identifier, requiring a unique key constraint in the index to prevent repeated indexing of reports for the same vehicle model.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Vehicle reports contain both structured parameter details and lengthy inspection instructions. This range retains context association for single parameters and avoids splitting that damages field integrity |
vector_model_type | bge-large-zh-v1.5 | This model has higher vector mapping accuracy for professional terminology in the motor vehicle domain, and can adapt to semantic extraction for both structured numerical values and natural text |
index_batch_size | 50–100 documents per batch | Single vehicle report data volume fluctuates significantly. Small batch submissions avoid overloading indexing node resources and reduce timeout probability |
recall_top_k | Top 10 results | Due diligence reports need to cover multi-dimensional parameters including power, chassis, and compliance. Excessive recall increases context redundancy and reduces the accuracy of generated results |
similarity_threshold | 0.72–0.78 | Similarity matching for structured numerical fields requires a higher threshold to avoid accidental recall of parameters from non-target vehicle models |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Text parsing for large-volume vehicle inspection reports takes a long time. This value covers the parsing cycle for most single documents |
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. Targeted analysis is required for specific issues. Testing with internal samples is recommended before finalizing settings.
Three Common Misconfigurations
- A single file remains in the indexing state for an extended period without completing indexing progress updates. This occurs when the
index_batch_sizeparameter is not configured, and full indexing is submitted directly after single file parsing, leading to excessive node resource occupation and task backlog. - A 60-second timeout error occurs when switching knowledge base indexing. This occurs when the
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted, and the default value cannot cover the parsing and indexing time for large-volume vehicle inspection reports. - Non-target vehicle model parameter information appears in retrieval results. This occurs when the
similarity_thresholdvalue is too low, leading to recall of non-target reports with low similarity and failure to filter invalid content.
How to Confirm Proper Configuration
- View indexing task logs to confirm that parsing time for single vehicle reports is lower than the value set for
PARSE_FILE_TIMEOUT_SECONDS. - Conduct retrieval tests using the VIN field of a single vehicle model, and verify that the similarity scores of recall results fall within the range set for
similarity_threshold. - Batch import 10 or more due diligence reports for different vehicle models, and confirm that indexing completion time meets expectations with no task backlog.
- Check the knowledge base indexing status panel to confirm no unfinished parsing or indexing tasks, with status showing ready.
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.