What Data for This Category Looks Like
Vehicle industry investment research data primarily comes from automakers’ public financial reports, Ministry of Industry and Information Technology new vehicle declaration catalogs, industry association evaluation reports, dealer channel feedback, and official technical documents. Update cycles vary: new vehicle parameter information is updated irregularly alongside release cycles, quarterly financial reports are updated on a fixed schedule, and industry dynamics are adjusted in real time as market conditions change.
There are two core document structure types. One is structured parameter tables, containing fields such as vehicle model, CLTC range, maximum power, torque, with units including kilometers, kilowatts, newton-meters, etc. The other is unstructured long text, such as annual strategic plans and in-depth evaluation articles, with wide variation in paragraph length.
Constraints Imposed on Retrieval and Recall Workflows
The coexistence of structured parameter tables and unstructured long text requires the retrieval pipeline to distinguish between exact matching and semantic recall logic. This prevents key information from being missed by single-recall methods.
Format differences across multiple data sources require unit normalization and field mapping first. Without these steps, retrieval bias will occur for the same parameter with different units.
Differing update cycles require incremental synchronization to support configurable trigger frequencies based on data type. This avoids ineffective synchronization or data lag.
Segmentation processing for long text documents must balance semantic integrity. Otherwise, critical parameters or chapter association information will be truncated.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Vehicle industry investment research documents include long financial report paragraphs and structured parameter groups. This range preserves complete semantics of single chapters or parameter groups, avoiding truncation of critical information |
similarity_threshold | 0.75–0.85 | High precision is required for vehicle parameter searches. This range filters irrelevant competing product data while covering parameter comparison needs for similar models |
recall_top_k | Top 8–10 results | Investment research analysis requires multi-dimensional reference. This quantity balances information richness and redundancy, avoiding excessive results that interfere with AI generation |
parse_metadata_enable | Enabled | Vehicle data includes metadata such as vehicle model, release date, and automaker name. When enabled, this metadata can be used to precisely filter recall results and narrow retrieval scope |
rerank_enable | Enabled | Data from multiple sources is prone to semantic confusion. Reranking improves the relevance ranking of recall results and optimizes retrieval experience |
sync_interval | Every 6 hours | New vehicle releases are irregular, but industry dynamics update frequently. This interval ensures timeliness of knowledge base data while reducing synchronization resource consumption |
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. Individual analysis is required for specific cases. Testing on local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Symptom: When calling the create file collection API, the
metadatafield passed in does not appear in retrieval results. Cause: Metadata indexing configuration is not enabled, or the passed metadata key name does not match preset fields in the knowledge base. - Symptom: RAG retrieval results include redundant chapter title prefixes, leading to repeated information in responses. Cause: Redundant chapter titles were not stripped during document parsing, or segmentation parameters do not adapt to the title structure of long documents.
- Symptom: Retrieval results include reference links from original documents, which harms response readability. Cause: The source link display configuration for retrieval results is not disabled, or the system prompt does not explicitly hide links.
How to Verify Proper Configuration
- Upload a DOCX document containing structured parameter tables. Confirm parsed segments retain parameter group integrity, and verify segmentation configuration meets requirements.
- Submit a retrieval request with specific vehicle parameters. Confirm relevance of recall results meets expectations, and verify similarity threshold and recall count configurations are reasonable.
- View metadata fields in retrieval results. Confirm passed
metadatacontent is correctly indexed, and verify metadata configuration is enabled. - Submit questions outside the knowledge base scope for testing. Confirm the AI only uses knowledge base content to generate responses, and verify system prompts and retrieval scope configurations are correct.
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.