What the data for this category looks like
Data sources include publicly filed periodic reports of listed commercial vehicle enterprises, monthly production and sales statistics documents from industry associations, and internal enterprise operation ledgers. Updates follow a schedule of quarterly, semi-annual, and annual periodic reports, plus temporary announcements triggered by major operating events. Document structures cover production and sales scale, revenue breakdown, cost composition, R&D investment, and channel operation data. Field units include units, ten thousand yuan, hundred million yuan, and days. It includes detailed metrics such as per-vehicle profit and order delivery cycle.
What constraints these characteristics impose on vector models and indexing
Commercial vehicle financial reports are generally long. A single periodic report may contain tens of thousands of characters of detailed data. This places higher requirements on the context window and parsing duration of vector models. Multi-source data has varying formats. Public reports are mostly in PDF format. Internal ledgers are mostly in table or CSV format. Indexes must support mixed format parsing. Field units are strongly correlated with detailed metrics. Vector indexes must retain field-level metadata to support precise recall. Update schedules are uneven. Real-time indexing needs for temporary announcements coexist with batch indexing needs for periodic reports. Indexes must support flexible switching between incremental and full indexing. Financial report data varies significantly across commercial vehicle sub-categories. Indexes must support vector partitioning by sub-category to improve recall accuracy.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | A single segment of commercial vehicle financial reports must cover complete production and sales or cost indicator units, to avoid splitting that breaks data correlation |
chunk_overlap | 100–150 characters | Financial report fields are closely linked. Overlapping segments preserve contextual connections for cross-segment indicators |
similarity_top_k | Top 8–12 results | Commercial vehicle financial reports have many detailed indicators. A sufficient number of segments must be recalled to cover query needs |
rerank_top_n | Top 3–5 results | Focus on core indicator segments to avoid redundant results interfering with financial report analysis |
INDEX_INCREMENTAL_ENABLE | Enabled | Supports incremental indexing for temporary announcements, adapting to the irregular update schedule of commercial vehicle financial reports |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single commercial vehicle financial report documents have long lengths. Sufficient time must be reserved for parsing and vectorization |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: No optional models appear in the text understanding model dropdown when creating a knowledge base. Cause: The embedding model type tag is not correctly configured in the model channel. Only the model file is uploaded without associating the corresponding model classification.
- Phenomenon: The number of segments increases abnormally after uploading a single document, with duplicate indexed segments appearing. Cause: No reasonable
chunk_overlapparameter is set, or the parsing process triggers duplicate segment logic, causing the same content to be split and stored multiple times. - Phenomenon: Only single-file re-vectorization is supported, and batch vector model training cannot be triggered. Cause: The target range parameter for batch updates is not configured, or the batch indexing function of the knowledge base is not enabled, resulting in only a single-file adjustment entry being available.
How to confirm the configuration is correct
- Navigate to the model channel page, view the type tag of the uploaded embedding model, and confirm that it matches the business requirements.
- Upload a test commercial vehicle financial report segment, and check whether the number of parsed segments matches the settings of the
chunk_sizeandchunk_overlapparameters. - Initiate a batch vectorization task, check whether the task log includes the target document list for batch updates, and confirm that the batch indexing function is enabled.
- Initiate a query test, and check whether the number of recall results matches the settings of the
similarity_top_kandrerank_top_nparameters.
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.