What the data for this category looks like
Financial report data for the snack food industry comes primarily from regular reports and temporary announcements publicly disclosed by domestic and overseas stock exchanges. Updates occur on a fixed schedule: quarterly, semi-annually, and annually. Temporary announcements are triggered by business events such as new product launches or channel adjustments. Documents include structured financial statements, management discussion and analysis, channel and SKU sales performance data, and other modules. Fields cover revenue, attributable net profit, inventory turnover days, sales per square meter per store, and more. Typical units include ten thousand yuan, days, square meters, and similar measurements.
What constraints these characteristics impose on vector models and indexing
Snack food financial reports have significant variations in document length. The management discussion and analysis module can reach ten thousand words. Structured tables contain multiple columns of detailed operational data. Updates happen at a high frequency based on quarters. These requirements mean vector models must adapt to text segments of varying lengths, and support separate vectorization of structured tables. Indexes must support incremental updates to match high-frequency update cycles. They must also handle semantic matching for a large number of detailed fields, and prevent generic indexes from missing core operational data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Adapts to the paragraph length of management discussion and analysis sections in snack food financial reports. Prevents overly fragmented segments that break semantic coherence, or overly long segments that reduce vector encoding accuracy |
UPLOAD_FILE_MAX_SIZE | 500 MB | Matches the typical size of a single annual financial report PDF for listed snack food companies. Prevents upload timeouts |
RECALL_TOP_K | Top 8–12 results | Core metrics for snack food financial reports (such as quarterly revenue, sell-through rate) are concentrated in a small number of key paragraphs. Too many recall results introduce irrelevant content |
VECTOR_SIMILARITY_THRESHOLD | 0.72–0.85 | Industry terms for the snack food sector (such as "sales per square meter per store", "sell-through rate") have relatively high semantic similarity. A threshold that is too low introduces irrelevant recall results, while a threshold that is too high misses relevant content |
INCREMENTAL_INDEX_ENABLE | Enabled | Financial reports update on a fixed quarterly schedule. Incremental indexing reduces the time and resource usage of repeated vectorization |
PARSE_TABLE_ENABLE | Enabled | Snack food financial reports contain large numbers of structured sales and channel data tables. Separately vectorizing tables improves the accuracy of metric retrieval |
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. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Vector calculation scores are abnormal. All recall results have similarity scores close to each other and fall outside the typical range. The cause is that the
PARSE_TABLE_ENABLEconfiguration is not enabled. Generic vector models cannot distinguish the semantics of numerical fields in structured tables in financial reports, leading to systematic bias in similarity calculation. - Vectorization process times out. Tasks remain stuck in indexing for long periods. The cause is that a reasonable segment length is not set per the
PARSE_CHUNK_SIZEconfiguration. A single large document is submitted for vectorization without splitting, exceeding the system's default parsing timeout threshold. - After importing 100,000 CSV-formatted financial report data entries, only more than 90,000 entries appear after vectorization. The cause is that the
PARSE_SKIP_EMPTY_FIELDconfiguration is not enabled. The system automatically filters invalid data containing empty SKU codes or revenue fields, leading to a reduction in the total number of entries.
How to confirm configurations are set correctly
- Upload a single quarterly financial report PDF, review the parsed segment results. Confirm that each segment has complete semantics, with no key content truncated or redundant concatenation.
- Run an incremental indexing task, compare the time taken for the first full index and subsequent incremental indexes. Confirm that the incremental indexing function operates correctly.
- Test core metric retrieval. Enter keywords such as "quarterly revenue" and "sell-through rate". Adjust
RECALL_TOP_KandVECTOR_SIMILARITY_THRESHOLDto ensure that recall results include the corresponding paragraphs in the financial report. - Check the vectorization task logs. Confirm that there are no error records for empty field filtering, and match the actual number of imported data entries.
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.