What Data for This Category Looks Like
Condiment investment research data primarily comes from publicly monitored reports by industry associations, regular financial reports of listed condiment enterprises, sampling data from terminal retail channels, upstream supply chain raw material price disclosures, and industry research reports. Update frequencies vary: industry association reports are released quarterly, enterprise financial reports are updated quarterly and annually, retail terminal data is updated weekly, and raw material price data is updated daily. Most documents use structured tables paired with text analysis, including fields such as category production capacity, raw material costs, channel share, and revenue scale. Field units include tons, yuan per kilogram, ten thousand yuan, and others. Some documents also include specification parameters and sales proportion details for individual SKUs.
Constraints Imposed on Vector Models and Indexing
The varied update frequencies of condiment investment research data require indexes to support concurrent incremental and full updates, to synchronize different data sources updated daily, weekly, or quarterly. The high proportion of structured table documents requires vector models to encode structured fields, to avoid information loss from only encoding plain text. The increased feature density from multi-category and multi-SKU detailed data requires adjusting the output dimension of vector models to fit segmented scenarios. Differences in fields across data sources require index configurations to support custom field mapping rules, to unify vector encoding logic for data from different sources. Frequently updated retail and raw material data places clear constraints on the write concurrency of vector databases and index refresh speed.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Fits the mixed structure of structured tables and text analysis in condiment investment research documents, avoids splitting table-related information across segments, and retains sufficient contextual semantics |
PARSE_TABLE_ENABLE | Enabled | Condiment investment research documents contain large numbers of structured tables for production capacity, costs, and channel shares. Enabling this parameter fully extracts field and numerical information from tables |
vector_model | Local deployment of m3e-base | Fits the encoding needs of Chinese investment research text, supports integration with local vector models, and meets requirements for localized data storage |
index_refresh_interval | 1 hour | Fits the update rhythm of weekly retail data and daily raw material data, balances index real-time performance and server resource usage |
recall_top_k | Top 10 entries | Condiment investment research requires covering associated data across multiple categories and SKUs. Recalling 10 entries balances relevance and information comprehensiveness |
custom_field_mapping | Map by category, raw material, and channel grouping | Unifies field formats across industry association reports, enterprise financial reports, and retail data, eliminating encoding deviations from different data sources |
The parameter values provided on this page are all common recommended starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: After creating new data in the knowledge base, it remains in the "indexing" state for a long time with no progress updates. Cause: The
index_refresh_intervalparameter is not configured, or the set refresh interval is too long, causing incremental index tasks to pile up without execution. - Symptom: After configuring custom index rules, search results do not match expected field content. Cause: The
custom_field_mappingparameter is not correctly configured, and fields from different data sources are not mapped to a unified format, leading to deviations in vector encoding matching. - Symptom: After connecting a custom vector database, search returns empty results or abnormal entry counts. Cause: The vector database collection shard count matching the dimension of condiment data is not set, or table parsing is not enabled, resulting in missing structured information in vector encoding.
How to Confirm Successful Configuration
- Check the collection data volume in the vector database, confirm it matches the number of uploaded condiment investment research documents, and verify that the vector dimension of each entry matches the output dimension of the selected model.
- Conduct a search test, input keywords related to categories or raw materials, check if returned results include corresponding structured tables and text analysis content, and verify that table parsing and field mapping are operational.
- Review index task logs, confirm that incremental update tasks run regularly at the configured refresh interval, with no piled-up errors.
- Adjust the recall count parameter, verify that the number of search results changes with the configuration, and confirm that the recall rule is active.
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.