What the Data for This Category Looks Like
Data sources include public industry monitoring documents from the China Condiment Association, regular disclosure filings of listed condiment companies, sales ledgers from online and offline retail channels, and spot transaction quotes for bulk grain and oil raw materials. Update cycles are categorized as: daily (raw material quotes), weekly (retail sales data), monthly (overall industry operation data), and quarterly/annual (corporate financial reports and in-depth research reports). Document formats include structured CSV sales ledgers, PDF-format industry analysis reports, Excel-format corporate revenue details, and plain-text raw material quote sheets. Fields and units are as follows: product name, monthly shipment volume, unit selling price, raw material cost. Shipment volume is measured in tons, selling price in yuan per kilogram, and raw material cost in yuan per ton.
Constraints on Citation Sources and Provenance Workflow
Differing update cycles across data sources require the citation provenance system to accurately mark the collection time and update cycle of each cited content piece. This prevents using weekly retail sales data to support monthly investment research conclusions. Mixed structured and unstructured document formats require provenance support for both cell-level positioning in structured CSV tables and page plus paragraph positioning in PDF research reports. This covers citation sources across all supported formats. Differences in field naming across data sources—such as some retail ledgers using "shipment volume" while industry reports use "sales volume"—require the provenance system to configure unified field mapping rules. This avoids field confusion in investment research analysis. The daily update characteristic of raw material quotes requires provenance to link specific transaction dates and quote sources. This ensures the timeliness of cited content matches the time window of investment research analysis.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_top_k | Top 8–12 results | Condiment investment research data covers multiple dimensions including retail, financial reports, and raw materials. Sufficient candidate sources must be recalled to cover core information and avoid redundant interference |
similarity_threshold | 0.75–0.85 | The condiment industry has differences in common terminology and field expressions. This threshold filters irrelevant data while retaining cross-channel information for the same category |
source_citation_enable | Enabled | Investment research analysis requires clear data source labeling to comply with industry information standards, and to facilitate backtracking and verification of logic |
parse_table_cell_citation | Enabled | Most condiment data is presented in structured tables. Enabling cell-level provenance allows precise positioning of specific data rows and fields for cited content |
citation_newline_render | Auto-convert to line breaks | Resolves issues where original \n characters fail to render correctly, ensuring clear display of citation content for tables and multi-line items |
max_citation_tokens | 1200–1500 characters | The full information length of a single piece of condiment structured data is mostly under 1000 characters. This range fully retains content required for provenance |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Line break markers \n are displayed directly as text in the citation module of investment research responses, without proper line break rendering. Cause:
citation_newline_renderis not configured for auto-conversion mode. The system retains original line break markers instead of rendering them as page breaks. - Phenomenon: Field names shown in citation provenance do not match those used in investment research analysis. For example, "shipment volume" from ledgers is labeled "sales volume". Cause: Unified field mapping rules are not configured. The provenance system uses original data source field names directly without aligning industry terminology.
- Phenomenon: The number of recalled citation sources exceeds expectations, leading to redundant responses and confusing provenance information. Cause:
recall_top_kis set too high. The number of recalled entries is not adjusted based on the dimensional density of condiment data, introducing a large amount of non-core edge data.
How to Verify Proper Configuration
- Upload a condiment retail ledger CSV file, initiate a query asking for the monthly shipment volume of this product, and check if the citation module displays specific cell positions and source file names.
- Enter test text containing line break markers, and verify that the citation module in responses converts \n to proper line breaks.
- Adjust the recall count parameter, initiate a multi-dimensional investment research query, and confirm that the number of recalled sources matches the analysis requirements.
- Check the citation section of investment research responses, and confirm that all sources are marked with collection time and complete source information.
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.