What the Data for This Category Looks Like
Snack food investment research data comes from multiple sources: monthly sales activity briefings from industry associations, weekly sales data from offline supermarket POS systems, quarterly financial reports of listed companies, public SKU sales and review data from e-commerce platforms, and daily quotes for upstream grain, oil and packaging raw materials. Data update cycles vary widely. Industry reports are released quarterly. Sales activity data is updated weekly. Raw material quotes are updated daily. Financial reports are updated irregularly based on their disclosure cycles. Individual documents mostly combine structured tables and paragraphs. They include fields such as SKU name, sales amount, month-on-month growth rate, raw material cost proportion, and more. Units include ten thousand yuan, ton, percentage, barcode number, and other standard units.
Constraints on Citation Source and Traceability
The multi-source and variable update cycle characteristics of snack food investment research data create multiple constraints for citation traceability. The time difference between data sources requires synchronous labeling of data collection time and publishing entity during traceability, to avoid confusing the timeliness of weekly sales activity data and quarterly industry reports. The large number of SKUs and detailed fields require precise binding of sales, cost and other fields for the target SKU during traceability, to prevent cross-category citation errors. Document formats vary significantly across sources. Structured financial reports and unstructured e-commerce reviews need different segmentation and extraction rules. Without these rules, the original source of cited content cannot be accurately located. Frequently updated raw material quote data requires separate traceability tags, to support differentiated management from low-frequency industry reports.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity threshold | 0.35–0.45 | There are many subcategories of snack foods, and similar SKU names are common. This range balances accuracy and recall scope, avoiding the introduction of irrelevant content or omission of subcategory data |
recall count | Top 8–12 entries | Individual investment research documents contain multiple sets of detailed SKU data. A small number of recalls avoids result redundancy while covering core key information such as sales and costs |
maxContext | 800–1200 characters | Snack food investment research documents are mostly structured fragments. This length can fully retain the associated data of a single set of SKUs, avoiding truncation of field information required for traceability |
PARSE_FILE_AUTO_TAG | Enabled | Automatically add tags such as industry report, sales activity data, raw material quote to documents from different sources, to facilitate filtering cited content by source type during traceability |
RECALL_SOURCE_SHOW | Enabled | Need to synchronously display the publishing entity and update time of the source in responses, to meet the traceability verification requirements of investment research scenarios |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Specific citation source fields (such as SKU barcode, release time) are not displayed in retrieval results. Cause: The
RECALL_SOURCE_SHOWconfiguration is not enabled, or document metadata fields are not retained during parsing. - Phenomenon: Target SKU-related data cannot be recalled even when the similarity threshold is set to 0.4. Cause: The threshold has not been adjusted for snack food subcategories, or pre-tokenization processing has not been performed for SKU names, leading to interference from similar names.
- Phenomenon: Core investment research data is truncated after the number of cited entries exceeds the preset limit, or the corpus returned by API calls cannot be associated with the original source. Cause: The values of
recall countandmaxContextare not configured correctly, or source metadata is not entered synchronously when uploading documents, resulting in missing identification information required for corpus traceability.
How to Verify Proper Configuration
- Upload a monthly sales activity data document for snack foods, initiate a retrieval request, and check whether returned results include metadata such as the document's publishing entity and update time.
- Enter a search term containing a specific SKU name, verify the matching degree of recall results, and adjust configuration items until matching results meet investment research requirements.
- Call the API to retrieve corpus, check whether returned results carry the document's source identification field, and confirm that traceability information displays normally.
- Test documents with different update frequencies, such as daily updated raw material quotes and quarterly financial reports, and confirm that retrieval results synchronously mark corresponding data timeliness 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.