Knowledge Base Retrieval and Recall for Snack Food Research

Data Characteristics of This Category

Snack food research report data sources include industry association public reports, leading brands’ annual or quarterly disclosure documents, offline retail monitoring datasets, and online e-commerce sales review documents. Two update cycles apply: industry overview reports are updated quarterly, leading brand disclosures sync with earnings cycles, and retail monitoring data updates more frequently. Most documents follow a four-section structure: industry overview, segment track performance, leading player updates, and consumption trends. Some documents include tabular sales data and detailed channel share breakdowns. Fields include brand revenue, sales volume, and number of terminal locations. Common units are RMB, tons, and count of terminal locations.

Constraints for Knowledge Base Retrieval and Recall

Diverse data sources with inconsistent update frequencies require knowledge bases to distinguish between static and real-time data sources, and match data timeliness weights during recall. Documents contain large amounts of tabular structured data. Chunk storage must avoid breaking field correspondence within tables. The large number of segment categories requires recall rules to bind snack food segment track tags, to avoid recalling content from other food and beverage categories. Unit differences across multi-source data require unified field unit calibration before retrieval, to prevent semantic matching bias.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`chunk_size`	800–1200 characters	Snack food research reports contain many short paragraphs and table rows. This range balances semantic completeness and chunk granularity
`chunk_overlap`	100–150 characters	Retains cross-row contextual coherence for structured data, avoiding damage to table field associations
`retrieve_top_k`	Top 8–10 results	Covers relevant research report content across multiple snack food segment tracks, meeting retrieval breadth requirements
`rerank_top_n`	Top 3–5 results	Focuses on core matching content, avoiding excessive redundant results interfering with retrieval goals
`parse_table_mode`	`structured`	Preserves field correspondence in sales data tables in research reports, improving retrieval accuracy for structured information
`rerank_enable`	`true`	Enables reranking functionality to optimize relevance ranking of recall results

The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material format, 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 Configuration Mistakes

Retrieval results return research report content from non-snack food categories. The cause is that no retrieve_filter recall filtering rule is configured, and no tags for snack food segment categories are bound, resulting in recall scope covering all food and beverage categories.
The exported dataset.csv only contains question-and-answer template formats, with no actual research report parsing content. The cause is that the "Export Full Knowledge Base" option is not checked. Only the preset question-and-answer pair template is exported, excluding parsed chunk data from uploaded documents.
Reranked return values for each retrieval result are false. The cause is that rerank_model_api_key is not configured, or the rerank_enable parameter is not set to true, causing the reranking function to not trigger normally.

How to Verify Configuration Is Correct

Upload a research report document for a snack food segment track, perform a retrieval, and check the data source tags of the recall results. Confirm only content related to that category is returned, and verify whether the retrieve_filter configuration is effective.
Perform a knowledge base export operation. Confirm the exported dataset.csv contains parsed research report chunk data, and that exported content includes content beyond the template format. Verify whether the "Export Full Text" option is enabled.
Deploy the reranking model, send a retrieval request, and check whether the rerank_score field exists in the returned results and has a reasonable value. Confirm that the rerank_enable parameter is enabled.
Adjust the chunk_size parameter, re-parse a research report document, and check whether the parsed chunk length matches the preset range. Confirm that the chunk configuration is effective.

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.