What the data for this category looks like
Data sources for cultural and entertainment products research reports include public reports from industry associations, segmented analysis from securities firm consumer research teams, and sales movement monitoring documents from e-commerce platforms. Update cycles fall into three categories:
- Full industry deep reports released quarterly
- Channel sales and supply chain data updated monthly
- Sales movement data for popular SKUs updated weekly
Document structures typically include category breakdowns, supply chain cost analysis, market sales movement data, and competitor dynamic comparisons. Some documents contain nested tables and product display images. Fields include shipment volume, revenue-related numerical items, with units such as ten thousand pieces, ten thousand yuan, and similar units. Some documents include specific product parameters such as SKU codes and tag prices.
What constraints do these characteristics impose on the knowledge base retrieval and recall link
The multi-source and multi-update frequency characteristics of cultural and entertainment products research reports require the knowledge base to support an import method combining incremental synchronization and full updates, to avoid repeatedly loading high-frequency weekly updated data. Nested tables and images in documents require the parsing link to support structured table extraction and image OCR, otherwise core data content will be lost. The long paragraphs and segmented field characteristics of research reports require segment length to adapt to complete category analysis units, avoiding truncation of paragraphs containing key data. Large differences in keyword density across research reports from different sources require the retrieval link to adjust matching thresholds, to avoid recalling irrelevant light industry category data or missing valid content of segmented categories.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_MAX_SIZE | 200 MB | Cultural and entertainment products research reports often contain multi-page images and nested tables, resulting in large single-file size. 200 MB covers most batch-imported research report documents |
chunk_size | 1000–1200 characters | Research reports contain long paragraphs of industry analysis and table breakdowns. This segment length preserves complete category analysis units and avoids truncating key data |
similarity_threshold | 0.72–0.78 | The keyword density of cultural and entertainment products research reports is relatively high. A threshold that is too low will recall irrelevant category data, while a threshold that is too high will miss relevant segmented reports |
recall_top_k | Top 8 entries | Research report content covers multi-dimensional data. Recalling 8 entries can cover valid information from different chapters and avoid the one-sidedness of single-entry recall |
PARSE_TABLE_ENABLE | Enabled | Cultural and entertainment products research reports contain a large number of tables with sales and channel data. Enabling this option extracts structured table content and improves retrieval accuracy |
IMAGE_OCR_ENABLE | Enabled | Research reports contain images such as e-commerce traffic charts and product trend charts. Enabling OCR extracts text within images and supplements retrieval dimensions |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: After importing a Feishu document, only the link is returned during retrieval, with no specific text content. Cause: The public access permission for the Feishu document is not enabled, or the parsing link fails to correctly extract rich text content.
- Phenomenon: A document is retrieved and identified, but no specific answer is returned. Cause: The
similarity_thresholdis set too high, so the retrieved documents do not meet the content matching threshold, or core paragraphs related to questions and answers are truncated during segmentation. - Phenomenon: After importing a docx format document, table content cannot be retrieved. Cause: The
PARSE_TABLE_ENABLEconfiguration is not enabled, or nested tables in the document are not correctly recognized by the parsing engine.
How to confirm the configuration is correct
- Upload a typical cultural and entertainment products research report in docx format, check the segmented content after parsing, and confirm that tables and image text have been properly extracted.
- Enter the test keyword "blind box shipment volume", check the number and matching degree of retrieved results, and adjust
similarity_thresholdto a range that meets business requirements. - Import a research report document in Feishu format, verify that specific text content within the document is returned during retrieval, and that only links are not returned.
- Test retrieval of long documents, confirm that core analysis paragraphs are not lost after segmentation, and that core query requirements are covered.
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.