What the data for this category looks like
Cultural and entertainment products investment research data mainly comes from public reports of light manufacturing industry associations, brand supply chain ledgers, e-commerce platform sales backend data, and IP licensing cooperation documents. Update rhythms vary: e-commerce sales data is updated daily, industry prosperity reports are updated weekly, and supply chain inventory data is updated every two weeks.
Document structures include structured SKU lists, semi-structured industry analysis PDFs, and unstructured new product development interview records. Core fields include SKU code, authorized IP name, unit production cost, retail guide price, monthly sales volume, and inventory turnover days. Corresponding units are none, string, yuan, yuan, unit, and day, respectively.
What constraints these characteristics impose on knowledge base retrieval and recall
Structured SKU data requires exact matching, so retrieval must support field-level filtering. Multiple data sources with different update frequencies require configuring incremental sync tasks to avoid excessive time spent on full updates. Authorized IP names have common aliases, so synonym expansion rules must be configured. Numeric fields such as inventory turnover days and monthly sales volume require support for numeric range retrieval. Long-text industry reports account for a relatively high proportion, so context splicing logic during segmented recall is required to avoid semantic breaks.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Cultural and entertainment products data includes long-text industry reports and short-text SKU information. This range balances semantic integrity for both document types |
recall_top_k | Top 10 entries | Investment research for cultural and entertainment products requires balancing SKU details and industry trends. 10 entries can cover multi-dimensional retrieval results |
similarity_threshold | 0.72–0.80 | Retrieval queries for cultural and entertainment products have many aliases, such as IP names. This threshold balances recall accuracy and coverage |
incremental_sync_interval | Every 12 hours | E-commerce sales data is updated daily, supply chain data is updated weekly. A 12-hour interval adapts to the update rhythms of multiple data sources |
parse_file_timeout_seconds | 600 seconds | Long-text industry analysis PDFs may take a long time to parse. This duration avoids parsing timeouts |
enable_field_filter | Enabled | Cultural and entertainment products data includes structured fields such as SKU codes and authorized IPs. Field filtering improves retrieval accuracy |
The parameter values provided on this page are standard recommendations used as starting points for configuration. Actual values are influenced by material formats, data volume and business rules. Specific issues require targeted analysis. It is recommended to conduct tests on relevant samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After importing knowledge base content with Chinese-English translations, the large model does not call corresponding Chinese knowledge base entries when generating responses. Cause: Chinese word segmentation and mixed Chinese-English recall rules are not configured, causing English translated text to fail to be correctly associated with Chinese retrieval terms.
- Phenomenon: During workflow testing based on question classification, only the first question triggers knowledge base reference, and subsequent questions do not. Cause: Session context knowledge base association continuation logic is not configured. Subsequent questions do not inherit the knowledge base binding relationship of the current session.
- Phenomenon: After the knowledge base import is completed, retrieval returns 0 matching results. Cause: Ingested data does not follow field naming specifications, causing the retrieval engine to fail to recognize core retrieval fields such as SKU codes and authorized IPs.
How to Verify Successful Configuration
- Upload a single long-text industry report, and check the character count of the parsed segmented results to confirm they fall within the
chunk_sizeconfiguration range. - Enter a retrieval query containing SKU codes and authorized IP names, check the field matching degree of retrieval results to confirm the
enable_field_filterconfiguration is active. - Conduct a multi-round question classification test, enter different types of investment research questions in sequence, check whether knowledge base reference is triggered in each round to confirm the session context association logic works normally.
- Check the knowledge base synchronization logs to confirm the incremental synchronization task automatically executes according to the cycle configured in
incremental_sync_interval.
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.