What the data for this category looks like
Cultural and entertainment products investment research data comes from multiple sources: industry association category monitoring reports, SKU sales and user review data from mainstream e-commerce platforms, licensing agreement information from copyright holders, new product announcements from offline exhibitions, and industry media review content. Update rhythms vary across data types: e-commerce sales data updates daily, new product licensing information syncs in real time as agreements are signed, industry weekly reports are released each week, and exhibition information updates according to event schedules. Each data entry includes fields such as SKU code, category name, material parameters, pricing range, user review keywords, and copyright expiration date. Material values primarily use text enumerations, pricing uses yuan per unit as the unit, and review content consists of unstructured short text.
Constraints imposed on database and operations workflows
Multiple heterogeneous data sources require the database access layer to support standardized mapping of multiple data formats. This prevents query errors caused by inconsistent field names or formats. Differentiated update frequencies require configured tiered synchronization strategies. Separate real-time sync for licensing information from scheduled sync for weekly report data. This balances real-time performance and operational resource usage. Mixed structured and unstructured field types require the database to support both relational queries and vector retrieval. This supports joint analysis of sales data and user reviews for investment research. High correlation between fields in a single data entry requires creating joint indexes to optimize query performance. This avoids excessive latency during multi-table joins.
Configuration recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_CONNECTION_POOL_SIZE | 5–10 | Cultural and entertainment products have a large number of SKUs but small per-entry data size. A small connection pool avoids excessive resource usage |
DB_QUERY_TIMEOUT | 15–30 seconds | Queries must retrieve data from multiple sources including e-commerce sales and industry reports. A longer timeout covers multi-table join queries |
RECALL_TOP_K | 8–12 entries | Investment research for cultural and entertainment products requires balancing multiple types of information including user reviews, sales data and industry updates. A moderate number of recall results covers core reference dimensions |
PARSE_DOC_SPLIT_SIZE | 600–800 characters | Product detail pages and review content for cultural and entertainment products have moderate length. This segment size preserves contextual relevance |
AUTO_DB_SYNC_CRON | 0 0 */6 * * * | Adapts to the update rhythms of daily e-commerce data updates and weekly industry report updates. Tiered synchronization balances real-time performance and resource consumption |
TOOL_CALL_MAX_RETRIES | 2 retries | Addresses occasional network fluctuations during database calls. A small number of retries reduces failure rates while avoiding duplicate requests that consume resources |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material formats, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- A database plugin call returns
400 Bad Request, with an error message related to unclosed single quotes. This occurs when unescaped single quotes exist in accessed data source fields, causing errors in database query statement concatenation. - A locally deployed FastGPT instance shows no output when calling a database connection. This occurs because the database access whitelist is not configured, or the container network for the local deployment does not map the database port, blocking the connection.
- The number of database query recall results does not match the configured value. This occurs because the
RECALL_TOP_Kparameter is not set correctly, or the recall logic does not filter duplicate SKU-related data, causing redundant results to occupy return quotas.
How to verify proper configuration
- Run a manual database synchronization task, and check the synchronization logs for field mapping failure prompts. Confirm that multi-source data field alignment meets expectations.
- Initiate an investment research-related tool call, and check the number and field completeness of returned results. Confirm that query timeout and recall count configurations are active.
- Simulate high-concurrency knowledge base query requests, and check database connection pool usage. Confirm that the connection pool size configuration does not trigger connection overflow.
- Check the running logs of scheduled synchronization tasks, and confirm that the automatic synchronization cycle configuration performs data updates as expected.
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.