What the data for this category looks like
E-commerce service yield and market data comes from e-commerce platform open trading APIs, structured reports exported from store backends, and market data APIs from third-party e-commerce operation tools. Data updates once daily, covering all transaction and revenue data from the previous calendar day. Documents use CSV or JSON format, split by store. Each entry includes fields such as store ID, transaction period, total transaction amount, number of completed orders, average order value, and net revenue. Field units are yuan, count, yuan, and yuan respectively, with no percentage-based units.
What constraints these characteristics impose on model access and configuration
E-commerce yield data updates daily on a T+1 cycle. Scheduled model trigger tasks must align with daily early morning update nodes, and avoid overly frequent calls. Data is split by store and includes many entries. Vector database retrieval rules must include a store ID filter to prevent cross-store invalid recalls. There are many structured fields with uniform units. Large model prompts must clearly specify value ranges and formats for fields, to avoid generating incorrect numerical content. Data volume grows with the number of stores. The number of recalled entries per batch must be limited to a reasonable range, to avoid exceeding context windows or causing timeouts.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_count | 8–12 entries | Yield data for e-commerce services has high information density per entry; too many recalls will exceed context window limits |
similarity_threshold | 0.72–0.78 | Structured data requires high field matching precision; a threshold that is too low will introduce irrelevant store data, while one that is too high will miss valid entries |
scheduled_task_trigger_time | 02:00 daily | E-commerce data typically completes summary updates for the previous day by 01:30 daily; this time ensures complete data is retrieved |
vector_db_shard_key | Store ID | E-commerce service data is split by store; sharding by store avoids cross-store invalid recalls and improves retrieval efficiency |
maxContext | 8000–12000 characters | The total length of structured data recalled in a single batch is relatively long; this adapts to the context processing capabilities of large models |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | When importing large batches of e-commerce transaction reports, parsing a single file takes a long time; this value prevents task interruption from mid-run timeouts |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: After switching the
vector_modelparameter, the task queue always displays "Waiting", and it is not possible to roll back to the original model in the interface. Cause: No breakpoint resume mechanism is configured for model switching, and no original index snapshot is retained during the full vector database index rebuild process. This makes recovery impossible after a switching failure. - Phenomenon: When calling a large model to generate yield broadcasts, the
LLM_model_response_emptyerror code is occasionally returned. Cause: Too many e-commerce data entries are recalled in a single batch, exceeding the currently configuredmaxContextlimit. This results in no valid output when the large model generates content. - Phenomenon: When importing e-commerce transaction reports in batches, fields for some stores are not parsed correctly. Cause: The
rag_parse_fieldsparameter is not specified as the required yield-related fields, leading to core data being missed during parsing.
How to Confirm the Configuration is Correct
- Manually trigger a data import task, and check that the parsing log includes all configured fields with no missing field prompts.
- Submit a query that includes a specific store ID, and verify that the recalled results only include relevant data for that store, with no cross-store invalid entries.
- Trigger the scheduled task three consecutive times, and confirm that all tasks complete within the configured trigger time, with no timeout errors.
- After switching the test vector model, verify that the task can normally rebuild the index and roll back to the original model, with no queue blocking issues.
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.