What the Data for This Category Looks Like
Data sources for e-commerce service research reports include public e-commerce industry research reports, official industry data disclosed by e-commerce platforms, and aggregated materials from third-party e-commerce monitoring institutions. Update frequency follows two schedules: industry-level research reports are updated quarterly or monthly, while merchant operation data is updated daily. Document structure includes report title, publishing entity, publishing time, core industry market indicators, segmented category performance, user behavior characteristics, and trend analysis modules. Fields include indicator name, corresponding value, statistical cycle, and units such as yuan, units, and person-times.
Constraints for Knowledge Base Retrieval and Recall
Multi-source data with varied update frequencies must be split into knowledge base partitions by data type. This prevents mixing old and new data, which reduces retrieval accuracy. Documents include multiple modules. Retrieval and recall processes must retain contextual associations between modules to avoid semantic breaks from truncation. Rich field dimensions require support for field-specific matching during retrieval. This enables accurate matching of user-specified indicators or statistical cycles. Merchant-level data has a high update frequency. Recall processes must prioritize the latest updated content to ensure result timeliness.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | E-commerce research reports contain multi-module content. The segment length adapts to cross-module semantic associations, avoiding truncation of the binding relationship between core indicators and analysis |
recall_top_k | Top 10–15 results | E-commerce research reports have a large number of segmented categories. Sufficient recall results are needed to cover different dimensions, avoiding omission of key segmented data |
similarity_threshold | 0.75–0.85 | The repetition rate of indicator names in e-commerce research reports is high. A higher threshold is required to filter low-match irrelevant documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Single industry research report files have large sizes. Allowing large file uploads avoids the hassle of splitting uploads |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large research reports takes a long time. Avoiding early timeouts that cause parsing failures |
enable_field_retrieval | Enabled | E-commerce research reports contain multi-dimensional fields. Enabling field retrieval allows accurate matching of user-specified indicators or cycles |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: A large amount of irrelevant content appears in search results after uploading research reports, and the number of recalled results is far lower than expected. Cause: Documents are not split according to the chapter structure of research reports. Uploading complete long documents directly causes semantic breaks during segmentation, making it impossible to match the core indicators of user queries.
- Phenomenon: A
504 Gateway Timeouterror occurs during parsing. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted. Parsing large research reports takes longer than the default threshold, resulting in a timeout. - Phenomenon: Outdated data accounts for a high proportion of search results, and the latest quarterly market data cannot be obtained. Cause: Knowledge base partitions are not divided by update frequency, and the configuration for filtering recalled results by release time is not enabled.
How to Verify Proper Configuration
- Upload a standard e-commerce research report, review the parsed segmented content. Confirm that segments do not truncate the binding relationship between core indicators and analysis, and check that the segment length configuration matches the document characteristics.
- Initiate a query that includes specific indicators, review the number of recalled results. Confirm that the recall count configuration covers the required dimension range.
- Upload two research reports on the same topic with different update times, initiate a query and check the sorting of results. Confirm that the latest published content is returned first, and verify that the data freshness configuration is active.
- Initiate a query that includes a specified field, confirm that search results only return documents containing that field. Verify that the field retrieval configuration is enabled.
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.