What the data for this category looks like
E-commerce service research report data mainly comes from e-commerce platform official operation backends, public analysis documents from third-party e-commerce monitoring institutions, and operation data independently submitted by merchants. Updates follow a monthly regular rhythm, with temporary special reports added during major e-commerce promotion nodes such as 618 and Double 11. Each document typically includes fields such as category GMV, visitor volume across channels, customer unit price, 直通车投产比, and other metrics. Units include ten thousand yuan, person-times, yuan, times, and others. The document structure is divided into three parts: core data summary, detailed category breakdown, and trend analysis.
Constraints Imposed on Citation Source and Traceability
The data sources of e-commerce service research reports are scattered. Update rhythms are flexible. Fields use different units. These factors create three core constraints for citation traceability. First, accurately link the original source identifier of each data point. Distinguish between e-commerce platform official data, third-party monitoring data, and merchant-submitted data to avoid confusing data subjects during traceability. Second, record the release time and type of each report. Differentiate between regular monthly reports and promotion special reports to prevent using expired or misaligned timely content. Third, add the field unit information to the traceability snippet. Metrics such as GMV and customer unit price use different units like ten thousand yuan and yuan. This step avoids data interpretation bias. Additionally, the fixed document structure requires clarifying the chapter that contains the cited snippet during traceability. This improves traceability accuracy.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall count | Top 15-25 entries | E-commerce service research reports contain multi-dimensional detailed data. Sufficient candidate snippets must be covered to match query needs, while avoiding too many recalled snippets exceeding the large model's context token limit |
Similarity threshold | 0.75-0.85 | Query keywords for e-commerce research reports are mostly detailed categories or operation metrics. A threshold that is too low will introduce irrelevant category data. A threshold that is too high may miss accurately matched detailed snippets |
Rerank result count | Top 5-8 entries | Control the final number of cited snippets to avoid exceeding answer token limits, while retaining core high-correlation data |
Chunk size | 800-1200 characters | Single e-commerce research report documents have long length. Too short segments will destroy the integrity of data fields. Too long segments will increase single-segment token consumption |
enable_source_metadata | Enabled | Retain metadata such as report release time and source type to meet timeliness and subject verification needs during traceability |
Citation Fragment Context Retention | 50 characters before and after | Fields in e-commerce research reports are closely linked. Retaining a small amount of context can clarify the analysis dimension to which the snippet belongs, improving traceability clarity |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Analyze specific issues on a case-by-case basis, and it is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: Setting
Recall countto 2000 causes the token consumption of a single answer to exceed 3000, triggering an error prompt for model context overflow. Reason: A single e-commerce service research report contains multi-dimensional detailed data. Too many recalled snippets will accumulate a large number of tokens, far exceeding the context limit of general large models. - Phenomenon: The display text of citation sources uses Chinese formatting, which cannot adapt to multilingual business scenarios. Reason: The prompt template for the knowledge base retrieval node was not modified. The default Chinese configuration is loaded, and the prompt format for the corresponding language was not adjusted.
- Phenomenon: The knowledge base snippet cited in the answer does not include the core e-commerce operation indicators required by the query. Reason: The
Similarity thresholdis set too high, causing highly relevant matching snippets that rank high to be filtered out because they do not meet the threshold requirements, and snippets with weaker relevance are returned instead.
How to Verify Correct Configuration
- Initiate a query for e-commerce detailed category operation data. Check the traceability list below the answer, and confirm that each traceability snippet marks the report source, release time, and field unit.
- Check the token consumption statistics of the answer. Confirm that no context overflow error occurs, and the token consumption meets business expectations.
- Adjust relevant retrieval parameters, initiate multiple queries, and verify that the number and relevance of cited snippets meet the configuration requirements.
- Switch the query language, and confirm that the prompt text for citation sources adapts to the currently used language format.
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.