What the data for this category looks like
Data sources for e-commerce service research reports include real-time sales data from e-commerce platform merchant operation backends, industry monitoring reports from third-party e-commerce data service providers, and monthly or annual operation review documents from brand merchants. Update cycles fall into two categories: internal brand operation data is updated daily, while in-depth industry research reports are updated weekly or monthly.
Documents include clear core metric fields, such as category transaction volume, average spend per customer, repurchase rate, traffic channel share, and SKU sales turnover rate. Corresponding units are RMB yuan, yuan per person, percentage, share of visitor trips, and individual units.
Document lengths vary widely. Some are single-day sales briefings with hundreds of characters, while others are industry analysis reports with tens of thousands of characters. Files also come in multiple formats, including Excel files with multiple worksheets and multi-dimensional documents.
What constraints do these characteristics impose on the vector models and indexing workflow?
Multi-source data formats for e-commerce service research reports require the indexing process to support unified parsing of structured tables, unstructured text, and multi-worksheet files. Differences in update frequencies require a switching mechanism for incremental and full indexing, to accommodate daily updated operation data and periodically updated industry reports.
Metrics include clear numeric indicators. Vector models must support semantic encoding of numeric features to avoid losing the business meaning of the metrics. The wide range of document lengths requires a chunking strategy that adapts to both short texts and long documents. Additionally, the large number of files in batch indexing requires balancing indexing efficiency and hardware resource usage.
How to set configurations
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
chunk_size | 800–1200 characters | Balances semantic integrity of short texts in e-commerce research reports and chunk indexing density of long documents, adapting to research report content of varying lengths |
index_batch_size | 32–64 items per batch | Adapts to 8c16G host configurations without GPUs, prevents out-of-memory errors caused by overly large single-batch indexing data |
recall_top_k | Top 10–15 results | Matches retrieval needs focused on core metrics in e-commerce service research reports, avoids excessive irrelevant recalls or insufficient coverage of relevant content |
PARSE_EXCEL_SHEET_ENABLE | true | Adapts to the characteristic that e-commerce research reports often contain structured data across multiple worksheets, ensuring content from all worksheets is parsed |
embedding_timeout | 600 seconds | Covers vector encoding time for long documents, prevents indexing process interruptions due to timeouts |
embedding_model | Calibrated for business scenarios, prioritize vector models that support numeric feature fusion | Meets the encoding needs of large numbers of numeric metrics in e-commerce research reports, preserving the business semantics of the metrics |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, 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 configuration mistakes
- Scenario: Knowledge base indexing fails when deploying a PostgreSQL database via Docker, on an 8c16G virtual machine without a GPU. Cause: The
index_batch_sizeparameter was not adjusted, and the single-batch indexing data volume exceeded the host's memory limit. - Scenario: After importing Excel files and multi-dimensional documents from a folder, indexing status shows an error, and no retrieval results are returned. Cause: The
PARSE_EXCEL_SHEET_ENABLEconfiguration was not enabled, so multi-worksheet content in Excel files was not parsed, and parsing adaptation rules for multi-dimensional documents were not configured. - Scenario: An error occurs when calling the embedding model via OneAPI, and indexing cannot be completed. Cause: The
embedding_modelparameter was not configured separately, the default OneAPI call chain was used, and the non-standard embedding interface format was not adapted.
How to confirm the configuration is correct
- Upload a single e-commerce research report Excel file, check if the parsed text includes field content from all worksheets, to confirm the
PARSE_EXCEL_SHEET_ENABLEconfiguration is active. - Initiate a batch indexing test, observe the host's memory usage, to confirm the
index_batch_sizeconfiguration adapts to the current hardware resources. - Initiate a retrieval request, verify that the number of returned results matches the preset recall rules, to confirm the
recall_top_kconfiguration is correct. - Call the embedding model interface separately, check if the returned vector dimensions match the model's preset standards, to confirm the
embedding_modelconfiguration is correct.
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.