What the data for this category looks like
E-commerce service investment research data comes from e-commerce platform API interfaces, merchant backend export files, industry public documents, and supply chain integration data. Update frequencies fall into three categories:
- Basic product information updates daily
- Transaction and search data updates hourly
- Industry documents update weekly
Document structures include structured tables (product lists, transaction reports) and unstructured text (user reviews, industry analysis reports). Fields include product code, selling price, sales volume, search volume, and user review content. Units correspond to yuan, pieces, and times.
What constraints these characteristics impose on knowledge base retrieval and recall
E-commerce service investment research data includes structured product fields and unstructured user reviews and industry documents. Structured fields require support for exact matching. Non-long text needs proper segmentation to avoid redundant recall. Frequently updated transaction and product data requires an incremental indexing mechanism. This prevents excessive time spent on full indexing. Cross-border business scenarios have mixed Chinese and English documents. These require multi-language retrieval support. Large industry reports may exceed default upload limits. Adjust relevant parameters to fit business needs.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2048 MB | Fits upload needs for single industry reports and bulk product documents in the e-commerce industry, prevents large file upload errors |
PARSE_CHUNK_SIZE | 800–1200 characters | E-commerce service data includes long-text user reviews and structured product descriptions. This segment length balances recall accuracy and context completeness |
RECALL_TOP_K | Top 8–12 results | E-commerce investment research covers multi-dimensional product and transaction data. Too many recall results increase context pressure, too few miss critical information |
SIMILARITY_THRESHOLD | 0.72–0.78 | Fits semantic matching accuracy for product attributes and user reviews in e-commerce scenarios, prevents incorrect recall of unrelated product data |
ENABLE_INCREMENTAL_INDEX | Enabled | E-commerce product and transaction data updates frequently. Incremental indexing significantly reduces indexing time and avoids long waits for index completion |
ENABLE_MULTILANG_RETRIEVE | Enabled | Cross-border e-commerce scenarios have mixed Chinese and English documents. This configuration supports multi-language retrieval needs and resolves issues where Chinese and English documents cannot be recalled |
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 specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Uploading industry report PDFs larger than 3 MB triggers upload errors. Cause: The
UPLOAD_FILE_MAX_SIZEparameter was not adjusted to a value suitable for large files. The default parameter cannot handle large documents in e-commerce scenarios. - The knowledge base stays in an indexing state with no progress in Docker deployment environments. Cause: Incremental indexing configuration was not enabled. Full indexing takes too long to process frequently updated e-commerce data, and no reasonable
PARSE_FILE_TIMEOUT_SECONDSparameter was configured, leading to timeout interruptions. - Chinese queries cannot recall English product documents in the knowledge base. Cause: Multi-language retrieval configuration was not enabled. Retrieval only targets Chinese semantics, so it cannot match English document content.
How to Confirm the Configuration Is Set Correctly
- Upload a test document with a volume that meets business expectations, confirm there are no upload errors, and verify that the upload parameter configuration matches business requirements.
- Submit a test query that includes structured product fields and unstructured text, check that the number and semantic matching of recall results meet business expectations.
- Submit a test query that includes English keywords, confirm that English documents can be recalled normally.
- Simulate a single data update task, confirm that the indexing task completes within a reasonable time frame.
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.