What the data for this category looks like
Data sources for e-commerce service intelligent due diligence include operating ledgers from e-commerce platform backends, consumer review data, supply chain fulfillment vouchers, and industry compliance filing documents. Update cycles are divided by operating nodes: real-time synchronization of transaction records, daily updates of user feedback, and quarterly updates of compliance archives. Document structures include structured fields such as unique shop identifiers, monthly GMV, and customer unit price, and unstructured text such as collections of user reviews and compliance self-check explanations. Field units cover currency units, transaction counting units, time units, and more.
What constraints these characteristics impose on the vector models and indexing link
Standardized formatting requirements for structured operating fields mean vector models must support numerical feature vectorization, to avoid semantic bias from general-purpose models. High-frequency updated transaction records require index configurations with incremental synchronization mechanisms, to reduce resource consumption from full index rebuilding. Unstructured user reviews have wide fluctuations in length, so adaptive segmentation thresholds must be set to avoid overloading single segments or making segments too fragmented. Fixed metadata tags for compliance filing documents require indexes to support targeted recall by tag dimension, which improves retrieval efficiency for due diligence reports.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
embedding_model | Set to baidu_embedding_v1 | Adapts to the mixed vectorization needs of structured fields and unstructured text for e-commerce due diligence data |
chunk_size | 800–1200 characters | Adapts to the average length of user reviews and compliance documents, balancing semantic completeness and retrieval accuracy |
index_incremental_sync | Enabled | Adapts to high-frequency updates of real-time transaction records, reducing resource usage from full index rebuilding |
recall_top_k | Top 10–15 results | Balances recall coverage and retrieval response speed for due diligence reports |
vector_index_shard_num | Calibrated to 4–8 shards based on data volume | Adapts to the index storage needs of multi-shop, multi-category e-commerce services, improving concurrent retrieval capability |
embedding_batch_size | 32–64 | Adapts to call rate limits for external APIs, avoiding errors such as 429 or 404 |
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. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Issue: A
404error is returned after configuringbaidu_embedding_v1. Cause: The interface address and key permissions for the Baidu embedding service were not correctly configured in the API management platform, causing the interface to fail to resolve properly. - Issue: Local vector models deployed without a GPU environment cannot be added to the knowledge base. Cause: The CPU inference mode of the model was not enabled, or the local container was not allocated enough CPU cores and memory resources.
- Issue: The number of vector recall results does not match the configured
recall_top_kvalue. Cause: The index shard configuration is unreasonable, leading to result truncation during cross-shard retrieval, or the similarity threshold is set too high, filtering out valid recall content.
How to confirm the configuration is complete
- Upload a single e-commerce operating ledger document, check if the vector generation log shows the correct model call identifier and embedding results.
- Initiate a retrieval request for due diligence data, verify that the number of returned results matches the configured
recall_top_kparameter. - Trigger an incremental index synchronization, check if the index update log only synchronizes new data and does not perform a full index rebuilding.
- Export part of the vector index data, verify that the metadata fields match the identification information of the original documents.
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.