What the data for this category looks like
The data for e-commerce service intelligent due diligence reports comes primarily from operating ledgers of partner e-commerce stores, compliance filing documents, and user feedback records. Data is updated daily with same-day operating details, and compliance qualification archives are updated quarterly. Document structures include structured store basic information tables, transaction detail lists, and unstructured user review texts and qualification scan files. Fields include unique store identifiers, operating categories, total transaction counts, compliance voucher numbers, user feedback keywords, and more. The statistical dimensions of some fields vary based on the store’s operating category.
What constraints these characteristics impose on knowledge base retrieval and recall
E-commerce due diligence data has a high proportion of structured content, and field dimensions vary by category. This requires the retrieval process to support precise matching by specified fields, to avoid irrelevant results being included due to fuzzy recall. Data is updated at a relatively high frequency, so an incremental synchronization mechanism must be configured to ensure recalled content matches the store’s current operating status. Unstructured user review texts are short in length but large in volume, so a reasonable segment length must be set to avoid overly long single segments that reduce recall accuracy. Additionally, stores in different operating categories have significantly different fields. Irrelevant knowledge base entries must be filtered by category dimension before recall to narrow the retrieval scope.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall Count | Top 10-15 results | E-commerce due diligence data has a large number of entries. Too many recalled results increase context processing load, while too few may miss key operating or compliance information |
Similarity Threshold | 0.75-0.85 | Most e-commerce due diligence data fields require precise matching. A threshold that is too low will introduce redundant data from unrelated stores, while a threshold that is too high will cause failure to recall critical information such as compliance vouchers |
Segment Length | 800-1200 characters | Unstructured review texts in e-commerce due diligence are mostly short fragments. Segments that are too long will mix in irrelevant semantic content, while segments that are too short will destroy the complete meaning of reviews |
Incremental Sync Interval | Every 6 hours | E-commerce operating data is updated at a relatively high frequency. An interval that is too long will cause recalled content to lag behind the store’s current status, while an interval that is too short will increase server sync load |
Multi-Field Recall Switch | Enabled | E-commerce due diligence data includes multi-dimensional fields such as store ID, operating category, and transaction count. Enabling this switch allows matching across multiple fields simultaneously, improving recall accuracy |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | E-commerce due diligence includes large files such as qualification scan documents. A 600-second timeout setting ensures complete parsing of large files and avoids parsing interruptions |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, 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
- Issue: All e-commerce qualification scan image files in the knowledge base fail to load, and the interface returns a 404 status code. Cause: Cross-domain access permissions for image resources are not configured, preventing FastGPT from pulling image files from the e-commerce service backend interface.
- Issue: When there are no matching due diligence entries in the knowledge base, the workflow still calls the large language model to generate a response, returning content unrelated to the target store. Cause: No termination trigger rule is set for retrieval when the knowledge base is empty, or the trigger condition is configured incorrectly.
- Issue: After the "Question Optimization" interface switch is turned off, recall results are still supplemented with information outside the knowledge base. Cause: The global context completion setting is not turned off synchronously, or automatic completion node configurations remain in the workflow.
How to Confirm Proper Configuration
- Upload an e-commerce store operating ledger file, check if the parsed fields fully cover the preset retrieval dimensions, and confirm that segments and parsing results match the configured requirements.
- Simulate a retrieval request containing a unique store identifier, verify that the number of recalled entries and similarity score fall within the preset configuration range.
- Manually clear the knowledge base test data, trigger the retrieval process, and confirm that the workflow terminates and returns a preset prompt when no matching entries exist.
- Check the knowledge base image resource access logs, confirm there are no 404 error reports, and that FastGPT can normally pull qualification scan document files.
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.