What the data for this category looks like
Data sources include daily financing application records, payment settlement statements, order transaction data from e-commerce platform merchants, and loan and repayment receipts from cooperating financial institutions. Full data synchronization for the previous day runs every early morning, and incremental supplementary updates are supported. Each document covers a single merchant’s daily financing-related record, including fields such as merchant unique identifier, total order amount (unit: yuan), financing application quota, loan time (format: YYYY-MM-DD HH:MM:SS), repayment term (unit: days), annualized fee rate (unit: %), and order remarks.
What constraints do these characteristics impose on the vector models and indexing link
The daily batch update feature requires indexes to support incremental updates to avoid performance loss caused by full reconstruction. There are many structured fields including numeric fields such as amount and time, which requires vector models to adapt to structured data encoding, or preprocessing that includes field extraction and normalization. The number of fields per document is fixed and highly correlated, so index sharding should be divided by merchant ID or date to reduce cross-shard query overhead. Data volume fluctuates with e-commerce promotional activities, so indexes must have elastic scaling capabilities to handle peak write requests.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 200–300 characters | Adapts to the structured short text and limited remark content of e-commerce financing daily reports, avoiding loss of key field information due to overly long segments |
vector_index_batch_size | 500–1000 items/batch | Balances the speed of batch data import and interface stability, avoiding request limit errors triggered by excessively large single submission volumes |
retrieve_top_k | Top 10–20 items | Financing-related associated information is usually concentrated in a small number of nearest neighbor results; excessive recall increases subsequent processing and display burdens |
similarity_threshold | 0.75–0.85 | Distinguishes valid associated records from irrelevant data, adapts to the semantic similarity range of structured fields, and reduces false recalls |
index_refresh_interval | 3600 seconds | Matches the daily data update rhythm, ensures the timeliness of index data, and avoids excessive delays |
incremental_index_enabled | Enabled | Supports incremental index updates, avoids performance overhead caused by daily full reconstruction, and adapts to the batch update characteristics of daily reports |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The batch import interface returns a
413 Request Entity Too Largeerror. Cause: Thevector_index_batch_sizeparameter is not configured, and the number of documents submitted in a single batch exceeds the interface's default limit, resulting in an overly large request body. - Phenomenon: When querying financing records across dates, the returned results mix non-current-day data. Cause: A unified index is not used for sharding by date fields, and instead an independent index is created for each daily report, causing queries to traverse all indexes and results to not be filtered by date.
- Phenomenon: After directly modifying vector fields via MongoDB, query results are not updated synchronously. Cause: Vector indexes rely on a dedicated storage engine. Directly modifying the native MongoDB collection does not trigger index reconstruction, resulting in inconsistent query results and actual data.
How to confirm the configuration is correct
- View the vector index refresh logs, confirm that the incremental update task executes at the configured interval, and verify that the task trigger time matches the data update time.
- Execute a query for a single merchant's financing records, check that the number of recalled results matches the configured recall quantity, and confirm that the similarity scores meet the configured threshold requirements.
- Try to add a test record via the index management interface provided by the platform, confirm that the vector index is updated synchronously, compare the modification results in the native storage, and check for any synchronization issues.
- View the monitoring data of the batch import interface, confirm that no configuration-related errors are returned.
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.