What this category’s data looks like
This section outlines the data source and structure for tax-free financing daily reports. The data comes from financing application ledgers of tax-free merchants, local commerce bureau tax-free qualification filing databases, and merchant operating transaction statements. The system generates full current day’s data at a fixed time each day, and also supports appending temporary entries added on the same day. Each document uses a structured format, including fields such as merchant ID, tax-free qualification level, daily financing application amount, approval status, affiliated business district, and disbursement time. The amount unit is Renminbi yuan. Qualification levels use graded identifiers, and approval status uses standardized text enumerations.
Constraints on vector models and indexing
This section lists the constraints imposed by the data characteristics on vector model and indexing setup. The data uses a structured format and includes classification and numerical business fields. General text vector models cannot adapt to semantic encoding of non-text features. The setup requires a vector model that supports structured field mapping. Daily updates and temporary supplementary entries require the index to support incremental writing and incremental synchronization. This avoids resource consumption caused by full reconstruction. Clear business associations exist between fields — for example, merchant ID binds to qualification level. The vector index must retain associated semantics between fields to avoid semantic deviation from independent encoding. Individual data entries have short length but large batch sizes. The index sharding and writing strategy must adapt to high-frequency small-batch writing scenarios to ensure retrieval and writing stability.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Deduplication Fields | ["商户编号", "融资申请日期"] | Duplicate data in tax-free financing daily reports mostly comes from repeated submissions by the same merchant on the same day. Using these two fields can accurately identify duplicate entries and avoid accidentally deleting valid data |
| Recall Count | Top 10 entries | The core retrieval needs for tax-free financing daily reports focus on specific merchants or same-day financing updates. Too many recall results will introduce irrelevant entries and reduce retrieval efficiency |
| Similarity Threshold | 0.72–0.78 | The semantic similarity distinction for structured fields is relatively high. This range can filter low-match irrelevant daily report entries and retain valid retrieval results |
| Incremental Sync Interval | Every hour | Daily report data includes temporary entries added on the same day. Synchronizing every hour can ensure index freshness while avoiding resource waste caused by overly frequent synchronization |
| Vector Model Type | Lightweight model that supports structured field encoding | Tax-free financing daily reports include classification and numerical fields. General text models cannot adapt to semantic encoding of non-text features. Lightweight models can ensure retrieval speed |
| Index Shard Count | 4–6 shards | The daily data volume stabilizes at thousands to tens of thousands of entries. This shard count balances write speed and retrieval latency, adapting to high-frequency small-batch writing scenarios |
The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: The order of index retrieval results does not match the order of original document chunks, and some valid entries are accidentally deleted. Cause: Only a single field is used as the deduplication basis, without combining merchant ID and financing application date, resulting in valid entries from different merchants with the same amount being misjudged as duplicates.
- Phenomenon: The vector model call returns a 401 status code, and the console prompts authentication failure. Cause: The vector model’s API key and request domain name are not configured correctly, or the used key has not been granted vector encoding permissions.
- Phenomenon: Custom added vector model requests are forwarded to the large language model interface, and the vector encoding logic is not triggered. Cause: The dedicated call path for vector models is not checked in the channel configuration, and the vector model is mistakenly connected to the general large language model channel.
How to confirm correct configuration
- Upload one test tax-free financing daily report data entry, check the index generation log to confirm that the deduplication fields are correctly identified and applied.
- Initiate a retrieval request that includes a specified merchant ID, check the field matching degree of the returned results, and adjust the similarity threshold to a range that meets business requirements.
- Manually trigger the incremental sync task, wait for the sync to complete, then check that the number of entries in the index matches the volume of the original test data.
- Call the vector encoding interface to verify that the encoding results of structured fields are complete, with no field loss or encoding abnormalities.
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.