What the data for this category looks like
Credit reports are generated by central bank credit centers, local credit reporting operators, and market-oriented credit reporting service providers. Official credit reports are updated in batches monthly. Market-oriented credit reports can be synchronized weekly or in real time. The document structure includes four modules: basic subject information, credit transaction details, public records, and query history. Fields cover credit balance (unit: yuan), number of overdue periods (unit: times), credit limit (unit: ten thousand yuan), and other items. Field types include three categories: numerical, text, and date. The content of a single document is relatively lengthy.
What constraints these characteristics impose on knowledge base retrieval and recall
Dispersed data sources make it difficult to align fields of credit reports from different channels. Retrieval must prioritize matching standardized fields first. Differences in update frequencies require the knowledge base incremental update strategy to adapt to the synchronization rhythm of different data sources. This avoids data lag or redundancy. A single lengthy document requires adjusting the chunking strategy to prevent semantic unit breakage. Inconsistent field units require calibration during preprocessing. Otherwise, numerical matching deviations will occur during retrieval. Multiple credit reports for the same subject contain large amounts of duplicate content, leading to redundant recall results.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | A single chunk of a credit report must cover complete credit details or public information units to avoid semantic breakage after splitting |
recall_top_k | Top 8–12 results | Risk control queries need to cover multi-dimensional credit records. Too few results will miss key credit information, too many will increase context redundancy |
similarity_threshold | 0.75–0.85 | Most credit report field matching needs are precise matching. A threshold that is too low will introduce irrelevant results, too high will miss valid matches |
rerank_top_k | Top 3–5 results | Risk control scenarios need to prioritize returning the most relevant core credit data to avoid non-core content interfering with decision-making |
incremental_update_interval | Official credit reports: 7 days, market-oriented credit reports: 1 day | Match the update rhythm of different data sources to avoid data lag or repeated update triggers |
The parameter values provided on this page are common recommended 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 the settings.
Three common misconfigurations
- Phenomenon: When testing knowledge base search, target credit report fragments cannot be recalled, and the returned results are empty or irrelevant to the query keywords. Cause: Preprocessing is not performed on non-standardized fields of credit reports such as institution names and overdue record descriptions, leading to deviations in semantic matching.
- Phenomenon: When batch uploading 100 5KB credit report files, nearly half show training exception status. Cause: Some files have encryption, format damage, or missing fields, leading to interruption of the parsing process.
- Phenomenon: The number of references returned by retrieval results exceeds expectations, and the model output has a context overflow error. Cause: The
recall_top_kandrerank_top_kparameters are not set reasonably, and the values exceed the maximum context length supported by the model.
How to verify successful configuration
- Upload a single typical credit report, check the chunked content parsed by the knowledge base, and confirm that the chunks do not split core credit units.
- Initiate a retrieval test with precise field keywords, and check that the similarity of the recalled results is within the preset reasonable range.
- Configure an incremental update task, check the synchronization logs, and confirm that the update frequency matches the update rhythm of the corresponding data source.
- Batch upload multiple credit reports of the same type, check the training status and parsing results, and confirm that there are no abnormal errors.
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.