What this type of data looks like
The data sources for financing daily reports include group-level financing management systems, subsidiary financial ledgers, and public market financing disclosure platforms. The update cycle is full daily data aggregation after daily market close. The document uses a multi-level JSON format. The top level contains the group's total financing amount and total number of financing transactions. Lower levels are categorized by subsidiary. Each subsidiary entry includes five core fields: financing entity name, financing amount (unit: ten thousand CNY), financing term (unit: month), disbursement date (format: YYYY-MM-DD), and fund usage.
Constraints for model access and configuration
The multi-level subsidiary data structure requires configuring subject-based grouping recall rules to avoid mixing financing entries from different subsidiaries. The daily update cycle requires matching sync task trigger intervals to the data update period, preventing duplicate syncs or missed daily data. The multi-field structured data requires vector model segmentation and recall logic to adapt to field dimensions, ensuring key information such as financing amount and disbursement date is prioritized for recall. The multi-source data access requirement requires configuring authorization and address parameters for multiple API sources, adapting to system interfaces of different internal subsidiaries.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
batch_sync_interval | 86400 seconds | Matches the daily update cycle of financing daily reports, ensures sync of the latest full dataset each day |
embedding_batch_size | 64 | Adapts to batch processing of multi-subsidiary, multi-field data, prevents task interruptions caused by single-batch data overload |
similarity_threshold | 0.75 | Distinguishes group-level summary and subsidiary-level detailed financing information, avoids recalling redundant duplicate data for the same entity |
rerank_top_n | 8 | For multi-entity financing entries, prioritizes returning results with the highest match to the query topic |
api_request_timeout | 300 seconds | Addresses delays during parallel pulling of data from multiple subsidiary data sources, prevents sync task failures due to timeout |
custom_api_auth | {"Authorization": "Bearer {{authorization}}"} | Adapts to the authentication requirements of financing daily report APIs, obtains authorization tokens from internal financing systems |
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 configuration mistakes
- Phenomenon: The interface returns 401 Unauthorized or 404 Not Found errors. Cause: Failed to correctly obtain the API gateway address and authorization token from the internal financing system, or the token has expired and is invalid.
- Phenomenon: The vector model reports repeated errors, with empty recall results or results that do not match the query topic. Cause: Failed to adapt to the field dimensions of financing daily reports, incorrectly used segmentation lengths and recall thresholds for general documents, leading to key information not being properly vectorized.
- Phenomenon: GPU utilization during model inference is below 30%, while a single CPU core utilization reaches 100%. Cause: Failed to configure GPU memory allocation parameters for the model, causing the model to only use CPU cores and fail to fully utilize GPU resources.
How to confirm successful configuration
- Manually trigger a data sync task, check the sync logs to confirm that financing data for all subsidiaries has been successfully imported, with no failed entries.
- Initiate a query about the financing status of a specific subsidiary, verify that recall results are grouped by subsidiary dimension and include core fields such as financing amount and disbursement date.
- Check the vector model's operation monitoring, confirm that GPU memory utilization is within a reasonable range, and CPU core utilization is balanced with no single-core overload.
- Check the operation logs for API authorization configuration, confirm that no 401, 403 or other identity verification 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.