What this category of data looks like
Data sources are publicly disclosed annual and semi-annual financial reports from large state-owned banks, plus operation ledgers submitted per regulatory requirements. Update frequency follows this schedule: full annual financial reports are updated once per year, semi-annual financial reports once every six months, and quarterly operation data once per quarter. Document structure contains modules such as financial statements, operation details, risk disclosures, and others. A single complete financial report document can be dozens of pages long. Fields include total assets, net profit, core tier 1 capital net amount, and similar items. Most units are hundreds of millions of yuan or ten thousands of yuan. Field naming complies with unified domestic financial regulatory standards.
What constraints do these characteristics impose on deployment and upgrade?
Multi-source data from public disclosures and regulatory submissions requires configuring compliant data import channels during deployment to prevent sensitive data leaks. The multi-cycle update rhythm requires deploying scheduled incremental sync tasks. Upgrades must be compatible with different data update frequencies for annual, semi-annual, and quarterly cycles. The dozens-of-page length of single documents requires configuring longer timeout thresholds for the parsing process, and adapting to long document segmentation rules. Unified regulatory field specifications require strict adherence to financial regulatory naming standards for vector database and knowledge base field mappings, to avoid data matching deviations. Large-scale financial report data volume requires reserving sufficient vector storage and recall computing resources during deployment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single financial report documents have long length; conventional parsing duration may exceed default values |
maxContext | 8000–16000 characters | Financial reports include multi-module content; sufficient context is needed to retain complete business logic |
RECALL_TOP_K | 10–15 entries | Financial reports have dense fields; sufficient recall range is needed to cover relevant data |
VECTOR_STORE_TYPE | milvus | Vector retrieval performance for large-scale financial report data is better than other storage types, and meets multi-cycle incremental update requirements |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Complete financial report scans or structured export files may reach large sizes |
SYNC_CRON_EXPR | 0 0 2 * * * | Incremental data sync must be completed during non-business peak hours, to adapt to batch update rhythms of quarterly, semi-annual, and annual cycles |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. Testing on internal samples is recommended before finalizing settings.
Three Common Mistakes
- Symptom: Vector search returns insufficient or empty results. Cause: Using PostgreSQL as the vector storage, which cannot adapt to the large-scale data volume and multi-cycle update requirements of large state-owned bank financial reports.
- Symptom: Docker container pull fails, with logs showing image pull timeout. Cause: Correct image acceleration source is not configured, and the default pull address has restricted access.
- Symptom: Long document parsing times out, with task status showing failure. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted; the default value is insufficient to complete full parsing of dozens-of-page financial reports.
How to Verify Correct Configuration
- Upload a single complete financial report document, check whether the parsing task status shows success, and verify that the parsed text segments cover all modules of the financial report.
- Initiate a scheduled sync task, check whether the number of newly added documents in the vector database matches the volume of data to be synced.
- Initiate a financial report analysis query, verify that the returned results include the user-specified financial report fields, and confirm that the relevance of vector recall meets expectations.
- Check container runtime logs, confirm that the connection status of dependent services such as Milvus and MongoDB is normal, and no error messages are present.
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.