What the data for this use case looks like
The data required for pre-existing condition determination comes from three primary sources: hospital visit records, medical insurance settlement databases, and historical claim archives. Update schedules have two categories: real-time synchronized outpatient and emergency records, and daily batch-synchronized inpatient settlement data. Document structure mixes structured fields and unstructured text. Structured fields include condition name, diagnosis date, treating hospital code, and medical insurance payment category. Unstructured text consists of outpatient or inpatient summaries. Field units follow general medical industry standards: diagnosis dates use ISO 8601 formatted date strings, hospital codes are 6-digit numeric strings, and medication records are free text.
What constraints these characteristics impose on deployment and upgrade
The interface formats and update frequencies of the three data sources differ. During deployment, adaptation rules for multi-source data access must be configured. During upgrade, version changes of different data sources must be supported. The mix of structured and unstructured data requires enabling both vector recall and structured field matching logic. During deployment, parameter thresholds for both parsing types must be configured separately. During upgrade, linkage rules between the two parsing methods must be preserved. Fields include medical-specific codes and date formats. During deployment, field mapping rules must be configured. During upgrade, mapping relationships must be revalidated to prevent missing or misaligned fields. Data volume grows with the number of claim cases. During deployment, expansion space for index shards must be reserved. During upgrade, sharding strategies must be adjusted to accommodate increased data volume.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Matches the typical size of medical record documents related to pre-existing condition determination, avoids parsing timeouts for large files, and aligns with the file upload limit of FastGPT v4.8.21-fix |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Covers parsing time for long medical record summaries, prevents recall failures caused by parsing timeouts |
RECALL_TOP_K | Top 10 entries | Covers multiple visit records for a single patient, balances recall accuracy and inference latency |
SIMILARITY_THRESHOLD | 0.75–0.85 | Accurately matches condition names and diagnosis times, filters low-correlation visit records, and reduces misjudgment risks |
INDEX_SHARD_COUNT | 2–4 | Adapts to single-node memory resources, facilitates subsequent shard expansion based on data volume, and optimizes index query performance |
INCREMENTAL_UPDATE_INTERVAL | Every hour | Matches the batch update frequency of medical insurance and visit data, balances real-time performance and server resource usage |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, 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 errors
- An
EMFILE: too many open fileserror occurs. The cause is failure to adjust the file handle limit for the Docker container during deployment. Index shards and parsing processes for pre-existing condition determination occupy too many file handles that are not released in a timely manner. - Knowledge base search responses time out, shown as a
504 Gateway Timeoutinterface return. The cause is that the configuredPARSE_FILE_TIMEOUT_SECONDSvalue is too short to complete parsing of long medical record documents, and no current limiting strategy for batch recall is configured. - Recall results lack the diagnosis time field, shown as incomplete fields in returned pre-existing condition determination results. The cause is failure to re-synchronize the mapping rules between medical insurance codes and knowledge base fields after an upgrade, leading to misaligned field matching.
How to verify proper configuration
- Access the target Docker container, execute the
ulimit -ncommand, and confirm the returned value is greater than10240to verify that the file handle configuration meets requirements. - Upload a test medical record document containing multiple segments of visit records, wait for parsing to complete, and check the vector recall results to confirm that the number of recalled entries matches the
RECALL_TOP_Kconfiguration. - Trigger a manual incremental update task, check the system logs, and confirm that newly added visit record fields are correctly mapped to the corresponding fields in the knowledge base.
- Call the public test search interface to verify that the response time meets the real-time requirements preset by the business.
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.