Data Characteristics
Real-World Research (RWR) documents come from various sources. These include Electronic Health Records (EHR), insurance claims data, patient registries, wearable device data, and Patient-Reported Outcomes (PRO). Update frequencies vary. EHR data might update in real-time. Insurance claims data typically updates quarterly or annually in batches.
Document structures also vary. EHRs often contain semi-structured or unstructured text, such as doctor's notes, lab reports, and medication orders. Insurance data is primarily structured tables. Fields and units are complex. For example, the "diagnosis" field in EHRs might contain International Classification of Diseases (ICD) codes or free-text descriptions. The "dosage" field might involve milligrams (mg), micrograms (mcg), or units (U), often with frequency and route information.
Constraints on Conversation Logs and Auditing
The heterogeneous nature of RWR document data sources requires conversation logs to record parsing processes and results from different sources. This allows for problem traceability. Varying update frequencies require the auditing system to distinguish between real-time and batch data processing. The system must also log corresponding processing timestamps.
Diverse document structures, especially the presence of extensive unstructured text, increase the complexity of structured parsing. Conversation logs must detail how the parsing model extracts key information, such as disease names and drug dosages from free text.
Complex fields and units require logs to precisely record field standardization or normalization processes, along with unit conversion details. This ensures the accuracy of subsequent analysis. For auditing, this means requiring more granular operation records. These records help pinpoint specific parsing steps when data inconsistencies or anomalous analysis results occur.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
logLevel | INFO or DEBUG | RWR document parsing is complex. DEBUG provides more detailed intermediate step logs for troubleshooting. |
maxContext | 2000–4000 characters | RWR documents often contain lengthy descriptive text. A larger context window captures complete semantics and avoids information truncation. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large RWR datasets may include huge text files. A longer timeout prevents parsing interruptions and ensures data integrity. |
similarityThreshold | 0.75–0.85 | The specificity of medical terminology requires a higher similarity threshold. This ensures the accuracy of retrieval results and reduces interference from irrelevant information. |
auditLogRetentionDays | 365 days | RWR data is typically used for long-term research and compliance reviews. Logs need long-term retention to meet auditing and traceability requirements. |
maxTokens | 4096 | This provides sufficient generation length for complex queries and detailed responses that may appear in RWR documents. |
Common Mistakes
- Key fields are empty in conversation logs. The
fieldNamefield value is missing. This occurs when the document parser fails to correctly identify or extract target information from unstructured text. This might be due to improperly configured regular expressions or semantic parsing models. - The model response is empty. The API returns
{"message": ""}or a similar empty response body. This might occur if the upstream model service connection times out or returns an incorrect format, preventing FastGPT from processing it. - The data lineage is broken in audit reports. Source data and target data for some parsing steps do not match. This happens when the data processing flow lacks a unique transaction ID or batch identifier. This prevents effective correlation of log records.
How to Verify Configuration
- Submit an RWR document containing complex medical terms and multi-unit values. Check if the conversation log completely records all extracted key field values and unit conversions. Compare these against expected results.
- Simulate a model timeout or error response. Verify that the system correctly logs the error type and timestamp. Confirm that it triggers corresponding retry or alert mechanisms.
- Perform a batch import and parsing of RWR documents. Use the auditing function to check the complete processing path for each document, from original input to structured output. Confirm the continuity and traceability of log records.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.