Data Characteristics
Real-World Evidence (RWE) quality documentation primarily originates from multi-center clinical studies, Electronic Health Records (EHR), medical insurance claims databases, disease registries, and patient-reported outcomes (PROs). Data update frequencies vary; EHR data might update daily, while large cohort study data could update quarterly or annually. Document structures typically include study protocols, data management plans, statistical analysis plans, ethics approval documents, data dictionaries, standard operating procedures (SOPs), and final study reports. These documents often exist as PDFs, Word files, or structured databases. Fields cover patient basic information, disease diagnoses, treatment plans, medication records, laboratory test results, adverse events, and follow-up data. These documents involve extensive medical terminology, units of measurement (e.g., mg/dL, mmol/L, ng/mL), and specific coding systems (e.g., ICD-10, LOINC, SNOMED CT).
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The complex data sources and diverse structures of RWE quality documentation demand robust data preprocessing capabilities from tool calling and plugins. Inconsistent data formats across sources require powerful data extraction and standardization tools to ensure effective model comprehension. The large volume of specialized medical terminology and coding systems in documents necessitates that plugins possess specialized term recognition and mapping capabilities to prevent errors from semantic misunderstandings. Inconsistent data source update frequencies require plugins to have flexible triggering mechanisms to accommodate real-time or periodic data synchronization. Furthermore, common tables, charts, and images in documents increase data parsing difficulty, requiring collaboration with image recognition and Optical Character Recognition (OCR) plugins. For data involving sensitive patient information, plugins must integrate strict data anonymization and privacy protection features to meet compliance requirements.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000 Tokens | Addresses the need for context comprehension of complex medical terminology and lengthy reports. |
Chunk size (Segment Length) | 1000 characters (Characters) | Balances semantic completeness and model processing efficiency. |
Recall count (Recall Count) | Top 10 entries (Top 10) | Enhances the comprehensiveness of professional knowledge recall. |
Similarity threshold (Similarity Threshold) | 0.85 | Precisely matches fine-grained medical concepts and terminology. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (Seconds) | Accommodates parsing times for large PDFs and multi-page Word documents. |
ENABLE_OCR | true | Processes common tables, images, and scanned content in documents. |
Common Mistakes
- In knowledge base Q&A, if the model cannot accurately answer a medical concept, the
Similarity threshold(Similarity Threshold) might be set too high, filtering out slightly less relevant documents. - Timeout errors when calling external database tools typically occur because the
PARSE_FILE_TIMEOUT_SECONDSparameter is too short, failing to adequately process large data queries or complex data transformation tasks. - If the model output contains a large amount of irrelevant information, the
Recall count(Recall Count) is often set too high, introducing too many irrelevant or low-relevance document snippets.
How to Verify Correct Configuration
- For core medical terms and disease names, perform multiple rounds of Q&A to verify if the model can accurately identify and cite relevant document snippets.
- Simulate complex queries involving external databases. Check if tool calls execute successfully and verify that returned data matches expectations.
- Upload scanned research reports containing tables and charts. Confirm that the OCR plugin accurately extracts text content and verify the model's understanding of this content.
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.