Data Characteristics
Data for rational drug use in clinical trial pre-screening comes from drug inserts, clinical guidelines, medical literature, pharmacogenomics databases (e.g., PharmGKB), and Electronic Health Records (EHR). Update frequencies vary. Drug inserts and clinical guidelines may update annually or with new research. Pharmacogenomics data updates more frequently. EHR data generates in real-time. Document structures are complex. Drug inserts typically include standard sections like indications, contraindications, dosage and administration, and adverse reactions. Clinical guidelines present diagnostic and treatment workflows and drug recommendations in structured text. Fields are diverse, including drug names, active ingredients, disease codes (e.g., ICD-10), gene loci, genotypes, phenotypes, dosage units (e.g., mg, ml), and routes of administration. Unit standardization and consistency are critical; for example, precise dosage units directly impact medication safety.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
Diverse data sources require tool calling to integrate various data interfaces, including RESTful API-based database queries and unstructured text parsing. Varying update frequencies mean plugins need data caching strategies and scheduled refresh mechanisms to ensure timely retrieval results. Complex document structures demand higher natural language processing (NLP) tool capabilities for text comprehension, especially for accurately extracting key information from unstructured text, such as drug interactions or contraindications for specific populations. Diverse fields and precise units constrain plugin parameter design. Input and output parameters must have clearly defined data types, units, and validation rules to prevent errors from unit mismatches. For example, a dosage calculation tool must strictly differentiate milligrams from micrograms and handle concentration conversions for different drug formulations. Processing pharmacogenomics data requires plugins to call external genotype-phenotype association query services and parse complex genetic information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 Token | Processing complex clinical guidelines and multiple drug inserts requires a larger context window. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large PDF clinical trial reports or drug inserts can be time-consuming. |
Recall count | Top 10 entries | Ensures sufficient relevant drug or trial information is retrieved during the initial screening phase. |
Similarity threshold | 0.75 | Balances recall and precision, avoiding inaccurate matches due to semantic ambiguity. |
Rerank result count | Top 5 entries | Re-ranks recall results to prioritize the most relevant rational drug use recommendations. |
ExternalAPIRequest timeout | 120 seconds | External drug databases or genomics query services may have longer response times. |
Common Pitfalls
- Batch execution nodes fail to complete the workflow: This occurs when the large language model or external tool's concurrent API request volume exceeds system or API provider limits, leading to failed or throttled requests.
- After an API call workflow, the conversation log's operational data is empty: This typically happens when the tool call's returned data format does not match the preset, or the JSON path is incorrect, preventing the system from parsing and populating the data.
- Custom plugin output download links appear after flickering: This might be due to the plugin's internal file generation or upload operations not being fully asynchronous, or the front-end component having refresh logic issues while waiting for the file URL to stabilize.
Verification of Configuration
- Simulate various clinical scenarios using different patient data and disease conditions. Verify that tool calling results align with expected rational drug use plans and that key drug parameters are correct.
- After each tool call, check if the structured data fields returned in the conversation log are complete and conform to predefined types, especially core fields like dosage, frequency, and contraindications.
- Observe whether response times are within an acceptable range for API call workflows. Check system resource utilization under concurrent requests to ensure stable operation under high loads and determine timeout thresholds.
The values provided are common starting points. Measure them against specific 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.