Data Characteristics in This Domain
Medical affairs data primarily comes from clinical trial reports, real-world evidence (RWE) studies, medical literature, drug labels, regulatory approval documents, and internal expert opinions. Data update frequencies vary. Clinical trial data typically releases in batches after study completion, while medical literature and regulatory dynamics update continuously. Document structures are mostly unstructured text. They contain extensive specialized terminology, abbreviations, and complex tables, such as clinical study protocols, statistical analysis plans (SAPs), and medical reviews. Fields and units include dosage (e.g., mg/kg), frequency (e.g., QD, BID), efficacy endpoints (e.g., OS, PFS), safety event classifications (e.g., CTCAE grades), and biomarker levels (e.g., ng/mL). Unit standardization is high, but subtle differences may exist across sources.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The high proportion of unstructured text requires workflows to strengthen text parsing and entity recognition capabilities during data preprocessing to accurately extract key information. Multi-source heterogeneous data necessitates flexible data ingestion interfaces and handling potential data conflicts and redundancy. The specialized and complex nature of medical terminology demands higher precision in large language model (LLM) node prompts, avoiding generalized understanding that leads to information bias. Data sources with varying update frequencies require workflows to support both scheduled and event-driven triggers, ensuring the timeliness and effectiveness of knowledge base content. Additionally, highly standardized fields and units provide a basis for validating and ensuring consistency in model output, but also require strict mapping and maintenance of these specifications when building the knowledge base.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 800–1200 characters | Balances contextual completeness of medical text with recall efficiency, avoiding excessive fragmentation. |
Recall count (Recall Count) | Top 5 | Ensures retrieval accuracy, reduces interference from irrelevant information, and considers model processing capacity. |
Similarity threshold (Similarity Threshold) | 0.75 | A high threshold ensures strong relevance of recalled content to medical queries, reducing false positives. |
Citation Content Template (Reference Content Template) | {{title}}\n{{content}} | Highlights document title and body content, facilitating model understanding of context. |
Rerank result count (Reranked Return Count) | 3 | Further filters recalled results to improve relevance to the query. |
maxContext | 4096 tokens | Adapts to mainstream LLM context windows, ensuring complete input of specialized medical content. |
Three Common Mistakes
- Custom variables do not display correctly when accessing links without login. This is because these variables are typically tied to user sessions and require authentication to take effect.
- Knowledge base search node is configured with user authentication, but it does not take effect. This is due to incorrect authentication configuration hierarchy or priority, causing global settings to override node settings.
- The dropdown list is empty when selecting variable references in the knowledge base search node. This is because the upstream node did not correctly output or define the variable, or the variable type does not match.
How to Confirm Correct Configuration
- Perform end-to-end testing with different types of medical affairs questions. Check if the output accurately cites knowledge base content and verify key fields and units.
- Simulate data updates to verify that workflow triggers and knowledge base synchronization execute as expected, ensuring information timeliness.
- Check the responses from the AI chat node's prompts. Ensure understanding and replies to medical terminology meet professional standards, and adjust
Similarity threshold(Similarity Threshold) based on actual feedback. - In workflow debug mode, track the input and output of each node. Confirm that custom variables and authentication configurations are correctly passed and effective at each stage.
The values provided are common starting points. They should be measured against your 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.