Data Characteristics
Medical Affairs data includes clinical study reports, drug labels, medical guidelines, academic papers, real-world evidence (RWE), and medical conference minutes. Data sources are public databases, academic journals, pharmaceutical company internal repositories, and clinical trial management systems. Update frequencies vary; drug labels and guidelines may update quarterly or annually, while academic papers and clinical trial data are continuously published. Document structures are highly standardized, such as ICH-GCP clinical report templates and the IMRaD structure for journal articles. Fields and units are highly specialized, covering dosages (mg/kg), efficacy endpoints (OS, PFS), and adverse event grading (CTCAE v5.0), requiring extreme precision in terminology.
Constraints from "Forms and Interactions"
The specialized and standardized nature of Medical Affairs data imposes strict requirements on form design and user interaction. First, non-real-time data updates mean query results may be outdated; the system must clearly indicate data timeliness during interaction. Second, highly structured documents require precise field-level retrieval, such as querying data for a specific dosage group of a particular drug. This demands forms with multi-dimensional, hierarchical filtering options. The specialized medical terminology means user input may contain many abbreviations or aliases. The system needs robust synonym recognition and terminology standardization capabilities to prevent query failures due to term mismatches. Additionally, due to sensitive medical information, interaction flows must cite sources to ensure traceability and accuracy. Query results must display original document links or cited paragraphs.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 | Ensures enough space for critical context snippets from medical reports |
Chunk size (Segment Length) | 800–1200 characters | Accommodates long paragraphs and high information density in medical documents |
Recall count (Recall Count) | Top 10 entries | Increases recall to cover more relevant medical evidence |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Balances precision and recall, filters irrelevant medical literature |
Rerank result count (Reranked Return Count) | Top 5 entries | Prioritizes the most relevant core medical evidence |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing time for large clinical study reports or multiple papers |
Common Pitfalls
- Symptom: After a user submits a query, the AI response does not cite any documents, or the cited documents are clearly irrelevant to the question. Cause: Incorrect knowledge base selection logic configuration. For example, when dynamically passing values to the
knowledgeSearchvariable, the knowledge base ID format is incorrect or does not match the target knowledge base. - Symptom: A
unmarshal_resperror occurs during speech-to-text conversion, preventing voice functionality from working. Cause: Data transmission or format parsing issues between the speech recognition service and FastGPT. This could be due to an expired API key, unstable network, or an unexpected response body structure. - Symptom: After a user enters specialized medical terms in a form, the system does not return expected results, even if relevant information exists in the knowledge base. Cause: The system lacks the ability to recognize medical term synonyms, abbreviations, or different naming convention versions, leading to a mismatch between query terms and knowledge base content.
Verification Steps
- Construct a series of test questions containing specialized terminology and abbreviations for different medical conditions, drugs, and treatment plans. Submit them via the form and check if the AI response accurately cites relevant documents from the knowledge base.
- Simulate actual user operations. Record multiple voice inputs containing complex medical terms. Check the accuracy of speech-to-text conversion and verify if subsequent query results meet expectations.
- Examine the assignment logic of the
knowledgeSearchvariable in the workflow. Ensure that when dynamically selecting a knowledge base, the passed knowledge base ID matches the actual knowledge base settings and correctly triggers the knowledge base search. - Randomly select several core medical documents from the knowledge base. Use key phrases from these documents to perform queries. Verify that the system accurately recalls these documents and check if the returned
Similarity threshold(similarity threshold) is within a reasonable range.
Note: The values provided are common starting points. Measure them 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.