Knowledge Base Retrieval for Medical Information (MI)

Data Characteristics

Medical Information (MI) responses in the biopharmaceutical domain use standard response library data. This data originates from pharmaceutical companies' medical affairs departments, clinical research reports, drug inserts, medical literature, and regulatory agency publications. Data is typically in structured or semi-structured document formats. Update frequency is stable, generally following drug lifecycle management (e.g., new drug launches, indication expansions, adverse event updates), which may be quarterly or semi-annually. Document structure commonly includes fields such as medical question (Q), standard answer (A), references, version number, effective date, and expiration date. Medical questions are typically clinical inquiries from patients or physicians. Standard answers must strictly adhere to medical accuracy and compliance requirements. Units for dosage, frequency, and treatment duration must be precise.

Constraints on Knowledge Base Retrieval and Recall

Standard response library data characteristics impose several constraints on knowledge base retrieval and recall. First, the rigor and compliance requirements of standard answers make recall accuracy a core concern. Recall results must align highly with original standard answers, without semantic drift or information loss. Second, version management and effective date fields require ensuring that recalled standard answers are current and valid, preventing outdated information. Explicit medical question-answer pairs in documents demand that the retrieval model accurately matches user queries with standard questions in the knowledge base, reducing fuzzy matches. Additionally, the high density of medical terminology requires advanced tokenization and semantic understanding capabilities to correctly process synonyms, near-synonyms, and professional abbreviations, and to identify subtle differences in dosage forms and administration routes.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500-800 characters` (characters)	QA pairs in standard response libraries are typically short. Excessive chunk length may introduce irrelevant information, while insufficient length may truncate complete answers.
`Chunk Overlap Length` (Chunk Overlap Length)	`50 characters` (characters)	Ensures context continuity at chunk boundaries, helping the model understand cross-chunk information.
`Recall count` (Recall Count)	`3-5 entries` (items)	Standard responses usually have clear answers. A few precise recalls are better than many vague results, reducing the burden of processing redundant information on the model.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Ensures recall results are highly relevant to the user query, reducing inaccurate or off-topic responses.
`Rerank result count` (Rerank Return Count)	`1 entries` (item)	Given the uniqueness and rigor of standard responses, typically only the single most matching standard answer is expected.
`maxContext`	`2048 token`	Ensures the recalled standard answer and its necessary contextual information can be fully contained, preventing truncation.

Common Pitfalls

Symptom: Model output responses show subtle differences or semantic deviations from standard answers. Reason: The Similarity threshold (Similarity Threshold) is set too low, leading to the recall of partially matched knowledge fragments, or Chunk size (Chunk Length) is inappropriate, truncating key information.
Symptom: A user asks about a specific dosage of a drug, but the model returns all dosage forms or general information for that drug. Reason: Document fields in the knowledge base are not effectively indexed, or the retrieval model fails to correctly identify key entities in the user query, leading to generalized recall.
Symptom: After deploying FastGPT, users from different departments or roles within the enterprise can access all knowledge base content. Reason: Access Permissions or Knowledge Base Grouping are not configured correctly, leading to failed permission control and inability to isolate knowledge bases by role or department.

Verification Steps

Perform retrieval tests for typical medical questions. Check if the recall results are the latest and most accurate standard answers. Verify reference version numbers.
Use a test set containing professional terms, synonyms, and abbreviations. Verify if the retrieval system can accurately recall corresponding standard responses. Evaluate recall rate and accuracy.
Simulate users with different permission levels. Test that they can only access authorized knowledge base content. Ensure Access Permissions configuration meets expectations.

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.