Knowledge Base Retrieval and Recall for Metabolic and

Data Characteristics

Clinical trial pre-screening data in the metabolic and endocrine domain originates from clinical trial protocols, Investigator's Brochures (IB), patient recruitment guidelines, medical literature (e.g., PubMed, Medline abstracts and full texts), disease diagnosis and treatment guidelines, and unstructured text within Electronic Health Records (EHR). This data updates frequently; medical literature and diagnostic guidelines may see new versions monthly or quarterly. Document structures vary. Clinical trial protocols typically contain structured section headings and unstructured descriptive paragraphs, covering inclusion/exclusion criteria, study design, and drug dosages. Common fields include physiological indicators like blood glucose (mmol/L or mg/dL), HbA1c (% or mmol/mol), insulin levels (μU/mL or pmol/L), and Body Mass Index (BMI, kg/m²), alongside disease diagnosis codes (e.g., ICD-10), medication history, and complication descriptions.

Constraints on Knowledge Base Retrieval and Recall

Data characteristics in metabolic and endocrine clinical trial pre-screening impose several constraints on knowledge base retrieval and recall. First, diverse document structures and mixed structured/unstructured data require the knowledge base to handle multiple document types effectively. It must support precise targeting of specific sections or paragraphs to avoid irrelevant information. Second, frequent data updates necessitate efficient incremental indexing to ensure retrieval results are current. For example, new treatment guidelines may invalidate old patient recruitment criteria. The variety of numerical ranges and units for physiological indicators requires the retrieval system to understand and match different numerical representations; HbA1c > 7% and HbA1c > 53 mmol/mol should be treated as equivalent. The specialized and standardized nature of terms like disease diagnosis codes and medication history demands medical terminology understanding to prevent recall omissions due to synonyms or near-synonyms. Additionally, patient privacy regulations (e.g., HIPAA) require strict de-identification of EHR data, impacting knowledge base content preprocessing.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`400–600 characters` (characters)	Inclusion/exclusion criteria paragraphs in clinical trial protocols are typically of moderate length. This range avoids redundancy from overly long chunks and loss of context from overly short ones.
`Chunk Overlap Length` (Chunk Overlap Length)	`50–100 characters` (characters)	Ensures critical information at paragraph boundaries is not lost during chunking, maintaining contextual coherence.
`Recall count` (Recall Count)	`Top 5–8 entries` (top 5–8)	Given the complexity of metabolic and endocrine disease diagnosis and treatment standards, increasing recall count improves recall rate and reduces missed diagnoses.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Clinical trial pre-screening requires high matching precision. A higher threshold filters out irrelevant trial protocols or patient information.
`Rerank result count` (Rerank Return Count)	`Top 3–5 entries` (top 3–5)	A reranking model re-sorts recall results, further enhancing relevance and focusing on the most pertinent results.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Parsing large PDF documents, such as complete clinical trial protocols, can be time-consuming. This provides sufficient time to prevent parsing timeouts.

Common Pitfalls

Incomplete knowledge base query results, with some relevant information not recalled. This may occur if the chunk length is set too short, causing critical information to be truncated or dispersed across different chunks, affecting semantic integrity.
After importing PDF documents, the search function returns no valid results and displays a parsing error. This may occur if the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low. For medical PDF files with many images or complex layouts, parsing time may exceed the limit, causing the task to fail.
No available model options appear in the text understanding model dropdown when creating a knowledge base. This may occur if the indexing model bge-m3 is not correctly configured or enabled in the FastGPT backend service, preventing the knowledge base creation wizard from recognizing it.

Configuration Verification

Select multiple representative clinical trial protocols for metabolic and endocrine diseases. Perform simulated queries using core inclusion/exclusion criteria and medication requirements to check if recall results include all relevant paragraphs.
Query the same concept using different expressions (e.g., colloquial descriptions, medical terminology, numerical ranges). Observe the stability and consistency of recall results to ensure semantic understanding.
Monitor whether newly imported medical literature or guidelines are retrieved promptly and accurately after a knowledge base update, especially for new disease diagnostic standards or treatment plans.
Check system logs for the disappearance of hnsw.iter-related error messages, confirming correct vector database index configuration.

Note: The values provided are common starting points. Measure performance 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.