Knowledge Base Retrieval and Recall for Quality Document

Data Characteristics

Quality document management in clinical trial pre-screening primarily involves Investigator Brochures, Clinical Study Protocols, Informed Consent Forms, Ethics Committee Approvals, Data Management Plans, and Statistical Analysis Plans. These documents originate from pharmaceutical companies, CROs (Contract Research Organizations), and various research centers, typically in PDF, Word, or scanned image formats. Update frequency is relatively low, occurring mainly during protocol revisions, ethical reviews, or regulatory updates. Document structures are complex, containing extensive normative text, figures, appendices, and citations. Fields include medical terminology, drug names, dosage units, time points, and investigator information. Units cover mg/kg, mL, µg, days, weeks, and months, often accompanied by abbreviations.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The complex structure and specialized terminology of quality documents challenge knowledge base chunking strategies and semantic understanding. A large volume of specialized vocabulary and abbreviations requires embedding models with strong domain knowledge to ensure retrieval relevance. Low document update frequency implies the need for version control in the knowledge base, but frequent full updates are not economical. The presence of scanned images demands high OCR accuracy. The diversity of fields and units, especially sensitive information like subject personal details, requires retrieval mechanisms to identify and isolate such data. Simultaneously, query results must accurately match numerical information like dosages and times, preventing misjudgments due to unit or value discrepancies.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters`	Balances contextual completeness with retrieval efficiency, avoids diluting key information with overly long chunks.
`Chunk Overlap Length` (Overlap)	`50–100 characters`	Ensures contextual continuity at chunk boundaries, improving recall accuracy.
`Embedding Model`	`text-embedding-ada-002` or domain-optimized model	Enhances understanding of medical terminology and specialized content, improving semantic matching precision.
`Recall count` (Recall Count)	`8–12 items`	Provides sufficient coverage while reducing the load on subsequent re-ranking and LLM processing.
`Similarity threshold` (Similarity Threshold)	Calibrate based on empirical data `0.75–0.85`	Balances recall rate and precision, prevents interference from irrelevant documents. Typically adjusted with specific datasets.
`Rerank result count` (Reranked Return Count)	`3–5 items`	Filters for the most relevant results, improving the quality of information presented to the user.

Note: The values provided are common starting points. Measure performance against your own samples to determine optimal settings.

Three Common Pitfalls

Uploaded PDF scanned content appears as garbled text. This occurs due to a lack of high-quality OCR pre-processing or insufficient optimization of the chosen OCR engine for medical documents.
Querying "specific drug dosage" returns numerous irrelevant documents. This symptom indicates that recall results include much non-dosage information or content unrelated to the queried drug. Possible causes include chunking strategies failing to effectively isolate key information or the embedding model lacking sufficient semantic understanding of numerical values and units.
API calls to the knowledge base return a 401 Unauthorized error. This is due to incorrect configuration or expiration of the authentication key.

Verification Steps

Upload various types of quality documents (PDF, Word, scanned images). Check the knowledge base content preview for normal display, ensuring no garbled text or missing information.
Query specific drug dosages, study time points, and other key information from clinical study protocols. Verify that recall results include precisely matching chunks and examine the distribution of similarity scores.
Use queries containing medical abbreviations and specialized terminology. Evaluate the semantic relevance of recall results and adjust Recall count (recall count) and Rerank result count (reranked return count).
Simulate high-concurrency query scenarios. Monitor system response times to ensure the knowledge base's performance meets expectations in practical applications.

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.