Knowledge Base Retrieval and Recall for Bioequivalence

Data Characteristics

Bioequivalence study data primarily comes from clinical trial reports, regulatory submission materials, and post-market surveillance reports. This data updates infrequently, typically aligning with drug development cycles and regulatory approval processes, with a few updates per year. Document structures mainly consist of structured tabular data and unstructured text reports, such as pharmacokinetic (PK) parameter tables, bioanalytical method validation reports, and adverse event (AE) descriptions. Key fields include drug name, active ingredient, dosage form, administration route, PK parameters (e.g., Cmax, AUC, Tmax), subject characteristics, adverse event codes (e.g., MedDRA codes), frequency, and severity. Units typically involve concentration (ng/mL), time (h), and area (ng·h/mL).

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The diversity of bioequivalence data requires the knowledge base to handle mixed structured and unstructured data. Numerical data like PK parameters demand precise matching and range queries, while text data such as adverse event descriptions rely on semantic understanding and fuzzy matching. Low update frequency means that knowledge base index rebuilding or incremental updates do not need to be frequent, but each update must ensure data integrity and consistency. Documents often contain extensive specialized terminology and abbreviations, requiring domain adaptation for tokenizers and embedding models. Furthermore, the low incidence and high dispersion of adverse events necessitate higher recall sensitivity to avoid missing critical information.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters`	Balances the completeness of specialized terminology context with the information density of a single chunk, avoiding dilution of key information by overly long texts.
`Overlap Length`	`100–150 characters`	Ensures information continuity across chunks, especially when processing adverse event descriptions and PK data analysis reports.
`embeddingModel`	`text-embedding-ada-002` or domain-specific model	Improves understanding of biomedical terminology and concepts, enhancing semantic matching accuracy.
`Recall count` (Recall Count)	`10–15 items`	Given the sparsity of adverse events, increasing the recall count raises the probability of finding potentially relevant information.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjust based on actual retrieval performance and false positive rates, typically fine-tuned around `0.75`.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient file parsing time when processing large clinical study reports or regulatory submission documents.

Common Pitfalls

File upload failure, with the message fail to create post presigned url: This usually indicates an S3-compatible storage configuration issue, such as incorrect MinIO Endpoint, Access Key, or Secret Key settings, leading to insufficient permissions for presigned URL generation.
Retrieval results lack key PK parameters or adverse event codes: The tokenizer might fail to correctly identify specialized terms like Cmax, AUC, or MedDRA codes, causing embedding vectors to deviate and impacting recall.
Knowledge base import of CSV files fails with error datasetId is required for S3 files: This indicates that when attempting to import files from S3, the associated datasetId parameter is missing, preventing the system from binding S3 files to a specific knowledge base dataset.

How to Verify Configuration

Upload various types (.txt, .pdf, .csv) of bioequivalence study documents. Check file parsing status and chunk previews for expected results.
Construct query statements containing PK parameters, adverse event descriptions, and regulatory requirements. Observe if recall results include highly relevant original document snippets and compare their similarity values.
Execute a series of queries for specific drugs or adverse reactions. Check if the Recall count (recall count) and Rerank result count (reranked return count) consistently provide sufficient and relevant knowledge points, and adjust the Similarity threshold (similarity threshold) accordingly.
Simulate small batch data updates. Observe if the knowledge base's incremental indexing process is smooth and if query accuracy does not decrease after the update.

Note: The values provided are common starting points. Measure them against your own samples for optimal results.

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.