Knowledge Base Retrieval and Recall for Bioequivalence

Data Characteristics

Bioequivalence (BE) product data primarily originates from research reports, clinical trial documents, regulatory filings, and peer-reviewed academic papers. Data updates are relatively stable, with major updates occurring during product approval applications, regulatory changes, or new product launches. Daily updates are minimal. Document structures for BE study reports are typically standardized, including trial protocols, subject selection criteria, pharmacokinetic (PK) parameter tables, statistical analysis results (e.g., geometric mean ratios, 90% confidence intervals), and conclusions. Fields and units are highly standardized, such as plasma concentration (ng/mL), area under the curve (AUC, ng·h/mL), and peak concentration (Cmax, ng/mL), along with various statistical P-values, confidence interval upper and lower bounds. Many critical data points are presented in tables, accompanied by extensive textual descriptions and graphical analyses.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The highly standardized and structured nature of BE product data demands high precision in knowledge base retrieval. Numerical information like PK parameters and statistical results require careful attention to contextual semantics during text embedding to avoid ambiguity from isolated values. The fixed document formats and tabular data mean that chunking strategies must preserve table integrity, ensuring that critical numerical values and their biological meanings are not separated. The low update frequency allows for greater initial investment in high-quality knowledge chunking and vectorization, reducing the overhead of frequent re-indexing later. Given the authoritative nature of the data sources, traceability of retrieval results is crucial, requiring precise localization to specific sections or tables in original reports. The standardization of fields and units also provides a foundation for structured queries and entity-recognition-based retrieval, but it requires the model to understand the deeper meaning of these specialized terms.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters`	Ensures capture of complete PK parameter tables or critical statistical result descriptions, preventing information truncation.
`Chunk overlap` (Chunk Overlap)	`100–150 characters`	Maintains contextual continuity between adjacent chunks, especially across page or table boundaries.
`Recall count` (Number of Retrieved Chunks)	`8–12 chunks`	Given the complexity of BE reports, increasing the number of retrieved chunks helps cover more relevant but non-core details.
`Similarity threshold` (Similarity Threshold)	Calibrate based on empirical testing	Determine by evaluating F1 scores on a test set, based on specific corpus and query types. Typically between `0.7–0.8`.
`Rerank result count` (Number of Reranked Chunks)	`5 chunks`	Further refines initial retrieval results using a reranking model, prioritizing the most relevant core conclusions.
`ENABLE_TABLE_PARSING`	`true`	BE reports contain extensive tabular data; enabling table parsing effectively extracts structured information.

Common Pitfalls

Retrieval results contain numerous irrelevant numbers or units. This happens when text chunking does not adequately consider the contextual semantics of numerical fields, leading to embedding vectors that fail to capture their biological meaning accurately.
When querying for specific pharmacokinetic parameters, the system fails to return the original table containing that parameter. The retrieved text only describes the table without actual data. This occurs because the document parser did not correctly identify and extract table content or fragmented the table during chunking.
When a user asks for statistical conclusions from a BE study, the results do not provide accurate confidence intervals or P-values. This is due to insufficient semantic understanding of these critical statistical indicators during knowledge base indexing, or their importance being diluted during vectorization.

Verification Steps

Select a BE study report with typical PK parameters and statistical results. Conduct multiple rounds of questioning to check if the retrieved content accurately presents key numerical values, units, and corresponding statistical conclusions from the report.
For tabular data within reports, construct specific queries to verify if retrieval results include complete information from the original tables, including row and column headers and numerical values.
By tracking query_id in logs, inspect the source_nodes field of retrieved chunks to confirm that the original document links and page numbers for each retrieved chunk are correct and traceable to the specific location in the original report.
Randomly select a batch of queries related to core BE product metrics. Evaluate their recall_score and precision_score to ensure a balance between retrieval quantity and accuracy.

Note: The values provided are common starting points. They should be measured 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.