Knowledge Base Retrieval and Recall for Target Discovery

Data Characteristics in This Domain

Target discovery data primarily originates from scientific literature, patent reports, clinical trial data, and genomic and proteomic databases. This data updates frequently; preprints and public databases can have new entries weekly or even daily. Document structures vary, including structured database records, semi-structured abstracts and method descriptions, and unstructured full-text papers. Common fields include gene name, protein ID, pathway name, disease association, mechanism of action, IC50/EC50 values, KD values, and side effect descriptions. Units often involve nM, µM for concentration; hours, days, weeks for time; and FPKM or TPM for expression levels. The data volume is large, rich in specialized terminology, and contains numerous abbreviations and synonyms.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The diversity and update frequency of target discovery data require the knowledge base to efficiently handle multi-source heterogeneous data and support flexible incremental updates. The presence of specialized terminology and synonyms means simple keyword matching struggles to accurately recall relevant information, necessitating advanced semantic understanding. For example, numerical values for drug target affinity (e.g., IC50) or gene expression (e.g., FPKM) are typically accompanied by units. Retrieval must ensure correct association between values and units to avoid confusion. Additionally, documents often contain complex charts and tables. Pure text knowledge base segmentation may lose critical information, posing challenges for document parsing. High-density specialized information also means a single short text block might not provide sufficient context, requiring a longer context window to improve recall quality.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`800–1200 characters`	Target discovery texts are information-dense, requiring longer segments to retain sufficient context. Overly long segments increase irrelevant information interference.
`Chunk Overlap Length` (Segment Overlap Length)	`200–300 characters`	Ensures connectivity of key information across segments, especially when describing mechanisms of action or pathways, preventing critical information from being split.
`Recall count` (Recall Count)	`Top 8–15 entries`	Target discovery queries often involve multiple aspects, requiring recall of enough potentially relevant document snippets to cover different angles.
`Similarity threshold` (Similarity Threshold)	Calibrated by actual measurement, typically `0.75–0.85`	Ensures the accuracy of recall results. Too low may introduce noise; too high may miss highly relevant documents.
`Rerank result count` (Reranked Return Count)	`Top 5 entries`	After processing by a reranking model, it can more precisely filter out a small number of high-quality results most relevant to the user's intent.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Addresses the common occurrence of large PDF or DOCX documents in the biomedical field, which often contain numerous charts and tables.

Three Common Mistakes

Missing critical numerical values or units in query results, such as returning only the IC50 value without the nM unit. This occurs because document parsing failed to correctly identify the association between the value and unit, or the segmentation strategy split them.
Retrieving a large number of irrelevant literature abstracts while truly needed experimental method details are not recalled. This happens because knowledge base segmentation is too coarse, failing to refine to the methodological description level.
System query timeouts, especially when processing specific gene or pathway queries. This is due to an overly large knowledge base and insufficient index optimization, leading to inefficient vector retrieval for high-frequency query terms.

How to Confirm Correct Configuration

Use a batch of test questions containing specialized terms, numerical units, and complex descriptions to verify that recall results include all critical information and that numerical values and units are accurate.
Upload typical PDF or DOCX documents from the target discovery domain. Check the knowledge base segment preview to ensure key tables and figure captions are correctly extracted and included within segments.
Simulate high-concurrency queries. Observe if system response times are within an acceptable range and check logs for timeout error codes caused by inefficient retrieval.
For a set of queries with known answers, evaluate the proportion of accurately hit document snippets given the Recall count (Recall Count) and Similarity threshold (Similarity Threshold) combination.

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.