Knowledge Base Retrieval for Target Discovery Registration

Data Characteristics in Target Discovery

Target discovery data originates from scientific literature, genomics/proteomics databases, clinical trial reports, patent documents, and internal experimental data. Data update frequencies vary. Public databases like NCBI and UniProt typically update quarterly or semi-annually, while internal experimental data is generated in real-time. Document structures for literature are often PDF or XML, containing standard sections such as abstract, introduction, methods, results, and discussion. Database records are highly structured, including fields like gene ID, protein sequence, expression profiles, pathway information, and disease associations. Field naming conventions are generally standardized, and units are usually explicit; for example, gene expression levels use FPKM or TPM, and protein concentrations use nM or μM.

Constraints on Knowledge Base Retrieval and Recall from These Characteristics

The highly specialized and diverse nature of target discovery data imposes specific requirements on knowledge base construction and retrieval. First, the unstructured nature of literature data demands efficient text parsing and vectorization capabilities to capture relationships between specialized terms and biological entities. Second, the rich fields and cross-references in structured database records require the knowledge base to support multi-dimensional metadata filtering and precise matching. Due to varying data update frequencies, the retrieval system must differentiate between old and new data versions and prioritize recalling the latest and most authoritative information. Identifying specialized units like nM and μM as distinct from ordinary text is crucial for ensuring retrieval accuracy. Additionally, fuzzy matching and synonym expansion for unique identifiers like target names and gene IDs are key to improving recall rates.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk Length`	`800–1200 characters`	Balances contextual completeness and retrieval efficiency; avoids overly long or short chunks.
`Recall Count`	`Top 10–15`	Ensures sufficient candidate results while controlling subsequent re-ranking computational costs.
`Similarity Threshold`	`0.75–0.85`	Balances recall precision and recall rate; reduces interference from irrelevant results.
`Rerank Return Count`	`Top 5`	Focuses on the most relevant results; improves the quality of final presentation.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Handles large literature PDF files; prevents parsing timeouts.
`MAX_EMBEDDING_BATCH_SIZE`	`50`	Optimizes vectorization throughput; reduces API call frequency.

Three Common Pitfalls

Empty knowledge base retrieval results in a conversation, but successful in knowledge base testing: This often occurs when the conversation context lacks sufficient relevance to the knowledge base content, preventing the retriever from triggering effective recall during actual application.
Knowledge base returns irrelevant content to the query: This might stem from a Similarity Threshold set too low, leading to the recall of semantically unrelated document snippets.
Formulas display incorrectly or are misparsed: The document parser might fail to correctly identify or extract the structure of mathematical formulas, leading to information loss during vectorization or text display.

How to Verify Correct Configuration

Perform end-to-end tests within the application using a set of typical target discovery-related queries. Check if the similarity scores and content relevance of the recalled results meet expectations.
Observe the recall_count and rerank_count fields in the log output to ensure that the number of recalls and re-ranks aligns with the Recall Count and Rerank Return Count configurations.
Upload PDF literature containing complex biological formulas or diagrams. Check if the text stored in the knowledge base after parsing is complete and readable, especially for formulas.
Test queries with different phrasings for known target names, gene IDs, and other entities. Verify the consistency and accuracy of the recall results.

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

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.