Knowledge Base Retrieval and Recall for Target Discovery

Data Characteristics

Quality documents in target discovery include experiment reports, validation protocols, data analysis records, batch production records, and compliance documents. Data sources are typically Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELN), or internal document management systems. These documents have a relatively low update frequency. Updates usually occur after an experiment batch finishes, a protocol revises, or an audit cycle completes. Documents are primarily unstructured text. They often contain specialized terminology, biomolecule names, gene sequences, experiment parameters (e.g., concentration, temperature, time), and units (e.g., nM, ℃, h). Some documents embed charts, but textual descriptions convey the main information.

Constraints on Knowledge Base Retrieval and Recall

The low update frequency of target discovery documents means full index updates are not frequently needed after initial knowledge base construction. However, an incremental update mechanism is necessary. Intensive use of unstructured text and specialized terminology requires retrieval models with strong semantic understanding. The model must identify synonyms, near-synonyms, and process complex sentence structures. Experiment parameters and units necessitate precise matching and range-based retrieval. An example is querying compound activity data within a specific concentration range. Chart metadata or descriptions within documents also require effective extraction and inclusion in the retrieval scope. Document citations and cross-validation relationships increase the need for associative recall to provide comprehensive contextual information.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters` (characters)	Balances semantic completeness and retrieval efficiency. Avoids diluting core information in overly long chunks.
`Chunk Overlap Length` (Chunk Overlap)	`50–100 characters` (characters)	Ensures contextual continuity. Handles semantic dependencies across chunk boundaries.
`Recall count` (Recall Count)	`8–12 entries` (items)	Covers potentially highly relevant document segments. Balances recall precision with subsequent processing load.
`Similarity threshold` (Similarity Threshold)	Calibrate by measurement, e.g., `0.75`	Adjusts for semantic matching of biomedical terminology and context. Avoids low-relevance results.
`Rerank result count` (Reranked Return Count)	`3–5 entries` (items)	Refines the final presented results. Focuses on the most core and precise information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Handles parsing time for large experiment reports or validation protocols. Prevents timeouts.

Common Pitfalls

Retrieval results contain many irrelevant or low-relevance document fragments. This usually occurs if the Similarity threshold (Similarity Threshold) is set too low or if the chunking strategy fails to capture the context of specialized terminology effectively.
After a user query, the AI response provides a generic answer instead of effectively citing knowledge base content. This may be because knowledge base retrieval failed to recall any relevant content, or the Recall count (Recall Count) was too low to cover useful information.
When uploading large experiment reports or validation protocols, the system reports file parsing failure or timeout. This may relate to an insufficient PARSE_FILE_TIMEOUT_SECONDS setting or the document parser's compatibility with complex structured content.

Validation Steps

Select a set of test questions containing specialized terminology, experiment parameters, and key conclusions. Observe whether retrieval results include the expected document fragments and evaluate their relevance ranking.
Examine retrieval logs. Confirm Recall count (Recall Count) and Rerank result count (Reranked Return Count) match expected settings. Analyze reasons for un-recalled or low-ranked relevant content.
Upload large documents containing complex charts, tables, or special formats. Verify the system successfully parses them and creates knowledge base chunks. Check for errors or warnings during parsing.
Conduct multi-turn dialogue tests for specific targets or compounds. Evaluate the accuracy and depth of AI responses citing knowledge base content. Confirm effective use of recalled information.

The values provided are common starting points. Measure them against specific 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.