Data Characteristics in This Category
Target discovery data typically originates from research papers, clinical trial reports, patent literature, bioinformatics databases (e.g., NCBI, UniProt, KEGG), and internal experimental records. Update frequencies vary; external databases may update quarterly or monthly, while internal experimental data is generated in real-time. Document structures are complex. They often contain large amounts of unstructured text, tables, figures, molecular structures, sequence information, and gene expression data. The text is highly specialized, involving extensive biological, chemical, and medical terminology, such as gene IDs, protein names, pathway names, compound structure codes, cell line names, and disease classification codes (ICD). Units are diverse, including concentration units (nM, μM), dosage units (mg/kg), time units (h, d, w), abundance units (FPKM, TPM), and various bioactivity indicators (IC50, EC50).
Constraints Imposed by These Characteristics on "Document Parsing and Chunking"
The complexity of target discovery documents imposes several constraints on document parsing and chunking. First, mixed-modality data requires parsers to identify and process text, tables, and some image content. Pure text chunking can lose critical information. Second, dense specialized terminology and abbreviations require ensuring contextual semantic integrity during chunking, preventing the separation of closely related terms or concepts. For example, gene-disease association descriptions should not be split. Third, differing data update frequencies mean the knowledge base needs to support incremental updates and version management. Chunking strategies must adapt to new data merges. Finally, the specificity of fields and units requires maintaining the association of these key pieces of information after chunking. For instance, compound activity data and corresponding concentration units must remain within the same chunk to ensure accurate recall later.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 800–1200 characters | Ensures chunks contain sufficient context, covering key information like targets, mechanisms of action, and experimental conditions, while avoiding excessive length that could reduce recall efficiency. |
Chunk size (Chunk Length) | 500 characters | Balances information density and retrieval granularity, making it easier for the model to understand the meaning within a segment. |
Chunk overlap (Chunk Overlap) | 100 characters | Guarantees contextual continuity, preventing critical information from being truncated at chunk boundaries. |
Image OCR Recognition | Enabled | Target discovery documents often contain figures and molecular structures. OCR can extract key textual information. |
Table Structural Parsing | Enabled | Experimental data and compound activity are usually presented in tables. Structural parsing preserves their semantics. |
Index Size | Calibrate based on actual measurements | Dynamically adjust based on total knowledge base volume and recall performance requirements. An initial setting of 100,000 entries is a good starting point. |
Three Common Mistakes
- Recall results after chunking lack critical experimental data or molecular structure information. This happens because image OCR or table structural parsing was not enabled, leading to the loss of non-textual content.
- When searching for relevant targets, recalled document segments are semantically incomplete or lack context. This manifests as results containing only isolated gene IDs or compound names. This occurs because
Chunk size(Chunk Length) is set too small orChunk overlap(Chunk Overlap) is insufficient. - After a knowledge base update, the retrieval effectiveness of new documents is poor. This occurs because the incremental update mechanism is not correctly configured, and new data is not timely parsed and indexed.
How to Confirm Proper Configuration
- Select a batch of typical target discovery documents containing text, tables, and images. Check if the parsed chunks completely retain key information, especially table data and figure captions.
- Perform keyword retrieval tests for core targets or compounds. Evaluate the contextual completeness and relevance of the recalled segments, ensuring semantic units are not incorrectly split.
- Regularly simulate new data injection. Observe the knowledge base's incremental update process and perform retrieval tests on newly added documents to verify data availability after updates.
- Check logs for parsing failure records, especially for specific file types or file sizes. Adjust parameters like
PARSE_FILE_TIMEOUT_SECONDSorUPLOAD_FILE_MAX_SIZE.
The values provided are common starting points. Measure them 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.