Data Characteristics
Bispecific antibody (BsAb) R&D documents come from diverse sources. These include internal experimental reports, patent literature, clinical trial records, and public research papers. Document update frequency is high, especially during early R&D stages, with rapid iteration of experimental data and results. Document structures are often complex. They contain text descriptions, charts, molecular structures, sequence information, and pharmacokinetic data. Fields and units involve antibody affinity (e.g., KD value, unit nM), half-life (t1/2, unit hours), target binding specificity, titer (titer, unit mg/mL), and dosage (unit mg/kg). These often come with complex naming conventions and abbreviations. Sequence information may be embedded in FASTA format. Chart data requires OCR or image recognition technology for extraction.
Constraints on Knowledge Base Retrieval and Recall
The complexity of bispecific antibody R&D documents poses multiple challenges for knowledge base retrieval and recall. First, multimodal data mixtures hinder traditional text retrieval. This requires integrating parsing capabilities for images, tables, and sequence information. Second, the high density of specialized terminology and abbreviations demands that vector models possess deep domain knowledge. This ensures accurate understanding of query intent and matching of relevant document segments. High update frequency means the knowledge base needs to support efficient incremental indexing and real-time update mechanisms. This ensures the timeliness of retrieval results. Furthermore, structural differences across documents from various sources make standardized extraction difficult. This can lead to fragmented information and impact recall completeness. Accurate retrieval of numerical data like affinity and half-life requires support for range queries and unit conversion. This avoids missing critical information due to pure semantic matching.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Balances semantic completeness and vector embedding efficiency. Avoids long paragraphs diluting key information and short paragraphs losing context. |
Recall count (Recall Count) | 15–25 items | Considers document complexity and information density. Increases recall quantity to improve potential relevance. Reranking can optimize this later. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements, 0.7–0.85 suggested | Domain-specific terminology may have high similarity. Balances precision and recall. The specific value depends on embedding model performance. |
Rerank result count (Reranked Return Count) | 5–8 items | After recall and reranking, focuses on the most relevant core information. Reduces the processing burden on downstream large language models. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large experimental reports and documents containing charts and sequences can take a long time. |
MAX_EMBEDDING_BATCH_SIZE | 32 | Adapts to different GPU memory limits. Balances processing speed and resource usage. Ensures stability for batch embedding of large documents. |
Common Mistakes
- Document parsing error
unsupported format. This usually indicates incorrect file type identification. Alternatively, the document content contains encryption, macros, or other complex elements, preventing the parser from correctly reading the internal structure. - Key numerical information in retrieval results is missing or inaccurate. This can happen if numerical values and units are not correctly associated during document structuring. Or, they are truncated during text chunking, leading to incomplete semantics during vectorization.
- Retrieval results do not reflect the latest data after knowledge base updates. This happens when the incremental indexing mechanism is not correctly configured or executed. New data is not vectorized and added to the search index in a timely manner.
Verification Steps
- For typical queries, check if recall results include expected key document segments. Compare these segments to confirm they accurately reflect core information in the original document.
- Select a batch of queries containing numerical values and units. Verify the accuracy of numerical values and correct unit matching in retrieval results. Check for support of range queries.
- Upload and index a new R&D report. Immediately perform relevant queries to confirm new data is recalled promptly. Evaluate the timeliness of recall results.
- Simulate multiple concurrent user queries. Monitor system response times. Ensure retrieval performance meets expectations under actual usage scenarios.
Note: The suggested values are common starting points. Measure them against your own samples for optimal performance.
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.