Data Characteristics
Bispecific antibody R&D documents typically contain highly specialized biological data, chemical structure information, and preclinical/clinical trial reports. Data sources are diverse, including internal experimental records, shared data from partner organizations, and public patents and literature. Update frequency depends on the R&D phase. Early discovery stages might generate new experimental data weekly, while clinical trial stages update in batches or at milestones. Document structures vary, encompassing structured experimental report tables, unstructured research logs, PDF-formatted patent files, and multi-column Excel screening data. Key fields include target name, antibody sequence, binding affinity (e.g., KD value, unit nM), half-life (unit hours), PK/PD parameters, and adverse event reports.
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The data characteristics of bispecific antibody R&D documents impose specific requirements on deployment and upgrades. Diverse document structures and specialized fields necessitate a RAG system with robust document parsing capabilities, especially for accurate extraction from tables and PDFs. For example, multi-column screening data in Excel can lead to semantic loss if not properly segmented. High update frequency requires the system to support efficient incremental update mechanisms, avoiding full re-indexing with every update. Complex specialized terminology and units of measurement demand customized tokenizers and entity recognition models to ensure the precision of key information retrieval. Furthermore, the system must handle large volumes of data, placing high demands on storage and computing resources, particularly during model upgrades, where data migration and compatibility issues need consideration.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates large experimental reports or image files, ensuring unobstructed file uploads. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances context completeness with retrieval efficiency, adapting to paragraph lengths in specialized documents. |
Recall count (Retrieval Count) | Top 5 entries (top 5 items) | Focuses on high relevance needs, emphasizing a small number of high-quality retrieval results. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances accuracy and recall rate, avoiding interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Provides ample time for processing large PDFs or complex table parsing. |
maxContext | 32000 token | Accommodates lengthy research reports and clinical trial documents, providing sufficient context. |
Three Common Mistakes
- Symptom: The system fails to recognize multi-column data in Excel files, leading to missing key information in the knowledge base. Reason: The default segmentation strategy is not suitable for multi-column structured data; it fails to treat each row as an independent semantic unit.
- Symptom: After deploying a new version of FastGPT, older workflow orchestrations cannot be imported, showing a format incompatibility error. Reason: The JSON structure or field definitions for workflow orchestrations may change between different versions, lacking forward compatibility.
- Symptom: When querying antibody affinity data after deployment, the results contain a large number of irrelevant biological terms. Reason: Lack of specialized tokenizers and entity recognition configurations for the biomedical domain leads to inaccurate understanding and indexing of specialized terminology.
Verification Steps
- Upload an Excel file containing multi-column tables. Verify that each row of data is correctly segmented and indexed in the knowledge base.
- Import an older version of the workflow orchestration file. Confirm that all nodes and connections display and run correctly.
- Perform queries for specific targets and antibody sequences. Verify that the retrieval results include accurate affinity (KD value) and half-life data, and check for consistent units.
- Simulate data updates for different R&D stages. Verify that the system can perform incremental indexing efficiently and ensure the real-time nature of query results.
Note: The values provided are common starting points. Measure against specific 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.