Workflow Orchestration for Antibody-Drug Conjugate (ADC)

Data Characteristics

ADC regulatory documents originate from pharmacovigilance agencies, internal quality management systems, clinical trial protocols, and manufacturing process specifications. These documents update infrequently, typically with policy changes or new drug development. The document structure is hierarchical, including chapters, sections, clause numbers, appendices, and figures. Key fields include drug name, target, conjugation technology, toxin molecule, indications, dosage, administration route, storage conditions, quality standards, production batch, and expiry date. These fields often have specific units, such as mg/kg for dosage, % for purity, ℃ for temperature, and mg/mL for concentration. Some documents also contain non-structured information like complex biomolecular structures, reaction flowcharts, and mass spectrometry graphs.

Constraints on Workflow Orchestration

The hierarchical structure and high density of specialized terminology in ADC regulatory documents require precise knowledge base retrieval within the workflow. This prevents misinterpretation due to missing context. The low update frequency means knowledge base update strategies can focus on periodic full synchronization or incremental review, reducing pressure from frequent real-time updates. Standardized fields and units provide clear matching criteria for entity recognition and information extraction, aiding in structured query construction. The presence of non-structured information, such as structural formulas and flowcharts, limits the effectiveness of text-based retrieval alone. This may necessitate integrating image recognition or multimodal processing capabilities. Furthermore, regulatory Q&A demands extremely high accuracy. Any deviation can lead to severe consequences, so the workflow must integrate strict verification and traceability mechanisms.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size`	`500-800 characters`	Balances clause completeness and retrieval efficiency, preventing dilution of key information in long paragraphs.
`Recall count`	`Top 5-8 entries`	Regulatory Q&A requires high precision; increasing retrieval quantity covers potentially relevant clauses.
`Similarity threshold`	`0.75-0.85`	Improves retrieval accuracy and reduces interference from irrelevant content, especially for specialized terminology.
`Rerank result count`	`3 entries`	Focuses on the most relevant regulatory clauses, reducing subsequent processing burden and facilitating quick user identification.
`maxContext`	`4000 token`	Ensures sufficient capacity for multiple retrieval results and user queries, providing enough context for accurate inference.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides ample parsing time for large PDF regulatory documents.

Common Pitfalls

Knowledge base search returning empty within the workflow. The outcome is a lack of basis for the final response or a direct error. This occurs due to improper knowledge base segmentation strategies or an excessively high similarity threshold, preventing matching relevant content.
Variables not persisting correctly in the workflow. Subsequent nodes cannot retrieve expected variable values or use old values. This happens due to a misunderstanding of variable update mechanisms, where variables are not configured for global or session-level storage.
Knowledge base ID or variable reference causing an error. Logs show KnowledgeBaseNotFound or InvalidParameter. This typically results from directly referencing a non-existent knowledge base ID or a mismatch between the assigned variable type and the expected parameter of the knowledge base node.

Verification of Configuration

Test with typical regulatory questions using different query methods. Verify the workflow consistently retrieves at least 3 highly relevant regulatory clauses.
Check workflow logs. Ensure maxContext does not overflow during complex query processing. Confirm the Recall count and Similarity threshold settings for knowledge base queries are effective.
Submit ADC-related questions containing obscure specialized terminology. Verify the workflow's entity recognition and specialized vocabulary processing capabilities, ensuring retrieval accuracy.

The values provided are common starting points and should be measured against the reader's 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.