Model Integration and Configuration for Antibody-Drug

Data Characteristics

Antibody-Drug Conjugate (ADC) regulatory and SOP documents originate from pharmaceutical quality management systems, R&D records, manufacturing process protocols, and clinical trial plans. These documents have a low update frequency, typically revised only when regulations change, technology advances, or internal processes are optimized. Revision cycles can range from months to years. Document structures are hierarchical and modular, including quality manuals, procedural documents, work instructions, and record forms. File formats are often PDF, Word, or scanned images. Content covers antibody selection, linker design, toxin conjugation, purification, formulation, stability studies, quality control, and batch release. Fields and units are highly specialized, such as drug-antibody ratio (DAR), conjugation efficiency (%), free drug (ng/mL), endotoxin content (EU/mg), batch number, expiration date, storage conditions (°C), and pH.

Constraints on Model Integration and Configuration

The low update frequency of ADC regulatory documents means model training and knowledge base construction do not require frequent full updates; incremental updates are more suitable. The hierarchical document structure requires the model to identify and link content from different document levels during retrieval to provide comprehensive answers. The high proportion of PDF and scanned documents demands robust OCR capabilities to ensure accurate text extraction, especially for critical data within tables and figures. Specialized fields and units necessitate that the model possesses domain knowledge to correctly interpret these terms and avoid unit confusion or misinterpretation. For example, interpreting DAR values requires the model to recognize the number and understand its significance in ADC quality control. These documents often contain numerous tables and diagrams, requiring strong multimodal model capabilities for integrated text and image understanding.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	ADC regulatory documents often contain high-resolution images and complex layouts, resulting in large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files, especially OCR processing of scanned documents, requires significant time.
`Chunk size` (Segment Length)	`800-1200 characters` (characters)	Ensures each text segment contains sufficient contextual information for the model to understand specialized terms and process details.
`Recall count` (Retrieval Count)	`Top 8-12 entries` (top 8-12 items)	ADC regulations are complex; a single question may involve multiple SOPs or protocols, requiring support from multiple retrieval results.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Balances retrieval breadth and precision to avoid missing or incorrectly retrieving relevant regulatory clauses.
`Rerank result count` (Reranked Return Count)	`3-5 entries` (items)	Reranked models can more accurately filter the most relevant regulatory details for a given question.

Common Pitfalls

Model answers misinterpret or confuse specialized terms or values, such as confusing DAR values with other metrics. This occurs when knowledge base segmentation granularity is too large or too small, preventing the model from accurately capturing context.
After uploading PDF documents, the model fails to recognize image content or table data within the document, leading to incomplete answers. This happens when multimodal large model calling capabilities are not enabled, or OCR services lack sufficient support for complex tables and diagrams.
During knowledge base construction, some regulatory files fail to upload or time out during parsing. This is due to UPLOAD_FILE_MAX_SIZE or PARSE_FILE_TIMEOUT_SECONDS being set too low, preventing the system from handling large or complex PDF files.

Verification Steps

Select several ADC regulatory documents, upload them to the knowledge base, and check file parsing status and segment previews. Confirm that text, tables, and image content are accurately extracted.
Ask the model typical questions related to ADC R&D, production, and quality control. Compare the model's answers with the original regulatory documents to assess the accuracy of specialized terms, values, and process descriptions.
Construct complex questions involving multiple levels and cross-document information. Test the model's ability to synthesize content from multiple documents to provide coherent and comprehensive answers. Also, check if Recall count (Retrieval Count) and Rerank result count (Reranked Return Count) are appropriate.

The values provided are common starting points. Measure 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.