Monoclonal Antibody Quality Document Parsing and Chunking

Data Characteristics

Monoclonal antibodies (mAb) are biologics. Their quality documents have distinct professional characteristics. Data sources are extensive, covering research and development, production, quality control, and clinical stages. Key document types include process development reports, batch production records, inspection reports (e.g., HPLC, mass spectrometry, electrophoretograms), stability study reports, quality standards, and deviation and change records. These documents update infrequently, typically with batch production or regulatory revisions. Document structures are rigorous, often using fixed templates. They contain numerous tables, graphs, and specialized terminology. Fields and units are highly standardized. For example, protein concentration commonly uses mg/mL, purity uses %, pH is precise to one decimal place, and molecular weight uses Da. Documents also involve complex identifiers like batch numbers, instrument models, and reagent lot numbers.

Constraints on Document Parsing and Chunking

The strict structure and specialized content of monoclonal antibody quality documents impose specific requirements on document parsing and chunking. The presence of many tables and graphs means standard text extraction may lose critical information. This requires enhanced table recognition and image OCR capabilities. Specialized terminology and acronyms require chunking to identify and retain contextual semantics. This prevents meaning fragmentation due to excessive splitting. Fixed templates and standardized fields provide opportunities for structure-based chunking. This can use preset rules to improve chunking accuracy. Additionally, extracting and associating key identifiers like batch numbers and instrument parameters is crucial for accurate knowledge retrieval. These require special handling during chunking to ensure information is not fragmented.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size`	`500–800 characters`	Balances semantic completeness of paragraphs in mAb documents with contextual relevance during retrieval.
`Chunk Overlap Length`	`100–150 characters`	Ensures sufficient contextual overlap between adjacent chunks, preventing critical information from being severed.
`Table Recognition Mode`	`Smart Recognition`	Handles complex table structures in documents, accurately extracting row and column table data.
`图像OCR`	`Enabled`	Ensures text information in images like graphs and structural formulas is recognized and included in the knowledge base.
`custom Chunking Rule`	Calibrate by actual measurement	Defines splitting points based on section titles or specific identifiers for particular report templates, such as "Batch Production Record." Examples include `batch number：` or `Test Item：`.
`ParsingTimeout`	`600 seconds`	Accounts for the complexity of large batch production records or stability reports, providing ample parsing time to prevent failures due to large file sizes.

Common Pitfalls

Observation: Certain critical data fields are empty after parsing. Reason: The default parser failed to recognize complex tables or embedded text within images in the document.
Observation: Knowledge base search results have low relevance, or retrieved chunks are semantically incomplete. Reason: The Chunk size setting is too small, leading to excessive splitting of specialized terminology or key descriptions and loss of context.
Observation: After uploading a document, the system reports Cannot redefine property: toString. Reason: mineru api or other third-party parsers conflict with FastGPT internal components when processing specific PDF formats. This can often be resolved by updating the parser version or adjusting the parsing strategy.

Verification Steps

Select a typical monoclonal antibody quality document containing tables and graphs. Upload it and review the parsed chunks. Verify the completeness of table data and the recognition of key text in graphs.
Perform retrieval tests for specialized terms and acronyms in the document. Check if relevant chunks are accurately recalled and assess the completeness of their context.
Parse documents of varying lengths and complexities. Observe if parsing times are within the expected range and check for timeouts or parsing failures.
Randomly select several parsed chunks. Manually verify if their content aligns with the semantic logic of monoclonal antibody quality documents, especially if key batch information and quality control parameters are correctly extracted.

Note: The values provided 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.