Model Integration and Configuration for Bispecific Antibody

Data Characteristics

Bispecific antibody quality documentation includes R&D reports, production batch records, quality standards, analytical method validation reports, and stability study data. These documents are often in various formats like PDF, Word, and Excel, frequently as scanned images containing numerous pictures and tables. Data primarily originates from internal Document Management Systems (DMS) or Laboratory Information Management Systems (LIMS). Document updates are infrequent, typically occurring during batch production, method changes, or regulatory updates, potentially months or years apart. Document structures are complex, combining narrative text with structured data such as test items, methods, units, acceptance criteria, and actual results. Field names may use abbreviations or industry-specific terminology. Units like ng/mL, %, and EU/mg require precise identification.

Constraints on Model Integration and Configuration

The multi-format and scanned nature of bispecific antibody quality documents requires robust document parsing capabilities during model integration, especially support for OCR and table structure extraction. Infrequent document updates mean an initial bulk import of historical data is necessary for knowledge base construction, with subsequent incremental updates being rare. Complex document structures and industry terminology demand advanced text segmentation and embedding model selection. The model must understand contextual meaning to avoid semantic fragmentation. Accurate identification of fields and units is critical. This requires strict post-processing of extraction results or using models with entity recognition capabilities to ensure data accuracy, which directly impacts retrieval and question-answering reliability. Long document content also challenges model context window size and segmentation strategies; overly short segments can lose critical information.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800–1200 characters`	Balances context completeness and retrieval efficiency, avoiding overly long or short segments.
`Chunk overlap`	`100–200 characters`	Ensures semantic continuity at segment boundaries, improving retrieval recall.
`OCR_ENABLED`	`True`	Quality documents often include scanned images; enabling OCR is essential for text recognition.
`TABLE_EXTRACTION_THRESHOLD`	`0.75`	Ensures accuracy of table content extraction, filtering out low-confidence results.
`maxContext`	`32000 tokens`	Accommodates lengthy R&D reports and analysis results, providing sufficient context.
`Similarity threshold`	`0.78`	Balances recall and precision, ensuring retrieved quality documents are highly relevant to the query.

Common Pitfalls

After uploading many documents to the knowledge base, some document content is missing or garbled. This occurs because OCR functionality is not enabled, or the OCR engine performs poorly on specific scanned documents, preventing correct extraction of text and tables from images.
The model returns "cannot find relevant information" when asked about specific test data or batch information. This likely results from an unreasonable segmentation strategy, leading to critical structured data (e.g., results, units in tables) being truncated or ineffectively embedded, making them unmatchable during retrieval.
API calls return a 504 Gateway Timeout error. This happens when processing PDF files containing many scanned images or complex tables, as document parsing takes too long and exceeds the default PARSE_FILE_TIMEOUT_SECONDS parameter setting.

Verification Steps

Randomly select multiple quality documents of different formats (PDF, Word, Excel) and content (R&D reports, batch records). Check their previews in the FastGPT knowledge base to ensure text, tables, and image content are correctly parsed and displayed.
Construct precise queries for specific test items, batch numbers, key results, and units contained within the documents. Verify that the model accurately recalls relevant document segments and uses them as the basis for answers.
Upload a bispecific antibody quality document containing complex tables and scanned pages via the API. Monitor the parsing process response time to ensure completion within the set timeout, and check the completeness of the returned results.

Note: The values provided are common starting points. Measure performance against your own samples for optimal configuration.

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.