Vector Models and Indexing for Bispecific Antibody

Data Characteristics

Bispecific antibody registration dossier data originates from clinical trial reports, pharmaceutical research documents, non-clinical study reports, and quality control files. The update frequency is relatively low, primarily occurring during submission of R&D milestones and supplementary applications. Document structures are complex, containing extensive specialized terminology, charts, and data tables. Fields cover molecular structure, manufacturing processes, pharmacokinetics, pharmacodynamics, toxicology, clinical efficacy, and safety. Units vary, including concentration units like µg/mL, dosage units like mg/kg, time units like hours or days, and various biological activity units. Complex abbreviations and proper nouns are common.

Constraints from Data Characteristics on Vector Models and Indexing

The specialized nature and complex document structures of bispecific antibody data require vector models to effectively capture semantic relationships in the biomedical domain. This avoids critical information loss from over-generalization. Low update frequency means index construction needs to balance efficiency and accuracy. Initial construction must be comprehensive, with subsequent updates primarily incremental. Diverse fields and units challenge text preprocessing, necessitating customized entity recognition and normalization. This ensures different data types are distinguished during vectorization. For example, similar numbers might represent dosage or concentration; lack of context directly impacts recall precision. Embedded tables and charts in documents require advanced parsing to convert them into vectorizable text representations, preventing the omission of critical structured data.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters`	Balances semantic completeness with vector model processing capacity. Avoids diluting key information in overly long texts or losing context in overly short texts.
`Chunk overlap` (Chunk Overlap)	`100–150 characters`	Ensures key information continuity across chunks, improving recall rate.
`embedding_model`	`Doubao-embedding` or other biomedical-optimized models	Prioritizes models pre-trained on biomedical text to improve vector representation accuracy for specialized terminology.
`Recall count` (Recall Count)	`Top 10–15 entries`	Given the complexity of bispecific antibody data, appropriately increases recall quantity to cover potentially relevant information.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Adjusts based on actual recall results, balancing completeness and precision. Typically between `0.75–0.85`.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Allows sufficient file parsing time for large clinical trial reports or pharmaceutical research documents.

Common Pitfalls

A knowledge base remains in a "processing" state for an extended period after switching vector models, preventing normal use. This usually occurs because the new vector model's API key or endpoint is not configured correctly, leading to model call failures.
After uploading PDF documents containing complex tables, table content is missing or inaccurate in retrieval results. This happens when the file parser fails to effectively identify and extract table structures from the PDF, preventing their conversion into vectorizable text.
When retrieving specific molecular names or clinical trial numbers, the recall results contain many irrelevant items, or critical information is omitted. This indicates that professional terminology was not effectively identified and normalized during the text preprocessing stage, leading to semantic drift during vectorization.

How to Verify Configuration

Upload a bispecific antibody non-clinical study report containing key data and specialized terminology. Use keywords and key phrases for retrieval. Check if recall results include core facts and data from the document.
After switching different vector models, check if the embedding_model field in the FastGPT console has updated to the target model. Attempt a small file upload and retrieval to confirm the model channel is functioning.
For specific sections or paragraphs in a document, ask questions through content summarization or key information queries. Evaluate if the generated answers accurately cite the original content. Check if the cited Chunk size (Chunk Length) and Recall count (Recall Count) meet expectations.

The values given are common starting points and should be measured against specific 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.