Monoclonal Antibody Products: Vector Models and Indexing

Data Characteristics for Monoclonal Antibody Products

Monoclonal antibody product data originates primarily from public databases (e.g., DrugBank, PubChem, PDB), pharmaceutical company websites, academic papers, and patent documents. Data update frequencies vary; public databases might update monthly or quarterly, while papers and patents are continuously published. Document structures typically include structured product information (trade name, generic name, target, indications, mechanism of action, molecular weight, sequence information, manufacturer, batch number, purity, potency, storage conditions) and unstructured experimental data, clinical study reports, and quality control documents. For fields, molecular weight is often in Da or kDa, purity in percentages, and potency involves biological activity units like IU/mg or μg/mL. Sequence information is usually amino acid or nucleotide strings.

Constraints Imposed by These Characteristics on Vector Models and Indexing

The mixed highly structured and unstructured nature of monoclonal antibody data requires vector models to effectively process text, numerical, and sequence information. The specificity of sequence information (e.g., antibody CDR regions) may necessitate specialized preprocessing or integration with bioinformatics tools for feature extraction to capture functional relevance. Inconsistent update frequencies mean indexing strategies must support incremental updates to avoid full rebuilds. For example, newly published clinical data or patents might only affect a subset of documents. Furthermore, diverse units and fields require standardization or normalization before vectorization to ensure comparability in the semantic space. For instance, kDa and Da need unit unification, and percentage fields should convert to numerical values.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Size)	`800–1200 characters`	Balances the completeness of monoclonal antibody product descriptions with vector model processing efficiency, preventing semantic fragmentation.
`Recall count` (Recall Count)	`Top 10–15 items`	Ensures broad initial recall, covering potentially relevant results and providing sufficient candidates for subsequent re-ranking.
`Similarity threshold` (Similarity Threshold)	Calibrate by measurement	Adjust based on actual query performance and expert feedback, typically between `0.7–0.85`.
`Vector Model` (Vector Model)	`bce-embedding` or `text-embedding-3-large`	Prioritize models that understand biomedical terminology well, balancing performance and cost.
`Rerank result count` (Re-ranked Return Count)	`Top 3–5 items`	Focuses on the most relevant results, improving the precision of the final output.
`Indexing Update Strategy`	`Incremental update`	Addresses the continuous update nature of monoclonal antibody data, reducing resource consumption and maintaining data timeliness.

Three Common Pitfalls

error: { message: 'This Token Is Not Authorized To Use The Model：text-embedding-3-large' (error: { message: 'This token is not authorized to use model: text-embedding-3-large'}): This error typically indicates insufficient permissions for the API key configured in the FastGPT backend, or that the corresponding channel in the OneAPI platform is not correctly linked to the required model.
Knowledge base search response time is too long: This can relate to insufficient computing resources for the vector model. For example, with locally deployed models like shaw/dmeta-embedding-zh, if hardware (e.g., an RTX2070 graphics card) does not match the data volume, inference speed can be slow.
Search results do not meet expectations or have low relevance: A common cause is an unreasonable chunking strategy, leading to critical information being truncated or mixed with irrelevant text, which affects vector quality.

How to Verify Configuration

Through the FastGPT administration interface, verify that the Vector Model (Vector Model) configuration item correctly selects a model supporting the biomedical domain, and check the API key status.
Execute a series of test queries containing specialized terminology (e.g., CD20, IgG1, FcRn), check the relevance of recall results, and confirm with domain experts.
Monitor knowledge base index update logs to confirm that incremental update tasks execute successfully at the expected frequency, and check for any indexing failures due to data format issues.
Perform stress tests during peak hours to check the average response time of knowledge base searches and compare it against defined performance metrics to ensure stable system operation under high load.

Note: The values provided 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.