Knowledge Base Retrieval and Recall for Monoclonal Antibody

Data Characteristics for This Category

Monoclonal antibody registration and submission documents typically originate from internal pharmaceutical company R&D documents, clinical trial reports, manufacturing process records, and regulatory guidelines. These data have a relatively low update frequency, primarily during new drug development and post-market change submissions. Document structures are complex, containing large amounts of unstructured text, charts, chemical structure images, and tabular data. Fields and units are highly specialized. For example, "antibody concentration" is often expressed in mg/mL, "purity" as a percentage, and "potency" may involve complex biological units like IU/mg or relative potency. Documents often include specific terminology such as CDR (Complementarity Determining Region) or Fc segment modification.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The complex document structure and specialized terminology of monoclonal antibody data require the knowledge base to identify and preserve the integrity of key information during chunking. For instance, descriptions involving antibody domains should not be arbitrarily truncated. The low update frequency means initial knowledge base construction requires significant effort in data cleaning and annotation, but subsequent maintenance costs are relatively low. The presence of large amounts of unstructured text and charts demands robust text embedding models and multimodal retrieval capabilities; pure text retrieval may not effectively recall passages containing critical chart information. Accurate identification of specialized fields and units is crucial to avoid retrieval deviations caused by unit conversion or misinterpretation.

Configuration Settings

Configuration Item	Recommended Value	Rationale for This Value
`Chunk size`	`800–1200 characters`	Balances paragraph integrity for monoclonal antibody data with the processing capacity of embedding models, preventing critical information truncation.
`Recall count`	`8–12 entries`	Given the complexity of monoclonal antibody submission data, more context is needed to ensure comprehensive understanding while controlling retrieval load.
`Similarity threshold`	`0.78–0.85`	For specialized terminology and concepts, a higher threshold improves retrieval precision and reduces interference from irrelevant information.
`Rerank result count`	`5 entries`	Refines initial retrieval results, prioritizing core passages most relevant to the query intent.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Monoclonal antibody submission data often includes large PDF files; this provides sufficient time for file parsing.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Allows uploading extra-large PDF files containing numerous charts and data, meeting practical requirements.

Three Common Mistakes

Phenomenon: AI responds with "no answer found," but relevant information clearly exists in the knowledge base. Reason: Key information in the knowledge base is present as images, and pure text retrieval fails to effectively identify and recall image content.
Phenomenon: API call prompts an incorrect knowledge base ID or inability to retrieve knowledge base content. Reason: The knowledge base ID is not correctly passed in the API call workflow, or the knowledgeBaseId passed is not the ID actually used by the system.
Phenomenon: When uploading large PDF files, an "offset out of range" error frequently occurs. Reason: The backend file processing service has defects in handling large file chunking or streaming, or unstable network transmission leads to data corruption.

How to Confirm Proper Configuration

Upload monoclonal antibody submission documents containing key charts and text, then verify that the knowledge base correctly parses and creates embeddings.
Query specific professional fields such as antibody concentration and purity, checking if retrieval results include accurate values and units.
Use long-tail questions related to complex concepts in the submission documents to assess the completeness and relevance of recalled passages.
Simulate an API call, passing the knowledgeBaseId parameter, and confirm that it successfully retrieves and returns expected results.

The values provided are common starting points and should be measured against the reader's 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.