Knowledge Base Retrieval and Recall for Ophthalmic R&D

Data Characteristics

Ophthalmic R&D document data originates from clinical trial reports, drug submission materials, academic papers, patent literature, and internal research records. These documents update frequently. Clinical trial data and academic papers, in particular, may see new developments monthly or even weekly.

Document structures vary. Clinical trial reports typically include standardized sections like abstracts, research methods, results, and discussions. Patent literature follows fixed formats such as claims and specifications. However, internal research records and early exploratory reports may have more flexible structures.

Fields and units involve specialized ophthalmic terminology and measurements like LogMAR, Snellen for visual acuity, mmHg for intraocular pressure, mm² for lesion size, and μg/mL for drug concentration.

Constraints on Knowledge Base Retrieval and Recall

The high update frequency of ophthalmic R&D documents demands an efficient incremental update mechanism for the knowledge base. This ensures the timeliness of retrieval results.

Standardized document structures facilitate precise filtering and recall using metadata. However, the presence of unstructured documents increases the difficulty of information extraction.

Identifying specialized terminology and measurement units is critical. General vector models may struggle to accurately capture semantic relationships, leading to recall bias. For example, converting and understanding different visual acuity units, or describing specific ocular disease manifestations, requires domain knowledge from the model.

Documents often include visual information like images and charts. Retrieval for this content requires considering the correlation between image features and text descriptions. Failure to do so may lead to the omission of critical data.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`500–800 characters` (characters)	Balances the completeness of paragraphs in ophthalmic documents with the processing capabilities of vector embedding models, preventing context loss from over-segmentation.
`Chunk Overlap Length` (Overlap Size)	`50–100 characters` (characters)	Ensures semantic continuity at paragraph boundaries, improving retrieval recall, especially when critical information spans multiple paragraphs.
`Recall count` (Recall Count)	`Top 8–12 entries` (top 8–12 chunks)	The specialized nature of ophthalmic R&D documents requires sufficient relevant context in the initial recall phase for subsequent re-ranking and LLM processing.
`Similarity threshold` (Similarity Threshold)	Calibrate by measurement	Requires testing against specific ophthalmic query scenarios and corpus characteristics to balance recall and precision, avoiding noise.
`Rerank result count` (Re-ranked Count)	`Top 3–5 entries` (top 3–5 chunks)	Selects the most relevant document snippets using a more complex re-ranking model based on initial recall, reducing the input burden on the LLM.
`maxContext`	`4096–8192 token`	Ensures the LLM can process all relevant context after re-ranking, covering background information required for complex queries.

Common Pitfalls

Retrieval results contain numerous irrelevant or low-relevance document snippets. This occurs when the Similarity threshold (Similarity Threshold) is set too low, failing to effectively filter noise.
Queries involving specific ophthalmic charts do not return relevant image information. This happens when image content is not effectively vectorized or associated with text descriptions.
Queries for the latest clinical research advancements do not include the most recent data. This indicates that the knowledge base's incremental update mechanism was not triggered promptly or failed to process.

Validation Steps

Build a test set using a batch of ophthalmic R&D documents. Include documents with new and old knowledge, different structures, and specialized terminology.
Design representative query statements for key information within the test set. These queries should cover various intentions and complexities.
Check if the Recall count (Recall Count) and Rerank result count (Re-ranked Count) meet expectations for each query. Manually evaluate the accuracy and completeness of the recalled content.
Pay close attention to queries involving specialized terms, units, or chart content. Verify that relevant information is correctly identified and recalled.

Note: The values provided are common starting points. Measure against specific samples to determine optimal settings.

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.