Data Characteristics in This Category
Ophthalmology product data primarily comes from drug registration approvals, medical device registration certificates, clinical trial reports, drug inserts, treatment guidelines, and academic literature. Data update frequencies vary by source. Drug approvals and device registration certificates typically have longer update cycles. However, inserts and treatment guidelines are revised periodically as clinical evidence accumulates, generally every six months to two years. Academic literature has the highest update frequency, almost continuous. In terms of document structure, drug inserts and registration certificates are often structured or semi-structured PDF documents, containing clear fields such as indications, dosage and administration, adverse reactions, and contraindications. Clinical trial reports may contain a large amount of unstructured free text. Fields and units are highly specialized, for example, intraocular pressure (IOP, unit mmHg), visual acuity (Snellen fraction or LogMAR), visual field defect degree (dB), and drug concentration (mg/mL or %).
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The highly specialized nature of ophthalmology product data and the diverse document structures demand accuracy and robustness from tool calling and plugins. Due to varying data source update frequencies, flexible synchronization mechanisms are necessary to prevent models from responding based on outdated information. For example, revisions to drug inserts might involve significant changes to dosage or contraindications. Failure to synchronize these changes promptly could lead to erroneous recommendations. Additionally, a large number of specialized terms and abbreviations, such as "Central Retinal Artery Occlusion (CRAO)" or "Intravitreal Injection (IVT)," require the model to recognize and correctly understand them. Extracting data from unstructured clinical trial reports requires more powerful natural language processing capabilities and entity recognition tools. The complexity of ophthalmic diseases also means that multiple products or treatment plans may interact. Tool calling needs to integrate information from different data sources for comprehensive judgment.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4096 | Ophthalmology is terminology-dense; sufficient context is needed to maintain semantic integrity. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Processing large PDF inserts or clinical reports can be time-consuming for file parsing. |
Chunk size | 800–1200 characters | Balances semantic coherence with RAG recall efficiency, preventing long paragraphs from diluting key information. |
Recall count | Top 8 entries | Ensures coverage of multi-source information, such as product-related snippets from inserts, guidelines, and clinical literature. |
Similarity threshold | 0.75 | Ophthalmology terms require high precision; increasing the threshold reduces irrelevant or ambiguous recalls. |
Rerank result count | Top 3 entries | After reranking, focuses on the most relevant core information, improving response accuracy. |
Three Common Mistakes
- Symptom: The model provides incomplete or incorrect information about the indications for an ophthalmic drug. Reason: The latest insert for the drug in the knowledge base was not synchronized in time, or critical information was truncated during document segmentation.
- Symptom: When calling an external tool to query the normal range of intraocular pressure, the returned result is empty or has an incorrect format. Reason: The API parameters for the external tool are configured incorrectly, or the returned data units do not match expectations, leading to parsing failure.
- Symptom: For complex ophthalmic cases, the model cannot integrate information from multiple products to provide recommendations. Reason: The workflow design did not adequately consider coordinated calls to multiple tools, or there was a lack of effective integration mechanisms for data returned by different tools.
How to Confirm Correct Configuration
- For core ophthalmology products, simulate user queries and verify that the model's answers to key information (e.g., indications, dosage, adverse reactions) align with official inserts.
- Test queries containing specialized terms and abbreviations to ensure the model can correctly identify and respond using the knowledge base or tools, and check if the response includes necessary specialized units.
- Execute workflows involving file uploads and parsing. Check if file content is extracted correctly and if extracted key fields can be effectively utilized by subsequent tools or models.
- Validate external tool call chains. Ensure each call returns data in the expected format, and that numerical values and units in the data conform to presets.
The values given 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.