Ophthalmology Regulations: Tool Calling and Plugins

Data Characteristics

Ophthalmology regulations and SOP documents originate from national health commissions, drug administration agencies, and hospital management departments. These include regulations, clinical guidelines, drug instructions, and internal hospital operating procedures. Documents exist as PDFs, Word files, or structured databases. Update frequencies vary: national regulations might update every few years, while internal SOPs may revise annually or with new technologies. Content covers diagnostic criteria for eye diseases, treatment plans, surgical protocols, equipment usage, drug dosages and contraindications, and infection control. Fields and units often involve vision (LogMAR, Snellen), intraocular pressure (mmHg), drug concentration (mg/ml), surgery duration (minutes), and equipment parameters (joules, hertz).

Constraints on Tool Calling and Plugins

The specialized nature and update cycles of ophthalmology documents demand highly accurate semantic understanding for tool calling to prevent misdiagnosis. Extensive technical terms and units require plugins to identify and process this information correctly. For example, converting intraocular pressure to mmHg for comparison or calculation. Irregular document updates necessitate regular data source synchronization and index rebuilding to maintain knowledge base currency. Varied document formats require robust PDF and Word parsing capabilities, and the ability to extract key information from structured data directly impacts answer accuracy. For sensitive information like drug dosages and surgical parameters, tool calling needs strict validation mechanisms to avoid outputting unverified values.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`2000` characters	Ensures inclusion of longer paragraphs from ophthalmology regulations while preventing excessive context that could lead to model misinterpretation or high inference costs.
`Recall Count`	`8` items	Balances recall rate with model processing efficiency, covering multiple relevant regulation entries.
`Similarity Threshold`	`0.75`	Ophthalmology regulation Q&A requires high accuracy; a higher threshold filters out less relevant results.
`Rerank Return Count`	`3` items	Selects the most relevant few regulation clauses, allowing users to quickly locate core information.
`PARSE_FILE_TIMEOUT_SECONDS`	`300` seconds	Accounts for potentially long parsing times for ophthalmology regulation documents, which may contain numerous charts and complex layouts.
`http_plugin_timeout`	`60` seconds	External API calls require sufficient response time, especially when querying drug databases or equipment parameters.

Common Pitfalls

Calling an external drug database API returns a 502 Bad Gateway error. This occurs when the HTTP plugin's configured URL or Request Headers do not match API requirements, or the target service is temporarily unavailable.
Search plugin returns web links that are unreadable, leading to missing critical information. This happens when the plugin defaults to returning only links and brief descriptions, without configuring further webpage content fetching or parsing steps.
The model provides incomplete parameters or steps when answering questions about ophthalmic surgical procedures. This can be due to overly large segmentation granularity in the relevant SOP documents within the knowledge base, preventing precise recall of specific sub-steps.

Validation Steps

Test the Q&A system with questions about typical ophthalmic disease diagnostic processes and drug contraindications. Verify that it accurately cites specific clauses and values from relevant regulations or SOPs, and confirm consistency with original documents.
Use queries containing specialized terminology and units to verify the system's ability to correctly identify and process this information. For example, input glaucoma treatment IOP target and check if the results include mmHg unit ranges.
Simulate a regulation update scenario by uploading a new document. Then, ask related questions to confirm the knowledge base has updated its index and provides the latest information, validating the data synchronization and index rebuilding mechanism.
Call an external drug query plugin with a specific drug name. Check if it successfully retrieves detailed information from the drug instructions and verify that the returned fields match the expected API output.

Note: The values provided are common starting points. Measure performance against your own samples to determine optimal configurations.

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.