Tool Calling and Plugins for Ophthalmic Pharmacovigilance

Data Characteristics in this Category

Ophthalmic pharmacovigilance data originates from clinical trial reports, real-world studies, adverse event reporting systems (e.g., FAERS, EudraVigilance), and medical literature. Data update frequencies vary. Clinical trial data is typically released in batches after trials conclude, while adverse event reports are continuous and high-frequency. Document structures are diverse, including unstructured free text descriptions (e.g., patient complaints, signs, examination results), semi-structured case report forms (CRFs), and structured coded data (e.g., MedDRA terms, ICD-10 codes). Common fields include patient demographics, medication history, adverse event descriptions, event onset and resolution times, severity, outcome, and causality assessment. Units involve dosage (mg, μg), frequency (times/day), duration (days, weeks, months), and various physiological measurement units.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The multi-source and heterogeneous nature of ophthalmic data presents integration challenges for tool calling, requiring plugins to handle diverse data input formats. The high frequency of adverse event report updates necessitates real-time or near real-time data ingestion capabilities for tools to quickly respond to new safety signals. The presence of unstructured text, especially detailed descriptions of ocular symptoms and signs, makes the accuracy of Natural Language Processing (NLP) tools critical. Plugins must accurately extract key information, such as specific ocular adverse reactions (e.g., blurred vision, increased intraocular pressure, conjunctival hyperemia). Structured coded data requires tools to correctly map and parse MedDRA or ICD-10 codes, ensuring terminology consistency. Furthermore, due to the sensitivity of pharmacovigilance, higher demands are placed on data processing robustness, traceability, and error handling mechanisms to prevent missed or erroneous reports caused by data parsing failures.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`maxContext`	`2000 characters`	Ensures capture of critical details in adverse event descriptions, especially complex descriptions of specific ocular symptoms.
`Chunk size` (Segment Length)	`500 characters`	Accommodates paragraph lengths in case reports, balancing semantic completeness with recall efficiency.
`Recall count` (Recall Count)	`top 8 entries`	Increases the likelihood of retrieving relevant medical literature or guidelines from the knowledge base, covering various ophthalmic drug adverse reactions.
`Similarity threshold` (Similarity Threshold)	`0.75`	Filters out low-relevance documents, focusing on context highly matching ophthalmic adverse events.
`PARSE_FILE_TIMEOUT_SECONDS`	`180 seconds`	Handles parsing time for large clinical trial reports or multi-page PDF documents, preventing timeouts.
`HTTP_REQUEST_TIMEOUT`	`60 seconds`	Ensures timely responses from external API calls (e.g., MedDRA coding services), reducing waiting times.

Three Common Mistakes

An external MedDRA coding service call returns HTTP 400 Bad Request because the input parameter term contains non-standard characters or is incorrectly formatted.
After an HTTP request node executes in the workflow, the expected field eye_adverse_event is empty because the NLP tool failed to identify specific ocular adverse reaction descriptions from free text.
After tool calling, the model output lacks an explanation of the association between a specific drug and an ocular adverse reaction. This occurs because the knowledge base recall of literature is insufficient to support the association analysis, or Recall count (Recall Count) is set too low.

How to Confirm Correct Configuration

Using the FastGPT debugging interface, input case descriptions containing various ocular adverse reactions into the model. Verify that it correctly identifies and outputs key information, such as specific terms like blurred vision and increased intraocular pressure.
Execute a workflow that includes an external tool call, such as querying MedDRA codes. Under the HTTP_REQUEST_TIMEOUT configuration, verify that the tool returns the correct coded results within the expected time and check that response.status is 200.
Upload a new ophthalmic pharmacovigilance report. After the knowledge base updates, observe whether the literature cited by the model in response to related questions is accurate and comprehensive. Also, evaluate the actual effect of the Similarity threshold (Similarity Threshold) by assessing the relevance of recalled entries.

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.