Multi-Turn Conversations and Prompts for Ophthalmological

Data Characteristics

Ophthalmological R&D documents include clinical trial reports, drug monographs, research papers, and disease atlases. Data sources are diverse. New drug development cycles are long, but clinical trial data updates periodically, especially when trial progress, adverse events, and efficacy data are disclosed. Documents have complex structures, often containing extensive medical terminology, abbreviations, charts, and formulas. Fields include vision measurements (e.g., LogMAR, Snellen), intraocular pressure (mmHg), ocular anatomical structure names, drug dosages (mg/kg), and administration frequencies. File formats vary, with PDF, DOCX, and scanned images being common. PDFs often contain both text and embedded charts.

Constraints on Multi-Turn Conversations and Prompts

The complex structure and specialized terminology of ophthalmological R&D documents demand deep understanding in multi-turn conversations. When a user asks about a specific drug's efficacy in a particular ophthalmic disease, the system must accurately identify the disease name, drug mechanism of action, key efficacy indicators, and their units. It must also track subtle differences in context to avoid confusing data from different trial phases. For example, when asked about vision improvement, the system needs to differentiate between LogMAR and Snellen visual acuity scales and maintain consistency throughout the conversation. The presence of numerous charts and scanned documents means text-only parsing may be insufficient, requiring stronger document preprocessing capabilities to extract key information. Furthermore, uncertain update frequencies require the knowledge base to have version management capabilities, ensuring multi-turn conversations are based on the latest and most accurate data.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8`	Ensures the model maintains conversational coherence in the context of complex ophthalmic terminology and long sentences, preventing loss of critical information.
`Chunk size` (Segment Length)	`500–700 characters` (characters)	Ophthalmological literature contains lengthy descriptive paragraphs and complex logical chains. A moderate length helps maintain contextual integrity.
`Recall count` (Recall Count)	`8–12 entries` (items)	Ensures sufficient relevant document segments are covered when facing highly specialized, information-dense queries, improving accuracy.
`Similarity threshold` (Similarity Threshold)	`0.75`	For the high specificity of medical terminology, increasing the threshold filters for more precise recall results, reducing interference from irrelevant information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Ophthalmological R&D documents often contain many pages and complex structures, requiring a longer parsing time to ensure comprehensive content extraction.
`Rerank result count` (Reranked Return Count)	`5 entries` (items)	After initial recall, selecting a few of the most relevant items for reranking improves the quality and focus of the final answer.

Common Mistakes

The conversation interface does not automatically set a default question, requiring users to manually enter the initial query. This happens because the conversation start event is not bound to a preset userMessage parameter.
Uploaded PDF documents consistently fail to parse, with backend logs showing Error: Document parsing failed. This may be due to PARSE_FILE_TIMEOUT_SECONDS being set too low, causing parsing to time out for documents with many scanned pages or complex charts.
Copying and pasting conversation content into external applications results in lost formatting, appearing as plain text. This occurs because the system's default output is Markdown, but the target application does not support or correctly render Markdown format, or the renderMarkdown option is not enabled.

Verification Steps

Upload an ophthalmological clinical trial report PDF containing charts and specialized terminology. Confirm the file parsing status is "successful" and key information is retrievable from the knowledge base.
Engage in a multi-turn conversation. For example, first ask about the main efficacy indicators of a drug in glaucoma treatment, then follow up on the specific numerical range of that indicator. Observe if the system accurately understands and provides coherent, context-aware responses.
Ask questions containing ophthalmology-specific units like LogMAR or mmHg. Verify that the numerical values and units in the returned results correctly match the document content.

Note: The values provided are common starting points. Measure them against your 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.