Data Characteristics for This Category
Ophthalmic product and reagent data originate from various sources, including drug inserts, medical device registration certificates, clinical trial reports, academic papers, product brochures, operating manuals, and internal training materials. These documents are typically in formats such as PDF, Word, and HTML. Data update frequencies vary; new product launches, expanded indications, adverse event reports, or regulatory changes can trigger updates, but core components and mechanisms of action remain relatively stable. Document structures are often standardized; for example, product inserts have fixed sections like [Indications], [Dosage and Administration], [Contraindications], and [Adverse Reactions]. Clinical reports include [Background], [Methods], [Results], and [Discussion]. Common data fields include generic drug name, brand name, approval number, manufacturer, specifications, expiry date, storage conditions, and adverse event codes (e.g., MedDRA codes). Units cover specialized measurements such as mg/ml, IU, mmHg, and D (diopter).
Constraints Imposed by These Characteristics on Multi-turn Conversations and Prompts
The specialized and structured nature of ophthalmic product data places specific demands on the accuracy of multi-turn conversations and prompt construction. Critical sections in product inserts, such as [Contraindications] and [Adverse Reactions], must be precisely recalled and prioritized in multi-turn conversations to prevent misinformation. Complex statistical data and charts in clinical trial reports require the system to extract key numerical values from unstructured text and present them clearly and concisely in conversations. For example, when a user asks about the side effects of an ophthalmic drug, the system must identify and summarize the incidence of adverse reactions from a large volume of text. Inconsistent update frequencies mean the knowledge base needs a version management mechanism to ensure that queries at different times retrieve the latest information for that period. Additionally, specialized terminology and units unique to ophthalmology require prompt design to effectively guide the model in recognizing these terms and avoiding confusion between similar but distinct concepts, such as significant differences in units and numerical ranges between myopia degrees and intraocular pressure values.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
chunkSize | 800-1200 characters | Ophthalmic product inserts and clinical report paragraphs are often long and contain multiple key pieces of information. Longer chunks maintain contextual completeness. |
overlapSize | 100 characters | Ensures sufficient overlap between adjacent chunks, preventing critical information from being cut and losing context, especially when referencing across paragraphs. |
topK | 5-8 items | Given the large volume of ophthalmic product information, recalling more relevant items increases coverage and reduces the omission of critical information. |
similarityThreshold | 0.75-0.85 | Ophthalmic terminology requires high precision. A higher similarity threshold filters out irrelevant recall results, improving accuracy. |
maxContext | 4000-6000 tokens | In multi-turn conversations, ophthalmic questions may involve multiple products, symptoms, or treatment plans, requiring a longer context window to maintain coherence. |
promptTemplate | Includes role definition like "As a professional ophthalmic product consultant" | Clearly defines the model's role, guiding it to answer ophthalmic questions with a professional and rigorous language style. |
Three Common Mistakes
- Conversation replies fail to cite specific product information from the knowledge base, providing generic responses. This occurs when
similarityThresholdis set too high, causing slightly less relevant documents to not be recalled, and the model lacks sufficient reference information. - When users ask about contraindications for a specific ophthalmic disease, the system returns general descriptions for multiple products, lacking specificity. This happens because knowledge base document chunk granularity is too large, leading to the recall of irrelevant information and making it difficult for the model to pinpoint specifics.
- When asked about the dosage of active ingredients in a specific eye drop, the reply contains incorrect units or values. This is because dosage information in the original documents may have multiple forms of expression, and the prompt fails to effectively guide the model in parsing and standardizing professional measurement units.
How to Verify Configuration
- Select core ophthalmic product inserts and conduct multi-turn questioning on key sections such as [Indications], [Contraindications], and [Dosage and Administration]. Check if replies accurately cite document content and maintain professionalism.
- Simulate a user consulting about an adverse reaction or a special case. Observe if the system can accurately recall and integrate data from clinical trial reports within the conversation, and correctly parse numerical values and units.
- For newly launched ophthalmic drugs or products with updated indications, test if the system can retrieve the latest version of document information and provide accurate answers based on it, evaluating if the knowledge base's update mechanism is effective.
The values provided 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.