Data Characteristics
Ophthalmic quality documents originate from various sources: national regulatory agency guidelines, industry association technical standards, internal quality management system documents (e.g., SOPs, batch production records, inspection reports), and clinical trial data. Update frequencies vary; regulatory documents typically see annual revisions, while internal documents may update quarterly or semi-annually due to process improvements, equipment changes, or new regulations. Document structures differ: regulatory documents often use numbered sections, while internal SOPs combine step-by-step lists with flowcharts. Fields and units adhere to strict dimensional and precision requirements for ophthalmic devices and drug production parameters, such as intraocular pressure (mmHg), visual acuity (LogMAR), and drug concentration (mg/mL).
Constraints on Knowledge Base Retrieval and Recall
The stringent regulatory nature and high update frequency of ophthalmic quality documents demand that knowledge base retrieval ensures timely and accurate recall results. Incorrect recall of outdated regulations or SOPs can lead to severe compliance risks. The structured nature of documents (e.g., section numbers, appendices) requires refined segmentation strategies to avoid splitting critical information or introducing redundancy. The extensive use of specialized terminology and abbreviations (e.g., OCT, IOL) challenges the understanding capabilities of text vectorization models, potentially leading to insufficient semantic matching. Furthermore, the precision requirements for various units and numerical values mean simple keyword matching is inadequate; deeper numerical range or unit conversion awareness is necessary. Anonymization in clinical data also increases data preprocessing complexity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Balances the completeness of regulatory clauses with the information density of a single segment, reducing context loss. |
Chunk Overlap Length (Segment Overlap Length) | 50–100 characters | Ensures the relevance of key information across segments, improving recall coherence. |
Recall count (Recall Count) | Top 10 | Covers potentially relevant information while controlling the computational load for subsequent reranking. |
Similarity threshold (Similarity Threshold) | Calibrate empirically, suggest 0.75–0.85 range for gradual adjustment | Balances recall completeness and precision, preventing interference from irrelevant documents or omission of important information. |
Rerank result count (Rerank Return Count) | Top 3 | Focuses on the most relevant core information, reducing user reading burden. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Accommodates the parsing needs of large PDFs or structurally complex documents, preventing file indexing failure due to timeouts. |
Common Pitfalls
- Network search parsing JSON errors. This may occur if the external API returns JSON in an unexpected format, or if network instability causes data transmission interruptions.
- Thinking model output garbled after adding a knowledge base. This may be due to an encoding mismatch between the knowledge base content and the model's expected encoding, or incorrect decoding of text returned by the vector database during transmission.
- Knowledge base does not automatically index images after local deployment. This typically happens if the image processing service is not correctly configured or started, or if the document parser does not support extracting and textualizing specific image formats.
Verification Steps
- Upload a typical ophthalmic SOP document. Check if the document is correctly segmented and if segment content maintains semantic integrity.
- Perform searches for specific ophthalmic terminology or regulatory clauses. Evaluate if recall results include all known relevant documents and verify their timeliness.
- Simulate user questions, such as "requirements for quality control of glaucoma medication production." Check if the ranking and content of recall results meet expectations, especially regarding the accuracy of numerical values and units.
Note: The values provided are common starting points and should be measured against specific 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.