Knowledge Base Retrieval and Recall for Surgical Robot

Data Characteristics

Data for surgical robot clinical trial pre-screening primarily originates from medical device registration documents, clinical study protocols, ethics approvals, subject informed consent form templates, device manuals, and relevant medical literature. These documents are typically in PDF, DOCX, or scanned image formats. Update frequency is relatively low, mainly occurring during device version iterations, expanded indications, or significant clinical protocol adjustments. Document structures are complex, containing extensive specialized terminology, charts, tables, and appendices. Key information, such as inclusion/exclusion criteria, complications, adverse events, surgical procedures, device parameters (e.g., arm length 1.5 Meters, accuracy 0.5 Millimeters), and consumable lists with batch numbers (e.g., SN20230101), are scattered across different sections. Data field units generally show good consistency; for example, length units are often millimeters (mm) or centimeters (cm), and time units are seconds (s) or minutes (min), but expressions may vary between documents.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The complexity of surgical robot clinical trial pre-screening documents requires the knowledge base to handle deeply nested text structures and multimodal information, such as extracting key data from charts. Low document update frequency means the knowledge base needs comprehensive and meticulous parsing during initial construction. Subsequent incremental update pressure is low, but historical data integrity and consistency must be ensured. The precision of specialized terminology and device parameters is crucial for recall accuracy. The tokenization strategy must identify these specific entities, for example, indexing "达芬奇手术机器人" as a single unit. Documents contain numerous tables and structured data; traditional text segmentation may lead to critical information loss. Therefore, finer paragraph segmentation and metadata annotation are necessary. Additionally, varying expressions across documents require the recall mechanism to possess semantic understanding capabilities, recognizing synonyms and similar expressions to avoid missed recalls due to inconsistent phrasing.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`500-800 characters`	Balances context completeness with retrieval efficiency, preventing overly long paragraphs from diluting key information.
`Chunk overlap`	`100-150 characters`	Ensures semantic coherence at paragraph boundaries, reducing information fragmentation.
`Maximum Paragraph Depth`	`5`	Adapts to complex document structures, such as nested chapters and lists, ensuring deep content parsing.
`Recall count`	`Top 8-12 entries`	Reduces computational load for subsequent re-ranking while ensuring coverage.
`Similarity threshold`	`0.75-0.85`	Balances recall precision and recall rate; a high threshold helps filter irrelevant results.
`Rerank result count`	`Top 3-5 entries`	Focuses on the most relevant results, improving efficiency and accuracy for the end-user.

Common Pitfalls

Recall results contain numerous irrelevant or duplicate paragraphs. This happens when Chunk size is set too large or Similarity threshold is too low, leading to imprecise segmentation granularity.
Queries for specific device models or complications return no or incomplete information. This occurs if the knowledge base does not effectively identify and index specialized terminology, or if critical fields in tables are not correctly extracted during document parsing.
Uploading large PDF documents results in a PARSE_FILE_TIMEOUT_SECONDS error. This is due to UPLOAD_FILE_MAX_SIZE file processing limits or parser performance bottlenecks, preventing timely text extraction and segmentation.

Configuration Verification

Select typical query statements, such as phrases containing a specific device model 达芬奇 Xi, surgical method, or complication 气胸. Check if the recall results include key paragraphs from relevant documents and verify the completeness of the recalled paragraphs.
Upload and parse multiple representative documents. Check if the number of paragraphs in the knowledge base backend matches expectations. Randomly sample several paragraphs to confirm logical segmentation and complete retention of key information.
Simulate actual pre-screening scenarios. Evaluate the ranking quality of recall results for queries of varying complexity. Confirm that the most relevant paragraphs are ranked highly. Adjust Similarity threshold and Rerank result count based on feedback from business experts.

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.