Tool Calling and Plugins for Surgical Robot Procedures

Data Characteristics for This Category

Surgical robot procedure and SOP documents primarily originate from internal quality management departments, equipment departments within medical institutions, and manufacturer operation manuals. These documents are often in PDF, Word, or scanned image formats. They contain extensive structured and unstructured information. Update frequency is relatively low, typically occurring quarterly or annually, coinciding with regulatory revisions, equipment upgrades, or clinical practice optimizations.

Document structures are rigorous. They usually include chapter titles, numbering, version numbers, effective dates, revision records, operating procedures, precautions, and troubleshooting guides. Fields may involve device model, serial number, calibration parameters, consumable batch, and operator qualifications. Units include precise engineering and medical measurements such as mm, °, N, and ml.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The data characteristics of surgical robot procedure documents place specific demands on tool calling and plugins. Low document update frequency means high initial indexing costs. However, subsequent incremental update pressure is lower. The focus is on ensuring update mechanisms accurately identify version differences.

Diverse document formats require robust file parsing capabilities. High OCR recognition accuracy is critical for scanned images, ensuring key fields like device model are correctly extracted. Precise engineering and medical units in documents require tool calls to accurately identify and process these units. This avoids operational risks from unit conversion errors.

Extensive structured information and operating procedures mean plugins must precisely extract specific steps or parameters during question answering. This may require integration with external databases for verification, for example, to check if operator qualifications meet requirements.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Procedure documents may contain many images or scanned pages, leading to larger file sizes. This value accommodates most cases.
`PARSE_FILE_TIMEOUT`	`600 seconds`	Complex PDF or OCR tasks can be time-consuming. Increasing the timeout prevents parsing interruptions.
`chunk_size`	`800–1200 characters`	Procedure clauses have moderate paragraph lengths. This range ensures semantic completeness without being too long, which could affect recall efficiency.
`overlap_size`	`100 characters`	Ensures context continuity and prevents important information loss due to segmentation.
`max_tokens`	`2048`	Ensures the model can process longer procedure descriptions or operating steps, avoiding truncation of critical information.
`similarity_threshold`	Calibrated by actual measurement, `0.75-0.85` recommended	Procedure Q&A requires high accuracy. This range balances recall rate with filtering out irrelevant low-quality content.

Three Common Pitfalls

External API calls return empty or incomplete results, preventing final answer generation. This can be due to external API authentication failure or incorrect request parameter formats, especially when passing specific fields like device serial number or consumable batch without proper encoding or type conversion.
Uploaded documents remain in an "indexing" state for extended periods, preventing Q&A. This occurs when the document parser takes too long for OCR recognition on complex scanned PDFs, or when PARSE_FILE_TIMEOUT is set too short, causing parsing tasks to be interrupted.
When a user asks "What are the calibration parameters?", multiple irrelevant parameters or information are returned. This may be because similarity_threshold is set too low, recalling semantically dissimilar but literally matching snippets, failing to precisely locate the definition of specific calibration parameters.

How to Verify Configuration

Upload various formats (PDF, Word, scanned images) of procedure documents. Check if they parse and index successfully within the specified time, especially complex documents with tables and diagrams.
Ask questions about specific device model, operating procedures, or fault codes within the documents. Verify that the tool-calling plugin accurately identifies and extracts relevant information, comparing it with the original document if necessary.
Design complex questions involving external API calls, such as querying the expiration date of a specific consumable batch (assuming this information is retrieved from an external system). Check if the plugin successfully initiates HTTP requests and returns correct data.
Through iterative testing, gradually adjust parameters like similarity_threshold and chunk_size. Continue until relevant content is recalled, irrelevant information is effectively filtered out, and high-quality answers are generated.

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.