Model Integration and Configuration for Surgical Robot

Data Characteristics

Surgical robot regulations and SOP documents primarily originate from internal hospital quality management departments, equipment departments, and manufacturer operation manuals. These documents are typically in PDF, Word, or scanned image formats. Content covers equipment operation procedures, maintenance standards, emergency plans, personnel training requirements, and ethical review guidelines. Update frequency is relatively low, usually occurring with new equipment introductions, technology upgrades, or policy adjustments. Document structures often feature numbered steps in SOPs and clause-based formats for regulations. Fields may include equipment model, serial number, operator qualifications, risk levels, and consumable batch numbers. Units involve time (minutes, hours), quantity (times, units), and temperature (Celsius). Documents may also contain numerous charts, flowcharts, and specialized terminology.

Constraints on Model Integration and Configuration

Dispersed document sources and diverse formats require model integration to support multiple file types, especially OCR capabilities for PDFs and scanned images. Low update frequency means high initial knowledge base construction costs but lower subsequent maintenance. Model training or embedding should focus on content accuracy and completeness, reducing real-time requirements. The step-by-step structure of SOPs and clause-based regulations demand specific text chunking strategies to ensure semantic integrity and avoid splitting critical steps or clauses. The presence of specialized terminology and charts increases text embedding difficulty, potentially requiring custom word vector models or multimodal embedding models for improved understanding. Standardized fields and units help build structured metadata, enabling attribute-based filtering during retrieval.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`chunkOverlapRatio`	`0.1`	Ensures context continuity and prevents truncation of key information at chunk boundaries.
`Chunk size`	`800–1200 characters`	Balances information density per chunk with model processing length limits; SOP steps are often long.
`embeddingModel`	`multimodal-embedding-v1`	Better handles documents with charts and complex layouts, improving semantic understanding.
`Recall count`	`Top 5 entries`	Regulation and SOP Q&A requires high precision; increasing retrieval range covers more relevant clauses.
`Similarity threshold`	`0.75`	Improves precision of retrieval results and reduces irrelevant content interference; fine-tune based on actual performance.
`RAG_PROMPT`	Calibrate based on actual tests	Optimizes for specialized terminology and Q&A patterns in the surgical robot domain, improving answer quality.

Three Common Mistakes

Symptom: Model responses frequently state "cannot find relevant information" or are overly generic. Reason: Low OCR recognition rate for PDFs or scanned images during document parsing, leading to ineffective extraction of original content.
Symptom: In an intranet environment, model API calls time out or return connection errors. Reason: The FastGPT deployment environment cannot access external model APIs. Configure a proxy server or the API_BASE address for internally deployed models like deepseek.
Symptom: Q&A results for a specific operation step are incomplete or skip critical parts. Reason: The Chunk size (chunk length) is set too small, causing continuous steps in SOPs to be split into different chunks, losing contextual relevance.

How to Verify Configuration

Select typical surgical robot operation procedures or regulation clauses and conduct multiple rounds of questioning to check answer accuracy and completeness.
Through the FastGPT management interface, check the chunk count and chunk content of imported documents in the knowledge base to confirm text chunking meets expectations.
Use the system's debugging tools to observe the retrieved items and similarity score when the model processes specific queries, evaluating retrieval quality.
Simulate API calls in an intranet environment to ensure the model responds stably and quickly, without connection or authentication errors.

The values provided are common starting points and should be measured 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.