Multi-Turn Conversations and Prompts for Surgical Robot

Data Characteristics for This Category

Surgical robot product data originates from various sources, including official manufacturer technical manuals, operation guides, maintenance documents, clinical trial reports, and medical device registration certificates. These documents typically exist as PDFs, Word files, or structured database entries. Data update frequency is relatively low, primarily occurring during product upgrades, new feature releases, or regulatory changes. Document structures are complex, containing extensive specialized terminology, diagrams, parameter lists, and operational procedures. Core fields include product model, serial number, functional modules, technical parameters (e.g., accuracy, degrees of freedom, load capacity), compatible consumables, maintenance cycles, error codes and their solutions, and applicable clinical scenarios. Units involve physical quantities such as millimeters, degrees, Newton-meters, volts, and amperes, as well as time units like hours and cycles.

Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"

The complexity and specialized nature of surgical robot data demand high accuracy in multi-turn conversations. Diagrams and parameter lists in technical manuals are difficult to interpret directly via text embeddings, requiring more refined preprocessing. Low update frequency means knowledge base construction must prioritize initial data completeness and effectively handle subsequent incremental updates. The abundance of specialized terminology requires the model to possess strong semantic understanding to avoid ambiguity in conversations. For example, a question about "accuracy" might involve different concepts such as repeatability and absolute positioning accuracy. Diverse units and field structures, such as the numerical range for "degrees of freedom" or the unit for "load capacity," require prompt design to guide the model in accurately extracting and comparing this information. The conversational system must handle precise user queries for specific models, error codes, or compatible consumables, and progressively refine questions through multi-turn interactions.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`Chunk size`	800–1200 characters	Balances semantic integrity with recall efficiency, adapting to long paragraphs in technical documents
`Recall count`	Top 5 entries	Covers highly relevant knowledge points, avoiding redundant information
`Similarity threshold`	0.78–0.85	Ensures accuracy of recalled content, filtering out low-relevance technical details
`Rerank result count`	3 entries	Refines final output, focusing on core answers, enhancing user experience
`maxContext`	4096 tokens	Supports context length for multi-turn conversations, especially in troubleshooting scenarios
`temperature`	0.3–0.5	Reduces model divergence, ensuring rigor and factual accuracy of responses

Three Common Pitfalls

Frequent 422 errors in early conversations might indicate that the API request body structure does not conform to specifications, for example, missing the model field or incorrect messages format.
Inconsistencies between knowledge base answers and API call results are typically due to outdated knowledge base document versions or incorrect knowledge base ID specification during API calls.
When users ask about consumable compatibility for specific models, the system may fail to provide a concrete list. This can occur if relevant parameters in the knowledge base are not correctly parsed into queryable structured data.

How to Confirm Proper Configuration

Select multiple typical surgical robot troubleshooting scenarios. Observe if the conversational system can provide accurate preliminary diagnoses or solutions within 3-5 turns, and record the token consumption for each conversation.
Randomly select 10 key parameters (e.g., accuracy, power, maintenance cycle) from product technical manuals. Query these parameters through the conversational system and verify consistency with the original manual text. Check the similarity score distribution.
Simulate user queries for compatible consumables of different surgical robot models. Verify if the system returns the correct consumable list for specific models and check if the returned source_nodes point to the correct document segments.

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.