Data Characteristics for This Category
Core data for surgical robot registration and declaration documents originate from clinical trial reports, design documents, risk analysis reports, manufacturing process files, and quality management system documents. This data updates infrequently, primarily during new product development, before clinical trial data submission, and after significant design changes. Document structures typically follow guidelines from national medical product regulatory bodies (such as NMPA) or international medical device regulatory agencies (such as FDA, CE). They exhibit high structuralization, for example, adhering to the annexes specified in the "Requirements for Medical Device Registration and Declaration Documents and Approval Certificate Formats." Fields include device model, software version, hardware parameters, clinical indications, adverse event codes, intended use, mechanism of action, performance indicators (e.g., accuracy, stability, response time), material composition, and sterilization methods. Units strictly follow international standards, such as millimeters (mm), Newtons (N), seconds (s), and degrees Celsius (°C), with clear requirements for precision and error.
Constraints Imposed by These Characteristics on "Tool Calling and Plugins"
The highly structured and infrequently updated nature of surgical robot declaration documents requires tool calling and plugins to precisely parse and extract specific field information. They must also handle long texts and multi-format documents. For example, extracting the incidence rate of specific adverse events from clinical reports requires plugins with complex text pattern matching capabilities. Strict requirements for precision and units mean that during data extraction and transformation, plugins must identify and retain the significant figures and correct units of numerical values to prevent data distortion. Documents often exist in formats like PDF and Word and contain numerous charts, requiring tool calling interfaces to process unstructured content and perform Optical Character Recognition (OCR). Since data updates are infrequent, plugin caching mechanisms can effectively reduce redundant computations and improve efficiency. Furthermore, compliance requirements for the approval process necessitate that plugins support version control and audit trail features to ensure data traceability and processing.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
max_tokens | 4096 | Declaration documents are often lengthy technical documents, requiring the processing of longer contexts. |
ocr_engine_type | High-Precision Text Recognition | Declaration documents often contain scanned copies and complex tables, requiring accurate recognition of image and text content. |
document_chunk_size | 1000–1500 characters (characters) | Balances semantic completeness with retrieval efficiency, avoiding information loss from over-chunking or inaccurate retrieval from over-large chunks. |
embedding_model | text-embedding-large-v1 | Ensures high-dimensional semantic understanding of specialized terminology and technical descriptions. |
tool_timeout_seconds | 300 seconds (seconds) | Complex document parsing and data extraction can be time-consuming; this provides sufficient execution time. |
max_retries_on_failure | 3 times (times) | Increases retry opportunities during network fluctuations or transient external service failures, improving task success rate. |
Three Common Mistakes
- After calling a custom plugin, input and output parameters do not appear in the workflow task: This typically indicates missing or incorrectly formatted
input_schemaoroutput_schemafields in the plugin definition file (e.g.,manifest.json). - When running an AI conversation in a loop, array elements are not processed correctly: The AI conversation call within the loop is not configured to receive individual elements from the array as input, or the array element type does not match the expected input of the AI conversation.
- DDG plugin returns "Failed to get the VQD for query" error: This usually occurs when the query content does not meet the specific requirements of the search engine API, such as containing special characters, an excessively long query string, or API key configuration issues.
How to Verify Configuration
- Use FastGPT's debugging interface to observe tool call input and output logs, confirming that parameter passing and returned results meet expectations.
- Upload typical surgical robot registration and declaration document samples, run the workflow, and verify that extracted key field values (e.g., device model, precision parameters, clinical trial numbers) are accurate, especially numerical values and units.
- Simulate several common error scenarios (e.g., missing critical sections, data format errors) to verify that error handling logic triggers as designed and provides clear prompts.
The values given are common starting points and should be measured against the reader's 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.