Data Characteristics
Surgical robot quality documentation primarily originates from manufacturer design documents, test reports, risk assessments, user manuals, and regulatory guidance. These documents update infrequently, typically with product iterations, regulatory changes, or major events, not daily or weekly. The document structure is semi-structured, containing extensive text descriptions, diagrams, flowcharts, and tables. Fields and units are highly specialized. For example, "repeatability" is typically in micrometers (µm), "force feedback threshold" might be in Newtons (N) or milliNewtons (mN), and "range of motion" is measured in degrees (°) or millimeters (mm). Documents also contain numerous specialized terms, abbreviations, and industry-specific standard codes for medical devices.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The low update frequency of surgical robot quality documentation means real-time requirements for knowledge base construction are low. However, completeness and accuracy of document content are critical. The semi-structured document structure implies traditional text segmentation methods may not capture key information. More refined parsing strategies are necessary, such as recognizing and extracting text from tables and diagrams, which increases preprocessing complexity. Highly specialized fields and units require accurate entity recognition during tool calls and semantic consistency when calling external tools (e.g., unit converters, specialized terminology lookups). Furthermore, documents contain specific standard codes and abbreviations. Plugins require extensibility to integrate with internal glossaries or external databases, providing accurate contextual explanations and data validation to prevent incorrect calls due to misinterpretation.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000-4000 characters | Quality documents often contain lengthy professional descriptions, requiring a larger context window to maintain semantic integrity and prevent fragmentation of key information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Surgical robot documents can contain many complex diagrams and embedded objects. File parsing can be time-consuming, so increasing the timeout reduces parsing failures. |
Chunk size (Segment Length) | 800-1000 characters | Considering the coherence of specialized text, a longer segment length helps preserve the complete semantics of a paragraph, reducing information loss from excessive truncation. |
Recall count (Recall Count) | Top 8-12 items | The specialized nature of quality documents means a single query may require more relevant contextual information to support an answer. Appropriately increasing the recall count improves comprehensiveness. |
Similarity threshold (Similarity Threshold) | 0.78-0.85 | For strict quality documents, a higher similarity threshold is necessary to ensure the precision of recalled content, avoiding interference from irrelevant or low-relevance content and ensuring accurate tool calls. |
Rerank result count (Reranked Return Count) | Top 5 items | Building on a higher recall count, reranking to select a smaller number of the most relevant items can improve the efficiency and accuracy of tool calling or plugin processing. |
Common Pitfalls
400 Bad Requesterrors when calling external APIs often occur because the parameter type or format passed to the API does not match expectations. An example is passing a string unit to an interface expecting a numerical value.- Slow or timed-out knowledge base search results often stem from inefficient indexing due to a failure to effectively extract key fields during document parsing.
- Empty result fields after a tool call may indicate the plugin failed to correctly parse specialized terms or abbreviations in the API response, preventing mapping to predefined output fields.
Verification Steps
- Simulate user queries to check if FastGPT accurately identifies specialized terms, units, and standard codes in documents and triggers corresponding tools or plugins.
- Verify that the results returned after tool calls or plugin execution contain the expected data fields, and that data types are consistent with the original document or external API response.
- Check FastGPT's stability in content extraction and knowledge base construction when processing documents with diagrams, tables, or complex layouts by comparing original documents with segmented results.
- Execute a series of numerical queries with units to verify that tools or plugins correctly perform unit conversions or numerical comparisons, ensuring results comply with industry standards.
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.