Data Characteristics
Imaging equipment procedures and SOP documents primarily originate from manufacturer operation manuals, maintenance guides, and internal medical institution guidelines for equipment use and maintenance records. These documents are often in PDF format; some may be scanned images. Update frequency is relatively low, occurring mainly when equipment models are updated, software is upgraded, or national regulations change. Document structures are complex, containing numerous charts, flowcharts, and specialized terminology such as "CT value," "MRI sequence," and "ultrasound probe frequency." They also involve specific equipment models, serial numbers, calibration parameters, and safety operating procedures. Units of measurement include mm, kVp, mA, ms, and Hz.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The complex structure and specialized terminology of imaging equipment documents challenge intent recognition and parameter extraction in tool calling. The presence of charts and flowcharts in documents means that pure text analysis may not capture all information, requiring tools with image understanding capabilities or reliance on manual annotation. The low update frequency but large content volume per update demands that the knowledge base synchronization mechanism can handle incremental or full replacement of large-scale documents. Key information like equipment models and serial numbers often serves as necessary parameters for tool calls; their accurate extraction directly impacts tool execution effectiveness. Additionally, document formats vary significantly across different equipment manufacturers, requiring plugins with strong parsing compatibility to handle diverse data sources.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 3000 characters | Ensures coverage of longer operational step descriptions in imaging equipment SOPs. |
chunkSize | 800 characters | Balances text block size to retain context while avoiding excessive length that could lead to inaccurate recall. |
top_k | 5 items | Considering the strictness of imaging equipment operations, increasing the number of recalled items improves relevance coverage. |
similarityThreshold | 0.75 | Given the prevalence of specialized terminology, appropriately raising the similarity threshold reduces false recalls. |
tool_call_timeout_seconds | 60 seconds | Addresses potential response delays from external tools (e.g., equipment status query interfaces). |
model_name | gpt-4o or glm-4 | Selects models with strong logical reasoning and multimodal capabilities to handle complex instructions. |
Common Pitfalls
- The tool calling module does not trigger; the model directly answers or refuses to answer. This may occur if the prompt does not explicitly guide the model to use tools, or if the tool's
descriptionis unclear and not correctly understood by the model. - The result returned after tool calling is empty or incomplete, for example, missing the device serial number. This typically happens when document parsing fails to accurately extract all necessary parameters, leading to missing parameters being passed to the external tool.
- Both knowledge base search and tool calling are enabled in the workflow, but the model only uses the knowledge base. This may be because the tool calling priority is set too low, or the knowledge base recall content already satisfies part of the query, preventing the model from triggering further tools.
Verification Steps
- For typical imaging equipment fault diagnosis or operational step queries, input test questions. Observe whether the expected tool call successfully triggers in the logs and verify that the passed parameters are correct.
- Simulate a scenario requiring real-time equipment status queries. Verify that the tool call successfully interacts with the external interface and returns valid data.
- Test imaging equipment SOP questions of varying complexity. Check whether the model's understanding of the context is consistent before and after tool calling, and whether the final answer accurately references information returned by the tool.
The values provided 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.