mirror of
https://github.com/mims-harvard/ToolUniverse.git
synced 2026-09-19 07:31:47 +08:00
Shanghua Gao
9f7070ed7b
### **Visualization Suite** - 2D/3D molecular visualization with interactive HTML output - Protein structure rendering with multiple visualization styles - Scientific plotting with Plotly, Matplotlib, and NetworkX ### **Coding API** - Type-safe function calling: `from tooluniverse.tools import ToolName` - Dynamic tool access: `tu.tools.ToolName()` - Built-in caching and error handling ### **Health Check System** - New `tooluniverse-doctor` CLI tool - Automatic dependency validation and bulk fix commands ### **Expanded Tool Ecosystem** - 50+ new scientific tools - Clinical guidelines (NICE, WHO, Europe PMC)
86 lines
3.6 KiB
Python
86 lines
3.6 KiB
Python
#!/usr/bin/env python3
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"""
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ToolUniverse Function Calling Examples
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Demonstrates various methods for calling tools in ToolUniverse
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"""
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from tooluniverse import ToolUniverse
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# Initialize ToolUniverse
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tu = ToolUniverse()
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tu.load_tools()
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# =============================================================================
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# Method 1: Direct Function-Style Calling
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# =============================================================================
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# Syntax: tu.tools.ToolName(parameter=value)
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# Description: Most intuitive way to call tools, like calling regular Python functions
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result1 = tu.tools.UniProt_get_entry_by_accession(accession="P05067")
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# =============================================================================
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# Method 2: Function-Style with Caching
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# =============================================================================
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# Syntax: tu.tools.ToolName(parameter=value, use_cache=True)
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# Description: Enables caching for repeated calls with same parameters
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result2 = tu.tools.UniProt_get_entry_by_accession(
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accession="P05067",
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use_cache=True
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)
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# =============================================================================
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# Method 3: Traditional JSON-Based Calling
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# =============================================================================
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# Syntax: tu.run_one_function({'name': 'ToolName', 'arguments': {...}})
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# Description: Original method using JSON structure for tool name and arguments
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result3 = tu.run_one_function({
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"name": "UniProt_get_entry_by_accession",
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"arguments": {"accession": "P05067"}
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})
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# =============================================================================
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# Method 4: Multiple Individual Calls
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# =============================================================================
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# Description: Execute multiple tools individually (alternative to batch calling)
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# Syntax: Multiple tu.run_one_function() calls
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result4a = tu.run_one_function({
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"name": "UniProt_get_entry_by_accession",
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"arguments": {"accession": "P05067"}
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})
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result4b = tu.run_one_function({
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"name": "UniProt_get_entry_by_accession",
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"arguments": {"accession": "P12345"}
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})
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# =============================================================================
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# Method 5: Tool Discovery and Dynamic Calling
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# =============================================================================
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# Syntax: getattr(tu.tools, 'ToolName')(parameters)
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# Description: Dynamically select and call tools based on runtime conditions
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tool_name = "UniProt_get_entry_by_accession"
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if hasattr(tu.tools, tool_name):
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tool_func = getattr(tu.tools, tool_name)
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result5 = tool_func(accession="P05067")
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# =============================================================================
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# Method 10: Async-Style Calling (if supported)
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# =============================================================================
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# Syntax: await tu.tools.ToolName(parameters) - if async support exists
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# Description: Asynchronous calling for non-blocking operations
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# Note: Check if your ToolUniverse version supports async calling
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# async def example_async_call():
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# result = await tu.tools.UniProt_get_entry_by_accession(accession="P05067")
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# =============================================================================
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# Summary of Methods
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# =============================================================================
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# All methods return the same type of result objects.
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# Choose the method that best fits your use case:
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# - Direct calling: Most intuitive for single tool calls
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# - JSON-based: Useful for dynamic tool selection
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# - Batch calling: Efficient for multiple tool calls
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# - Dynamic calling: Good for programmatic tool selection
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# - Error handling: Essential for robust applications
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