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skills/research-lookup/examples.py

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+#!/usr/bin/env python3
+"""
+Example usage of the Research Lookup skill with automatic model selection.
+
+This script demonstrates:
+1. Automatic model selection based on query complexity
+2. Manual model override options
+3. Batch query processing
+4. Integration with scientific writing workflows
+"""
+
+import os
+from research_lookup import ResearchLookup
+
+
+def example_automatic_selection():
+    """Demonstrate automatic model selection."""
+    print("=" * 80)
+    print("EXAMPLE 1: Automatic Model Selection")
+    print("=" * 80)
+    print()
+    
+    research = ResearchLookup()
+    
+    # Simple lookup - will use Sonar Pro
+    query1 = "Recent advances in CRISPR gene editing 2024"
+    print(f"Query: {query1}")
+    print(f"Expected model: Sonar Pro (fast lookup)")
+    result1 = research.lookup(query1)
+    print(f"Actual model: {result1.get('model')}")
+    print()
+    
+    # Complex analysis - will use Sonar Reasoning Pro
+    query2 = "Compare and contrast the efficacy of mRNA vaccines versus traditional vaccines"
+    print(f"Query: {query2}")
+    print(f"Expected model: Sonar Reasoning Pro (analytical)")
+    result2 = research.lookup(query2)
+    print(f"Actual model: {result2.get('model')}")
+    print()
+
+
+def example_manual_override():
+    """Demonstrate manual model override."""
+    print("=" * 80)
+    print("EXAMPLE 2: Manual Model Override")
+    print("=" * 80)
+    print()
+    
+    # Force Sonar Pro for budget-constrained rapid lookup
+    research_pro = ResearchLookup(force_model='pro')
+    query = "Explain the mechanism of CRISPR-Cas9"
+    print(f"Query: {query}")
+    print(f"Forced model: Sonar Pro")
+    result = research_pro.lookup(query)
+    print(f"Model used: {result.get('model')}")
+    print()
+    
+    # Force Sonar Reasoning Pro for critical analysis
+    research_reasoning = ResearchLookup(force_model='reasoning')
+    print(f"Query: {query}")
+    print(f"Forced model: Sonar Reasoning Pro")
+    result = research_reasoning.lookup(query)
+    print(f"Model used: {result.get('model')}")
+    print()
+
+
+def example_batch_queries():
+    """Demonstrate batch query processing."""
+    print("=" * 80)
+    print("EXAMPLE 3: Batch Query Processing")
+    print("=" * 80)
+    print()
+    
+    research = ResearchLookup()
+    
+    # Mix of simple and complex queries
+    queries = [
+        "Recent clinical trials for Alzheimer's disease",  # Sonar Pro
+        "Compare deep learning vs traditional ML in drug discovery",  # Sonar Reasoning Pro
+        "Statistical power analysis methods",  # Sonar Pro
+    ]
+    
+    print("Processing batch queries...")
+    print("Each query will automatically select the appropriate model")
+    print()
+    
+    results = research.batch_lookup(queries, delay=1.0)
+    
+    for i, result in enumerate(results):
+        print(f"Query {i+1}: {result['query'][:50]}...")
+        print(f"  Model: {result.get('model')}")
+        print(f"  Type: {result.get('model_type')}")
+        print()
+
+
+def example_scientific_writing_workflow():
+    """Demonstrate integration with scientific writing workflow."""
+    print("=" * 80)
+    print("EXAMPLE 4: Scientific Writing Workflow")
+    print("=" * 80)
+    print()
+    
+    research = ResearchLookup()
+    
+    # Literature review phase - use Pro for breadth
+    print("PHASE 1: Literature Review (Breadth)")
+    lit_queries = [
+        "Recent papers on machine learning in genomics 2024",
+        "Clinical applications of AI in radiology",
+        "RNA sequencing analysis methods"
+    ]
+    
+    for query in lit_queries:
+        print(f"  - {query}")
+        # These will automatically use Sonar Pro
+    print()
+    
+    # Discussion phase - use Reasoning Pro for synthesis
+    print("PHASE 2: Discussion (Synthesis & Analysis)")
+    discussion_queries = [
+        "Compare the advantages and limitations of different ML approaches in genomics",
+        "Explain the relationship between model interpretability and clinical adoption",
+        "Analyze the ethical implications of AI in medical diagnosis"
+    ]
+    
+    for query in discussion_queries:
+        print(f"  - {query}")
+        # These will automatically use Sonar Reasoning Pro
+    print()
+
+
+def main():
+    """Run all examples (requires OPENROUTER_API_KEY to be set)."""
+    
+    if not os.getenv("OPENROUTER_API_KEY"):
+        print("Note: Set OPENROUTER_API_KEY environment variable to run live queries")
+        print("These examples show the structure without making actual API calls")
+        print()
+    
+    # Uncomment to run examples (requires API key)
+    # example_automatic_selection()
+    # example_manual_override()
+    # example_batch_queries()
+    # example_scientific_writing_workflow()
+    
+    # Show complexity assessment without API calls
+    print("=" * 80)
+    print("COMPLEXITY ASSESSMENT EXAMPLES (No API calls required)")
+    print("=" * 80)
+    print()
+    
+    os.environ.setdefault("OPENROUTER_API_KEY", "test")
+    research = ResearchLookup()
+    
+    test_queries = [
+        ("Recent CRISPR studies", "pro"),
+        ("Compare CRISPR vs TALENs", "reasoning"),
+        ("Explain how CRISPR works", "reasoning"),
+        ("Western blot protocol", "pro"),
+        ("Pros and cons of different sequencing methods", "reasoning"),
+    ]
+    
+    for query, expected in test_queries:
+        complexity = research._assess_query_complexity(query)
+        model_name = "Sonar Reasoning Pro" if complexity == "reasoning" else "Sonar Pro"
+        status = "✓" if complexity == expected else "✗"
+        print(f"{status} '{query}'")
+        print(f"  → {model_name}")
+        print()
+
+
+if __name__ == "__main__":
+    main()
+