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Timothy Kassis
174
scientific-skills/research-lookup/scripts/examples.py
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174
scientific-skills/research-lookup/scripts/examples.py
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#!/usr/bin/env python3
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"""
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Example usage of the Research Lookup skill with automatic model selection.
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This script demonstrates:
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1. Automatic model selection based on query complexity
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2. Manual model override options
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3. Batch query processing
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4. Integration with scientific writing workflows
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"""
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import os
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from research_lookup import ResearchLookup
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def example_automatic_selection():
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"""Demonstrate automatic model selection."""
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print("=" * 80)
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print("EXAMPLE 1: Automatic Model Selection")
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print("=" * 80)
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print()
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research = ResearchLookup()
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# Simple lookup - will use Sonar Pro Search
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query1 = "Recent advances in CRISPR gene editing 2024"
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print(f"Query: {query1}")
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print(f"Expected model: Sonar Pro Search (fast lookup)")
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result1 = research.lookup(query1)
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print(f"Actual model: {result1.get('model')}")
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print()
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# Complex analysis - will use Sonar Reasoning Pro
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query2 = "Compare and contrast the efficacy of mRNA vaccines versus traditional vaccines"
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print(f"Query: {query2}")
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print(f"Expected model: Sonar Reasoning Pro (analytical)")
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result2 = research.lookup(query2)
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print(f"Actual model: {result2.get('model')}")
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print()
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def example_manual_override():
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"""Demonstrate manual model override."""
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print("=" * 80)
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print("EXAMPLE 2: Manual Model Override")
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print("=" * 80)
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print()
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# Force Sonar Pro Search for budget-constrained rapid lookup
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research_pro = ResearchLookup(force_model='pro')
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query = "Explain the mechanism of CRISPR-Cas9"
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print(f"Query: {query}")
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print(f"Forced model: Sonar Pro Search")
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result = research_pro.lookup(query)
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print(f"Model used: {result.get('model')}")
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print()
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# Force Sonar Reasoning Pro for critical analysis
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research_reasoning = ResearchLookup(force_model='reasoning')
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print(f"Query: {query}")
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print(f"Forced model: Sonar Reasoning Pro")
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result = research_reasoning.lookup(query)
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print(f"Model used: {result.get('model')}")
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print()
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def example_batch_queries():
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"""Demonstrate batch query processing."""
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print("=" * 80)
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print("EXAMPLE 3: Batch Query Processing")
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print("=" * 80)
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print()
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research = ResearchLookup()
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# Mix of simple and complex queries
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queries = [
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"Recent clinical trials for Alzheimer's disease", # Sonar Pro Search
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"Compare deep learning vs traditional ML in drug discovery", # Sonar Reasoning Pro
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"Statistical power analysis methods", # Sonar Pro Search
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]
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print("Processing batch queries...")
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print("Each query will automatically select the appropriate model")
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print()
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results = research.batch_lookup(queries, delay=1.0)
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for i, result in enumerate(results):
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print(f"Query {i+1}: {result['query'][:50]}...")
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print(f" Model: {result.get('model')}")
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print(f" Type: {result.get('model_type')}")
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print()
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def example_scientific_writing_workflow():
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"""Demonstrate integration with scientific writing workflow."""
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print("=" * 80)
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print("EXAMPLE 4: Scientific Writing Workflow")
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print("=" * 80)
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print()
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research = ResearchLookup()
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# Literature review phase - use Pro for breadth
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print("PHASE 1: Literature Review (Breadth)")
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lit_queries = [
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"Recent papers on machine learning in genomics 2024",
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"Clinical applications of AI in radiology",
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"RNA sequencing analysis methods"
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]
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for query in lit_queries:
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print(f" - {query}")
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# These will automatically use Sonar Pro Search
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print()
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# Discussion phase - use Reasoning Pro for synthesis
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print("PHASE 2: Discussion (Synthesis & Analysis)")
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discussion_queries = [
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"Compare the advantages and limitations of different ML approaches in genomics",
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"Explain the relationship between model interpretability and clinical adoption",
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"Analyze the ethical implications of AI in medical diagnosis"
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]
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for query in discussion_queries:
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print(f" - {query}")
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# These will automatically use Sonar Reasoning Pro
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print()
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def main():
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"""Run all examples (requires OPENROUTER_API_KEY to be set)."""
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if not os.getenv("OPENROUTER_API_KEY"):
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print("Note: Set OPENROUTER_API_KEY environment variable to run live queries")
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print("These examples show the structure without making actual API calls")
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print()
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# Uncomment to run examples (requires API key)
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# example_automatic_selection()
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# example_manual_override()
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# example_batch_queries()
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# example_scientific_writing_workflow()
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# Show complexity assessment without API calls
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print("=" * 80)
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print("COMPLEXITY ASSESSMENT EXAMPLES (No API calls required)")
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print("=" * 80)
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print()
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os.environ.setdefault("OPENROUTER_API_KEY", "test")
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research = ResearchLookup()
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test_queries = [
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("Recent CRISPR studies", "pro"),
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("Compare CRISPR vs TALENs", "reasoning"),
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("Explain how CRISPR works", "reasoning"),
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("Western blot protocol", "pro"),
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("Pros and cons of different sequencing methods", "reasoning"),
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]
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for query, expected in test_queries:
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complexity = research._assess_query_complexity(query)
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model_name = "Sonar Reasoning Pro" if complexity == "reasoning" else "Sonar Pro Search"
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status = "✓" if complexity == expected else "✗"
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print(f"{status} '{query}'")
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print(f" → {model_name}")
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print()
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if __name__ == "__main__":
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main()
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93
scientific-skills/research-lookup/scripts/lookup.py
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93
scientific-skills/research-lookup/scripts/lookup.py
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#!/usr/bin/env python3
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"""
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Research Lookup Tool for Claude Code
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Performs research queries using Perplexity Sonar Pro Search via OpenRouter.
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"""
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import os
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import sys
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import json
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from typing import Dict, List, Optional
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# Import the main research lookup class
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sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'scripts'))
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from research_lookup import ResearchLookup
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def format_response(result: Dict) -> str:
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"""Format the research result for display."""
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if not result["success"]:
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return f"❌ Research lookup failed: {result['error']}"
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response = result["response"]
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citations = result["citations"]
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# Format the output for Claude Code
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output = f"""🔍 **Research Results**
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**Query:** {result['query']}
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**Model:** {result['model']}
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**Timestamp:** {result['timestamp']}
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---
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{response}
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"""
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if citations:
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output += f"\n**Extracted Citations ({len(citations)}):**\n"
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for i, citation in enumerate(citations, 1):
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if citation.get("doi"):
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output += f"{i}. DOI: {citation['doi']}\n"
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elif citation.get("authors") and citation.get("year"):
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output += f"{i}. {citation['authors']} ({citation['year']})\n"
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else:
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output += f"{i}. {citation}\n"
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if result.get("usage"):
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usage = result["usage"]
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output += f"\n**Usage:** {usage.get('total_tokens', 'N/A')} tokens"
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return output
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def main():
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"""Main entry point for Claude Code tool."""
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# Check for API key
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if not os.getenv("OPENROUTER_API_KEY"):
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print("❌ Error: OPENROUTER_API_KEY environment variable not set")
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print("Please set it in your .env file or export it:")
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print(" export OPENROUTER_API_KEY='your_openrouter_api_key'")
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return 1
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# Get query from command line arguments
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if len(sys.argv) < 2:
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print("❌ Error: No query provided")
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print("Usage: python lookup.py 'your research query here'")
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return 1
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query = " ".join(sys.argv[1:])
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try:
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# Initialize research tool
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research = ResearchLookup()
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# Perform lookup
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print(f"🔍 Researching: {query}")
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result = research.lookup(query)
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# Format and output result
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formatted_output = format_response(result)
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print(formatted_output)
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# Return success code
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return 0 if result["success"] else 1
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except Exception as e:
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print(f"❌ Error: {str(e)}")
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return 1
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if __name__ == "__main__":
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exit(main())
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