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Examples

This page provides examples of using The DETERMINATOR for various research tasks.

Basic Research Query

Example 1: Drug Information

Query: 

What are the latest treatments for Alzheimer's disease?

What The DETERMINATOR Does: 1. Searches PubMed for recent papers 2. Searches ClinicalTrials.gov for active trials 3. Evaluates evidence quality 4. Synthesizes findings into a comprehensive report

Query: 

What clinical trials are investigating metformin for cancer prevention?

What The DETERMINATOR Does:

  1. Searches ClinicalTrials.gov for relevant trials
  2. Searches PubMed for supporting literature
  3. Provides trial details and status
  4. Summarizes findings

Advanced Research Queries

Example 3: Comprehensive Review

Query:

Review the evidence for using metformin as an anti-aging intervention, 
including clinical trials, mechanisms of action, and safety profile.

What The DETERMINATOR Does: 1. Uses deep research mode (multi-section) 2. Searches multiple sources in parallel 3. Generates sections on: - Clinical trials - Mechanisms of action - Safety profile 4. Synthesizes comprehensive report

Example 4: Hypothesis Testing

Query: 

Test the hypothesis that regular exercise reduces Alzheimer's disease risk.

What The DETERMINATOR Does: 1. Generates testable hypotheses 2. Searches for supporting/contradicting evidence 3. Performs statistical analysis (if Modal configured) 4. Provides verdict: SUPPORTED, REFUTED, or INCONCLUSIVE

MCP Tool Examples

Using search_pubmed

Search PubMed for "CRISPR gene editing cancer therapy"

Using search_clinical_trials

Find active clinical trials for "diabetes type 2 treatment"

Using search_all

Search all sources for "COVID-19 vaccine side effects"

Using analyze_hypothesis

Analyze whether vitamin D supplementation reduces COVID-19 severity

Code Examples

Python API Usage

from src.orchestrator_factory import create_orchestrator
from src.tools.search_handler import SearchHandler
from src.agent_factory.judges import create_judge_handler

# Create orchestrator
search_handler = SearchHandler()
judge_handler = create_judge_handler()

# Run research query
query = "What are the latest treatments for Alzheimer's disease?"
async for event in orchestrator.run(query):
    print(f"Event: {event.type} - {event.data}")

Gradio UI Integration

import gradio as gr
from src.app import create_research_interface

# Create interface
interface = create_research_interface()

# Launch
interface.launch(server_name="0.0.0.0", server_port=7860)

Research Patterns

Iterative Research

Single-loop research with search-judge-synthesize cycles:

from src.orchestrator.research_flow import IterativeResearchFlow

async for event in flow.run(query):
    # Handle events
    pass

Deep Research

Multi-section parallel research:

from src.orchestrator.research_flow import DeepResearchFlow

async for event in flow.run(query):
    # Handle events
    pass

Configuration Examples

Basic Configuration

# .env file
LLM_PROVIDER=openai
OPENAI_API_KEY=your_key_here
MAX_ITERATIONS=10

Advanced Configuration

# .env file
LLM_PROVIDER=anthropic
ANTHROPIC_API_KEY=your_key_here
EMBEDDING_PROVIDER=local
WEB_SEARCH_PROVIDER=duckduckgo
MAX_ITERATIONS=20
DEFAULT_TOKEN_LIMIT=200000
USE_GRAPH_EXECUTION=true

Next Steps