examples

Examples

This directory contains practical examples demonstrating Context usage patterns.

Examples Overview

1. Backend Automation

Path: backend-automation/

Demonstrates using Context in backend automation scenarios like GitHub Actions or scheduled jobs.

Features:

• Creating contexts for data analysis
• Adding inputs with relevance scores
• Automatic pruning to fit token limits
• Strategy-based routing
• Context operations (extend, merge, serialize)

Run:

cd backend-automation
PYTHONPATH=../../core/python python backend_example.py

Use Cases:

• Scheduled catalog analysis
• Automated data processing
• Batch LLM operations
• Report generation

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2. Browser Analysis

Path: browser-analysis/

Demonstrates using Context in browser environments with user-provided API keys.

Features:

• Browser-based Context creation
• Interactive input management
• Strategy selection
• Context serialization
• Stub execution (shows structure without actual API calls)

Run:

cd browser-analysis
# Open index.html in a browser
python -m http.server 8000
# Then visit http://localhost:8000

Use Cases:

• User-driven data exploration
• Client-side analysis with user keys
• Interactive data visualization
• Privacy-preserving analysis

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3. Hybrid Workflow

Path: hybrid-workflow/

Demonstrates cross-runtime workflow: backend creates context, frontend extends and customizes.

Features:

• Backend preprocessing and initial analysis
• Context serialization for frontend
• Frontend context loading and extension
• User customization with their API keys
• Context sharing via URL or download

Run Backend:

cd hybrid-workflow
PYTHONPATH=../../core/python python backend.py

Run Frontend:

# Open frontend.html in a browser
python -m http.server 8000
# Then visit http://localhost:8000/frontend.html

Use Cases:

• Collaborative analysis workflows
• Backend preprocessing + frontend customization
• Shareable analysis contexts
• Reproducible research

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Common Patterns

Creating a Context

from context import Context

ctx = Context(
    intent="analyze",
    constraints={"max_tokens": 4000},
    routing={"strategy": "cost_optimized"}
)

Adding Inputs

# Add with relevance score
ctx.add_input(data, relevance=0.9)

# Multiple inputs
for item in dataset:
    ctx.add_input(item, relevance=item.priority)

Pruning

# Prune to fit token limit
ctx.prune(max_tokens=2000, relevance_threshold=0.5)

Routing

# Strategy-based
ctx.route(strategy="cost_optimized")

# Explicit
ctx.route(model="gpt-4", provider="openai")

Execution

result = ctx.execute(
    task="Analyze this data",
    api_key=os.environ["OPENAI_API_KEY"]
)

Serialization

# Save
json_str = ctx.to_json()
with open("context.json", "w") as f:
    f.write(json_str)

# Load
with open("context.json") as f:
    ctx = Context.from_json(f.read())

Cross-Runtime

Backend (Python):

ctx = Context(intent="analyze")
ctx.add_input(preprocessed_data)
# Save for frontend
with open("context.json", "w") as f:
    f.write(ctx.to_json())

Frontend (TypeScript):

// Load context created by backend
const response = await fetch('context.json');
const data = await response.json();
const ctx = Context.fromJSON(data);

// Extend with user data
ctx.addInput(userInput);
const result = await ctx.execute({
    task: "Custom analysis",
    apiKey: userApiKey
});

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Running Examples

All examples use stub execution by default (no real API calls). To enable real execution:

1. Set up API keys:

   export OPENAI_API_KEY="your-key-here"
   # or
   export ANTHROPIC_API_KEY="your-key-here"

2. Implement provider adapters in adapters/ directory

3. Update executor to use real providers instead of stubs

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Example Data

Examples use synthetic data for demonstration. In real usage, replace with your actual data sources:

• Catalog metadata
• User inputs
• API responses
• Database queries
• File contents

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Next Steps

After reviewing examples:

1. Try modifying the examples for your use case
2. Review the Getting Started Guide
3. Check the API Reference
4. Read the Migration Guide for adopting Context in existing projects