# Context Management: Your Data, Your Control

Open Notebook's context management system is a revolutionary feature that gives you **granular control** over what information gets shared with AI models. Unlike traditional research tools that send all your data to AI providers, Open Notebook empowers you to make precise decisions about context sharing, balancing functionality with privacy and cost control.

## Table of Contents

1. [Understanding Context Levels](#understanding-context-levels)
2. [Context Configuration Strategies](#context-configuration-strategies)
3. [Privacy and Data Control](#privacy-and-data-control)
4. [Performance Optimization](#performance-optimization)
5. [AI Model Integration and Cost Management](#ai-model-integration-and-cost-management)
6. [Advanced Context Features](#advanced-context-features)
7. [Best Practices](#best-practices)

## Understanding Context Levels

Open Notebook provides three distinct context levels, each designed for different use cases and privacy requirements:

### 🚫 Not in Context
**"⛔ not in context"**

- **What it does**: Completely excludes the source or note from AI interactions
- **Data sharing**: Zero information sent to AI providers
- **Use cases**: 
  - Highly sensitive or confidential documents
  - Personal notes you don't want AI to access
  - Reference materials that don't need AI analysis
  - Large files that would consume excessive tokens

**Example scenario**: You've uploaded a confidential contract for reference but don't want any AI model to process its contents.

### 🟡 Summary Only (Sources)
**"🟡 insights" - Available for sources only**

- **What it does**: Shares only AI-generated insights and summaries, never the full document text
- **Data sharing**: Processed summaries, key points, and transformations
- **Use cases**:
  - Balancing functionality with privacy
  - Reducing token consumption while maintaining usefulness
  - Large documents where full text isn't necessary
  - Cost-effective AI interactions

**Example scenario**: You have a 50-page research paper where you only need the AI to understand the key findings and conclusions, not every detail.

### 🟢 Full Content
**"🟢 full content"**

- **What it does**: Provides complete access to the source text or note content
- **Data sharing**: Entire document or note content sent to AI models
- **Use cases**:
  - Documents requiring detailed analysis
  - Short documents where full context is needed
  - Sources requiring precise citation and quotation
  - Interactive research where AI needs complete information

**Example scenario**: You're analyzing a specific methodology section and need the AI to reference exact procedures and technical details.

## Context Configuration Strategies

### Research-Focused Strategy

**Best for**: Academic research, detailed analysis, comprehensive understanding

```
Sources:
- Primary research papers: 🟢 Full Content
- Background materials: 🟡 Summary Only
- Reference documents: 🚫 Not in Context
- Personal notes: 🟢 Full Content
```

**Benefits**:
- Deep AI understanding of key materials
- Cost-effective use of background information
- Protection of sensitive reference materials
- Complete access to personal insights

### Privacy-First Strategy

**Best for**: Sensitive research, confidential documents, personal projects

```
Sources:
- Sensitive documents: 🚫 Not in Context
- Public materials: 🟡 Summary Only
- Specific analysis targets: 🟢 Full Content (selectively)
- Personal notes: 🚫 Not in Context
```

**Benefits**:
- Maximum privacy protection
- Selective AI engagement
- Reduced data exposure
- Control over sensitive information

### Cost-Optimization Strategy

**Best for**: Budget-conscious users, large document collections, token management

```
Sources:
- Large documents: 🟡 Summary Only
- Critical materials: 🟢 Full Content (limited)
- Reference materials: 🚫 Not in Context
- Generated insights: 🟢 Full Content
```

**Benefits**:
- Minimized token consumption
- Focused AI spending
- Efficient resource utilization
- Strategic information sharing

### Collaborative Strategy

**Best for**: Team research, shared projects, knowledge management

```
Sources:
- Shared documents: 🟡 Summary Only
- Team notes: 🟢 Full Content
- External references: 🚫 Not in Context
- Project materials: 🟢 Full Content
```

**Benefits**:
- Balanced privacy and collaboration
- Standardized information sharing
- Controlled team access
- Efficient knowledge transfer

## Privacy and Data Control

### Data Sovereignty

Open Notebook's context management ensures **complete data sovereignty**:

- **Local Processing**: All context filtering happens on your infrastructure
- **Selective Sharing**: Only specifically authorized content reaches AI providers
- **Audit Trail**: Full transparency about what information is shared
- **Reversible Decisions**: Context levels can be changed at any time

### Privacy Compliance

The system supports various privacy frameworks:

**GDPR Compliance**:
- Data minimization through context level selection
- User consent for each information sharing decision
- Right to be forgotten through context exclusion
- Transparent data processing practices

**HIPAA Considerations**:
- Medical documents can be excluded from AI processing
- Summary-only access for research purposes
- Full control over patient information sharing
- Audit trails for compliance reporting

**Corporate Security**:
- Proprietary information protection
- Selective competitive intelligence sharing
- Confidential document isolation
- Controlled IP exposure

### Dynamic Privacy Controls

Context levels can be adjusted in real-time:

1. **Per-Conversation**: Change context for specific AI interactions
2. **Per-Source**: Individual control over each document or note
3. **Per-Project**: Notebook-level privacy settings
4. **Per-Provider**: Different context levels for different AI models

## Performance Optimization

### Token Management

Context levels directly impact token consumption:

**Token Usage by Context Level**:
- **Not in Context**: 0 tokens consumed
- **Summary Only**: 10-20% of full document tokens
- **Full Content**: 100% of document tokens

**Optimization Strategies**:
- Use summary context for background materials
- Reserve full content for critical analysis
- Exclude large reference documents
- Monitor token usage through built-in counters

### Processing Speed

Context management affects response times:

**Performance Characteristics**:
- **Summary Context**: Faster processing, smaller payloads
- **Full Content**: Slower processing, larger payloads
- **Mixed Strategy**: Balanced performance and functionality

**Speed Optimization Tips**:
- Start with summary context for exploration
- Switch to full content for detailed analysis
- Use context exclusion for irrelevant materials
- Cache frequently accessed summaries

### Memory Management

Context levels help manage system resources:

**Memory Usage**:
- **Context Exclusion**: Reduces memory footprint
- **Summary Processing**: Efficient memory utilization
- **Full Content**: Higher memory requirements

**Resource Optimization**:
- Use selective context for large document collections
- Implement context rotation for different research phases
- Monitor system performance metrics
- Archive unused context materials

## AI Model Integration and Cost Management

### Provider-Specific Considerations

Different AI providers have varying cost structures:

**OpenAI GPT Models**:
- Input tokens: $0.01-$0.06 per 1K tokens
- Output tokens: $0.03-$0.12 per 1K tokens
- **Strategy**: Use summary context for exploration, full content for analysis

**Anthropic Claude**:
- Input tokens: $0.003-$0.015 per 1K tokens
- Output tokens: $0.015-$0.075 per 1K tokens
- **Strategy**: Leverage higher context windows with selective full content

**Google Gemini**:
- Input tokens: $0.001-$0.0075 per 1K tokens
- Output tokens: $0.002-$0.03 per 1K tokens
- **Strategy**: Cost-effective for larger context, good for mixed strategies

**Local Models (Ollama)**:
- No per-token costs
- **Strategy**: Use full content freely, optimize for quality

### Cost Calculation Tools

Open Notebook provides built-in cost estimation:

```python
# Example cost calculation
total_tokens = context_response.total_tokens
estimated_cost = calculate_cost(total_tokens, model_provider, model_name)
```

**Cost Monitoring Features**:
- Real-time token counting
- Per-conversation cost tracking
- Model comparison tools
- Budget alerts and limits

### Multi-Model Strategies

Leverage different models for different context levels:

**Tiered Approach**:
- **Summary Generation**: Use cost-effective models (Gemini, local)
- **Analysis**: Use high-quality models (Claude, GPT-4)
- **Citations**: Use precise models (GPT-4, Claude)
- **Exploration**: Use free local models (Ollama)

## Advanced Context Features

### Contextual Transformations

Apply different transformations based on context level:

**Summary-Level Transformations**:
- Automated summaries
- Key point extraction
- Topic identification
- Sentiment analysis

**Full-Content Transformations**:
- Detailed analysis
- Citation generation
- Methodology extraction
- Critical evaluation

### Dynamic Context Adjustment

Context levels can be modified during conversations:

1. **Progressive Disclosure**: Start with summaries, expand to full content
2. **Focus Shifting**: Change context based on conversation direction
3. **Privacy Escalation**: Reduce context when discussing sensitive topics
4. **Performance Tuning**: Adjust context based on response quality

### Context Inheritance

New sources can inherit context settings:

**Inheritance Patterns**:
- **Notebook Defaults**: New sources adopt notebook-level settings
- **Source Type**: Different defaults for PDFs, web links, notes
- **User Preferences**: Personal default context strategies
- **Project Templates**: Standardized context configurations

### Context Metadata

Each context decision includes metadata:

**Tracking Information**:
- Context level selection timestamp
- Reasoning for context decision
- Token consumption estimates
- Privacy impact assessment

## Best Practices

### Getting Started

**Initial Configuration**:
1. **Start Conservative**: Begin with summary-only context
2. **Test Gradually**: Experiment with full content on small documents
3. **Monitor Costs**: Track token usage and adjust accordingly
4. **Establish Patterns**: Develop consistent context strategies

### Ongoing Management

**Regular Review**:
- **Weekly**: Review context decisions for active projects
- **Monthly**: Analyze token usage and cost effectiveness
- **Quarterly**: Evaluate privacy and security practices
- **Annually**: Update context strategies based on workflow changes

### Workflow Integration

**Research Phases**:
1. **Discovery**: Use summary context for broad exploration
2. **Analysis**: Switch to full content for detailed examination
3. **Synthesis**: Mix context levels based on importance
4. **Communication**: Use full content for accurate citations

### Team Collaboration

**Shared Standards**:
- **Naming Conventions**: Clear context level indicators
- **Documentation**: Explain context decisions to team members
- **Templates**: Standardized context configurations
- **Training**: Educate team on context management benefits

### Security Considerations

**Regular Audits**:
- Review context sharing decisions
- Verify privacy compliance
- Monitor unauthorized access
- Update security policies

**Incident Response**:
- Procedures for context exposure
- Rollback strategies for privacy breaches
- Communication protocols for data incidents
- Recovery procedures for compromised context

### Performance Monitoring

**Key Metrics**:
- **Token Usage**: Track consumption by context level
- **Response Quality**: Measure AI performance by context type
- **Cost Efficiency**: Calculate cost per insight generated
- **User Satisfaction**: Monitor workflow effectiveness

**Optimization Cycles**:
1. **Measure**: Collect performance data
2. **Analyze**: Identify optimization opportunities
3. **Adjust**: Modify context strategies
4. **Validate**: Confirm improvement results

### Troubleshooting Common Issues

**Poor AI Responses**:
- **Problem**: AI lacks necessary context
- **Solution**: Increase context level for key sources
- **Prevention**: Review context decisions before important queries

**High Token Costs**:
- **Problem**: Excessive full content usage
- **Solution**: Switch to summary context for background materials
- **Prevention**: Implement cost monitoring and alerts

**Privacy Concerns**:
- **Problem**: Too much information shared with AI
- **Solution**: Reduce context levels for sensitive materials
- **Prevention**: Regular privacy audits and policy updates

**Performance Issues**:
- **Problem**: Slow AI responses
- **Solution**: Optimize context selection and document sizes
- **Prevention**: Monitor system resources and adjust context accordingly

## Conclusion

Open Notebook's context management system represents a paradigm shift in AI-powered research tools. By providing granular control over information sharing, it empowers users to:

- **Maintain Privacy**: Share only what's necessary with AI providers
- **Control Costs**: Optimize token usage and AI spending
- **Enhance Security**: Protect sensitive information from exposure
- **Improve Performance**: Balance functionality with system resources
- **Enable Compliance**: Meet organizational and regulatory requirements

The key to success with context management is understanding that it's not just a feature—it's a fundamental approach to responsible AI integration. By thoughtfully configuring context levels, monitoring their impact, and continuously optimizing your strategy, you can achieve the perfect balance between AI-powered insights and data protection.

**Remember**: With great power comes great responsibility. Use Open Notebook's context management system to build research workflows that are not only powerful and efficient but also secure and compliant with your privacy requirements.

---

*For more information about Open Notebook's features, visit our [documentation](../user-guide/index.md) or join our [community](https://discord.gg/37XJPXfz2w).*