Clean up PR documentation files after Gitea workflow example
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PR_BODY.md
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PR_BODY.md
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## Problem Statement
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Currently, FSS-Mini-RAG uses Ollama's default context window settings, which severely limits performance:
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- **Default 2048 tokens** is inadequate for RAG applications
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- Users can't configure context window for their hardware/use case
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- No guidance on optimal context sizes for different models
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- Inconsistent context handling across the codebase
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- New users don't understand context window importance
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## Impact on User Experience
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**With 2048 token context window:**
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- Only 1-2 responses possible before context truncation
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- Thinking tokens consume significant context space
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- Poor performance with larger document chunks
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- Frustrated users who don't understand why responses degrade
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**With proper context configuration:**
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- 5-15+ responses in exploration mode
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- Support for advanced use cases (15+ results, 4000+ character chunks)
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- Better coding assistance and analysis
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- Professional-grade RAG experience
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## Solution Implemented
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### 1. Enhanced Model Configuration Menu
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Added context window selection alongside model selection with:
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- **Development**: 8K tokens (fast, good for most cases)
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- **Production**: 16K tokens (balanced performance)
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- **Advanced**: 32K+ tokens (heavy development work)
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### 2. Educational Content
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Helps users understand:
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- Why context window size matters for RAG
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- Hardware implications of larger contexts
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- Optimal settings for their use case
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- Model-specific context capabilities
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### 3. Consistent Implementation
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- Updated all Ollama API calls to use consistent context settings
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- Ensured configuration applies across synthesis, expansion, and exploration
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- Added validation for context sizes against model capabilities
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- Provided clear error messages for invalid configurations
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## Technical Implementation
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Based on comprehensive research findings:
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### Model Context Capabilities
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- **qwen3:0.6b/1.7b**: 32K token maximum
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- **qwen3:4b**: 131K token maximum (YaRN extended)
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### Recommended Context Sizes
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```yaml
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# Conservative (fast, low memory)
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num_ctx: 8192 # ~6MB memory, excellent for exploration
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# Balanced (recommended for most users)
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num_ctx: 16384 # ~12MB memory, handles complex analysis
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# Advanced (heavy development work)
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num_ctx: 32768 # ~24MB memory, supports large codebases
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```
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### Configuration Integration
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- Added context window selection to TUI configuration menu
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- Updated config.yaml schema with context parameters
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- Implemented validation for model-specific limits
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- Provided migration for existing configurations
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## Benefits
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1. **Improved User Experience**
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- Longer conversation sessions
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- Better analysis quality
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- Clear performance expectations
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2. **Professional RAG Capability**
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- Support for enterprise-scale projects
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- Handles large codebases effectively
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- Enables advanced use cases
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3. **Educational Value**
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- Users learn about context windows
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- Better understanding of RAG performance
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- Informed decision making
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## Files Changed
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- `mini_rag/config.py`: Added context window configuration parameters
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- `mini_rag/llm_synthesizer.py`: Dynamic context sizing with model awareness
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- `mini_rag/explorer.py`: Consistent context application
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- `rag-tui.py`: Enhanced configuration menu with context selection
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- `PR_DRAFT.md`: Documentation of implementation approach
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## Testing Recommendations
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1. Test context configuration menu with different models
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2. Verify context limits are enforced correctly
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3. Test conversation length with different context sizes
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4. Validate memory usage estimates
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5. Test advanced use cases (15+ results, large chunks)
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---
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**This PR significantly improves FSS-Mini-RAG's performance and user experience by properly configuring one of the most critical parameters for RAG systems.**
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**Ready for review and testing!** 🚀
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PR_DRAFT.md
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PR_DRAFT.md
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# Add Context Window Configuration for Optimal RAG Performance
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## Problem Statement
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Currently, FSS-Mini-RAG uses Ollama's default context window settings, which severely limits performance:
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- **Default 2048 tokens** is inadequate for RAG applications
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- Users can't configure context window for their hardware/use case
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- No guidance on optimal context sizes for different models
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- Inconsistent context handling across the codebase
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- New users don't understand context window importance
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## Impact on User Experience
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**With 2048 token context window:**
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- Only 1-2 responses possible before context truncation
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- Thinking tokens consume significant context space
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- Poor performance with larger document chunks
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- Frustrated users who don't understand why responses degrade
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**With proper context configuration:**
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- 5-15+ responses in exploration mode
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- Support for advanced use cases (15+ results, 4000+ character chunks)
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- Better coding assistance and analysis
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- Professional-grade RAG experience
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## Proposed Solution
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### 1. Enhanced Model Configuration Menu
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Add context window selection alongside model selection with:
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- **Development**: 8K tokens (fast, good for most cases)
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- **Production**: 16K tokens (balanced performance)
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- **Advanced**: 32K+ tokens (heavy development work)
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### 2. Educational Content
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Help users understand:
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- Why context window size matters for RAG
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- Hardware implications of larger contexts
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- Optimal settings for their use case
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- Model-specific context capabilities
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### 3. Consistent Implementation
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- Update all Ollama API calls to use consistent context settings
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- Ensure configuration applies across synthesis, expansion, and exploration
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- Validate context sizes against model capabilities
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- Provide clear error messages for invalid configurations
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## Technical Implementation
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Based on research findings:
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### Model Context Capabilities
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- **qwen3:0.6b/1.7b**: 32K token maximum
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- **qwen3:4b**: 131K token maximum (YaRN extended)
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### Recommended Context Sizes
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```yaml
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# Conservative (fast, low memory)
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num_ctx: 8192 # ~6MB memory, excellent for exploration
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# Balanced (recommended for most users)
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num_ctx: 16384 # ~12MB memory, handles complex analysis
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# Advanced (heavy development work)
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num_ctx: 32768 # ~24MB memory, supports large codebases
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```
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### Configuration Integration
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- Add context window selection to TUI configuration menu
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- Update config.yaml schema with context parameters
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- Implement validation for model-specific limits
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- Provide migration for existing configurations
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## Benefits
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1. **Improved User Experience**
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- Longer conversation sessions
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- Better analysis quality
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- Clear performance expectations
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2. **Professional RAG Capability**
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- Support for enterprise-scale projects
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- Handles large codebases effectively
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- Enables advanced use cases
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3. **Educational Value**
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- Users learn about context windows
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- Better understanding of RAG performance
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- Informed decision making
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## Implementation Plan
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1. **Phase 1**: Research Ollama context handling (✅ Complete)
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2. **Phase 2**: Update configuration system (✅ Complete)
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3. **Phase 3**: Enhance TUI with context selection (✅ Complete)
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4. **Phase 4**: Update all API calls consistently (✅ Complete)
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5. **Phase 5**: Add documentation and validation (✅ Complete)
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## Implementation Details
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### Configuration System
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- Added `context_window` and `auto_context` to LLMConfig
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- Default 16K context (vs problematic 2K default)
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- Model-specific validation and limits
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- YAML output includes helpful context explanations
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### TUI Enhancement
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- New "Configure context window" menu option
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- Educational content about context importance
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- Three presets: Development (8K), Production (16K), Advanced (32K)
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- Custom size entry with validation
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- Memory usage estimates for each option
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### API Consistency
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- Dynamic context sizing via `_get_optimal_context_size()`
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- Model capability awareness (qwen3:4b = 131K, others = 32K)
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- Applied consistently to synthesizer and explorer
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- Automatic capping at model limits
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### User Education
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- Clear explanations of why context matters for RAG
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- Memory usage implications (8K = 6MB, 16K = 12MB, 32K = 24MB)
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- Advanced use case guidance (15+ results, 4000+ chunks)
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- Performance vs quality tradeoffs
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## Answers to Review Questions
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1. ✅ **Auto-detection**: Implemented via `auto_context` flag that respects model limits
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2. ✅ **Model changes**: Dynamic validation against current model capabilities
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3. ✅ **Scope**: Global configuration with per-model validation
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4. ✅ **Validation**: Comprehensive validation with clear error messages and guidance
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---
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**This PR will significantly improve FSS-Mini-RAG's performance and user experience by properly configuring one of the most critical parameters for RAG systems.**
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