Fss-Rag-Mini/PR_DRAFT.md

Add Context Window Configuration for Optimal RAG Performance

Problem Statement

Currently, FSS-Mini-RAG uses Ollama's default context window settings, which severely limits performance:

• Default 2048 tokens is inadequate for RAG applications
• Users can't configure context window for their hardware/use case
• No guidance on optimal context sizes for different models
• Inconsistent context handling across the codebase
• New users don't understand context window importance

Impact on User Experience

With 2048 token context window:

• Only 1-2 responses possible before context truncation
• Thinking tokens consume significant context space
• Poor performance with larger document chunks
• Frustrated users who don't understand why responses degrade

With proper context configuration:

• 5-15+ responses in exploration mode
• Support for advanced use cases (15+ results, 4000+ character chunks)
• Better coding assistance and analysis
• Professional-grade RAG experience

Proposed Solution

1. Enhanced Model Configuration Menu

Add context window selection alongside model selection with:

• Development: 8K tokens (fast, good for most cases)
• Production: 16K tokens (balanced performance)
• Advanced: 32K+ tokens (heavy development work)

2. Educational Content

Help users understand:

• Why context window size matters for RAG
• Hardware implications of larger contexts
• Optimal settings for their use case
• Model-specific context capabilities

3. Consistent Implementation

• Update all Ollama API calls to use consistent context settings
• Ensure configuration applies across synthesis, expansion, and exploration
• Validate context sizes against model capabilities
• Provide clear error messages for invalid configurations

Technical Implementation

Based on research findings:

Model Context Capabilities

• qwen3:0.6b/1.7b: 32K token maximum
• qwen3:4b: 131K token maximum (YaRN extended)

Recommended Context Sizes

# Conservative (fast, low memory)
num_ctx: 8192    # ~6MB memory, excellent for exploration

# Balanced (recommended for most users)  
num_ctx: 16384   # ~12MB memory, handles complex analysis

# Advanced (heavy development work)
num_ctx: 32768   # ~24MB memory, supports large codebases

Configuration Integration

• Add context window selection to TUI configuration menu
• Update config.yaml schema with context parameters
• Implement validation for model-specific limits
• Provide migration for existing configurations

Benefits

1. Improved User Experience

   • Longer conversation sessions
   • Better analysis quality
   • Clear performance expectations

2. Professional RAG Capability

   • Support for enterprise-scale projects
   • Handles large codebases effectively
   • Enables advanced use cases

3. Educational Value

   • Users learn about context windows
   • Better understanding of RAG performance
   • Informed decision making

Implementation Plan

1. Phase 1: Research Ollama context handling (✅ Complete)
2. Phase 2: Update configuration system (✅ Complete)
3. Phase 3: Enhance TUI with context selection (✅ Complete)
4. Phase 4: Update all API calls consistently (✅ Complete)
5. Phase 5: Add documentation and validation (✅ Complete)

Implementation Details

Configuration System

• Added context_window and auto_context to LLMConfig
• Default 16K context (vs problematic 2K default)
• Model-specific validation and limits
• YAML output includes helpful context explanations

TUI Enhancement

• New "Configure context window" menu option
• Educational content about context importance
• Three presets: Development (8K), Production (16K), Advanced (32K)
• Custom size entry with validation
• Memory usage estimates for each option

API Consistency

• Dynamic context sizing via _get_optimal_context_size()
• Model capability awareness (qwen3:4b = 131K, others = 32K)
• Applied consistently to synthesizer and explorer
• Automatic capping at model limits

User Education

• Clear explanations of why context matters for RAG
• Memory usage implications (8K = 6MB, 16K = 12MB, 32K = 24MB)
• Advanced use case guidance (15+ results, 4000+ chunks)
• Performance vs quality tradeoffs

Answers to Review Questions

1. ✅ Auto-detection: Implemented via auto_context flag that respects model limits
2. ✅ Model changes: Dynamic validation against current model capabilities
3. ✅ Scope: Global configuration with per-model validation
4. ✅ Validation: Comprehensive validation with clear error messages and guidance

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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.