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Implement comprehensive context window configuration system

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Add intelligent context window management for optimal RAG performance:

## Core Features
- Dynamic context sizing based on model capabilities
- User-friendly configuration menu with Development/Production/Advanced presets
- Automatic validation against model limits (qwen3:0.6b/1.7b = 32K, qwen3:4b = 131K)
- Educational content explaining context window importance for RAG

## Technical Implementation
- Enhanced LLMConfig with context_window and auto_context parameters
- Intelligent _get_optimal_context_size() method with model-specific limits
- Consistent context application across synthesizer and explorer
- YAML configuration output with helpful context explanations

## User Experience Improvements
- Clear context window display in configuration status
- Guided selection: Development (8K), Production (16K), Advanced (32K)
- Memory usage estimates and performance guidance
- Validation prevents invalid context/model combinations

## Educational Value
- Explains why default 2048 tokens fails for RAG
- Shows relationship between context size and conversation length
- Guides users toward optimal settings for their use case
- Highlights advanced capabilities (15+ results, 4000+ character chunks)

This addresses the critical issue where Ollama's default context severely
limits RAG performance, providing users with proper configuration tools
and understanding of this crucial parameter.
This commit is contained in:
BobAi 2025-08-15 13:09:53 +10:00
parent a84ff94fba
commit a189a4fe29
5 changed files with 518 additions and 14 deletions
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108
PR_DRAFT.md Normal file
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@ -0,0 +1,108 @@
# 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
```yaml
# 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
3. **Phase 3**: Enhance TUI with context selection
4. **Phase 4**: Update all API calls consistently
5. **Phase 5**: Add documentation and validation
## Questions for Review
1. Should we auto-detect optimal context based on available memory?
2. How should we handle model changes that affect context capabilities?
3. Should context be per-model or global configuration?
4. What validation should we provide for context/model combinations?
---
**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.**

9
mini_rag/config.py
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@ -81,6 +81,10 @@ class LLMConfig:
enable_thinking: bool = True # Enable thinking mode for Qwen3 models
cpu_optimized: bool = True # Prefer lightweight models
# Context window configuration (critical for RAG performance)
context_window: int = 16384 # Context window size in tokens (16K recommended)
auto_context: bool = True # Auto-adjust context based on model capabilities
# Model preference rankings (configurable)
model_rankings: list = None # Will be set in __post_init__
@ -255,6 +259,11 @@ class ConfigManager:
f" max_expansion_terms: {config_dict['llm']['max_expansion_terms']} # Maximum terms to add to queries",
f" enable_synthesis: {str(config_dict['llm']['enable_synthesis']).lower()} # Enable synthesis by default",
f" synthesis_temperature: {config_dict['llm']['synthesis_temperature']} # LLM temperature for analysis",
"",
" # Context window configuration (critical for RAG performance)",
f" context_window: {config_dict['llm']['context_window']} # Context size in tokens (8K=fast, 16K=balanced, 32K=advanced)",
f" auto_context: {str(config_dict['llm']['auto_context']).lower()} # Auto-adjust context based on model capabilities",
"",
" model_rankings: # Preferred model order (edit to change priority)",
])

2
mini_rag/explorer.py
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@ -408,7 +408,7 @@ Guidelines:
"temperature": temperature,
"top_p": optimal_params.get("top_p", 0.9),
"top_k": optimal_params.get("top_k", 40),
"num_ctx": optimal_params.get("num_ctx", 32768),
"num_ctx": self.synthesizer._get_optimal_context_size(model_to_use),
"num_predict": optimal_params.get("num_predict", 2000),
"repeat_penalty": optimal_params.get("repeat_penalty", 1.1),
"presence_penalty": optimal_params.get("presence_penalty", 1.0)

47
mini_rag/llm_synthesizer.py
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@ -114,6 +114,51 @@ class LLMSynthesizer:
self._initialized = True
def _get_optimal_context_size(self, model_name: str) -> int:
"""Get optimal context size based on model capabilities and configuration."""
# Get configured context window
if self.config and hasattr(self.config, 'llm'):
configured_context = self.config.llm.context_window
auto_context = getattr(self.config.llm, 'auto_context', True)
else:
configured_context = 16384 # Default to 16K
auto_context = True
# Model-specific maximum context windows (based on research)
model_limits = {
# Qwen3 models with native context support
'qwen3:0.6b': 32768, # 32K native
'qwen3:1.7b': 32768, # 32K native
'qwen3:4b': 131072, # 131K with YaRN extension
# Qwen2.5 models
'qwen2.5:1.5b': 32768, # 32K native
'qwen2.5:3b': 32768, # 32K native
'qwen2.5-coder:1.5b': 32768, # 32K native
# Fallback for unknown models
'default': 8192
}
# Find model limit (check for partial matches)
model_limit = model_limits.get('default', 8192)
for model_pattern, limit in model_limits.items():
if model_pattern != 'default' and model_pattern.lower() in model_name.lower():
model_limit = limit
break
# If auto_context is enabled, respect model limits
if auto_context:
optimal_context = min(configured_context, model_limit)
else:
optimal_context = configured_context
# Ensure minimum usable context for RAG
optimal_context = max(optimal_context, 4096) # Minimum 4K for basic RAG
logger.debug(f"Context for {model_name}: {optimal_context} tokens (configured: {configured_context}, limit: {model_limit})")
return optimal_context
def is_available(self) -> bool:
"""Check if Ollama is available and has models."""
self._ensure_initialized()
@ -174,7 +219,7 @@ class LLMSynthesizer:
"temperature": qwen3_temp,
"top_p": qwen3_top_p,
"top_k": qwen3_top_k,
"num_ctx": 32000, # Critical: Qwen3 context length (32K token limit)
"num_ctx": self._get_optimal_context_size(model_to_use), # Dynamic context based on model and config
"num_predict": optimal_params.get("num_predict", 2000),
"repeat_penalty": optimal_params.get("repeat_penalty", 1.1),
"presence_penalty": qwen3_presence

366
rag-tui.py
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@ -1353,8 +1353,10 @@ Your suggested question (under 10 words):"""
config_path = self.project_path / '.mini-rag' / 'config.yaml'
print("📋 Current Settings:")
print(f" 🤖 AI model: {config.llm.synthesis_model}")
print(f" 🧠 Context window: {config.llm.context_window} tokens")
print(f" 📁 Chunk size: {config.chunking.max_size} characters")
print(f" 🧠 Chunking strategy: {config.chunking.strategy}")
print(f" 🔄 Chunking strategy: {config.chunking.strategy}")
print(f" 🔍 Search results: {config.search.default_top_k} results")
print(f" 📊 Embedding method: {config.embedding.preferred_method}")
print(f" 🚀 Query expansion: {'enabled' if config.search.expand_queries else 'disabled'}")
@ -1362,12 +1364,14 @@ Your suggested question (under 10 words):"""
print()
print("🛠️ Quick Configuration Options:")
print(" 1. Adjust chunk size (performance vs accuracy)")
print(" 2. Toggle query expansion (smarter searches)")
print(" 3. Configure search behavior")
print(" 4. View/edit full configuration file")
print(" 5. Reset to defaults")
print(" 6. Advanced settings")
print(" 1. Select AI model (Fast/Recommended/Quality)")
print(" 2. Configure context window (Development/Production/Advanced)")
print(" 3. Adjust chunk size (performance vs accuracy)")
print(" 4. Toggle query expansion (smarter searches)")
print(" 5. Configure search behavior")
print(" 6. View/edit full configuration file")
print(" 7. Reset to defaults")
print(" 8. Advanced settings")
print()
print(" V. View current config file")
print(" B. Back to main menu")
@ -1386,16 +1390,20 @@ Your suggested question (under 10 words):"""
elif choice == 'v':
self._show_config_file(config_path)
elif choice == '1':
self._configure_chunk_size(config_manager, config)
self._configure_llm_model(config_manager, config)
elif choice == '2':
self._toggle_query_expansion(config_manager, config)
self._configure_context_window(config_manager, config)
elif choice == '3':
self._configure_search_behavior(config_manager, config)
self._configure_chunk_size(config_manager, config)
elif choice == '4':
self._edit_config_file(config_path)
self._toggle_query_expansion(config_manager, config)
elif choice == '5':
self._reset_config(config_manager)
self._configure_search_behavior(config_manager, config)
elif choice == '6':
self._edit_config_file(config_path)
elif choice == '7':
self._reset_config(config_manager)
elif choice == '8':
self._advanced_settings(config_manager, config)
else:
print("Invalid option. Press Enter to continue...")
@ -1422,6 +1430,340 @@ Your suggested question (under 10 words):"""
print("\n" + "=" * 50)
input("Press Enter to continue...")
def _configure_llm_model(self, config_manager, config):
"""Interactive LLM model selection with download capability."""
self.clear_screen()
print("🤖 AI Model Configuration")
print("=========================")
print()
# Check if Ollama is available
import subprocess
import requests
ollama_available = False
try:
subprocess.run(['ollama', '--version'], capture_output=True, check=True)
response = requests.get("http://localhost:11434/api/version", timeout=3)
ollama_available = response.status_code == 200
except:
pass
if not ollama_available:
print("❌ Ollama not available")
print()
print("To use AI features, please:")
print(" 1. Install Ollama: https://ollama.com/download")
print(" 2. Start the service: ollama serve")
print(" 3. Return to this menu")
print()
input("Press Enter to continue...")
return
# Get available models
try:
available_models = subprocess.run(['ollama', 'list'], capture_output=True, text=True, check=True)
model_lines = available_models.stdout.strip().split('\n')[1:] # Skip header
installed_models = [line.split()[0] for line in model_lines if line.strip()]
except:
installed_models = []
print("🧠 Why Small Models Work Great for RAG")
print("=====================================")
print()
print("RAG systems like FSS-Mini-RAG don't need massive models because:")
print("• The relevant code/docs are provided as context")
print("• Models focus on analysis, not memorizing facts")
print("• Even 0.6B models give excellent results with good context")
print("• Smaller models = faster responses = better user experience")
print()
print("💡 Advanced Use: For heavy development work with 15+ results")
print(" and 4000+ character chunks, even these models excel!")
print(" The 4B Qwen3 model will help you code remarkably well.")
print()
# Model options
model_options = {
'fast': {
'model': 'qwen3:0.6b',
'description': 'Ultra-fast responses (~500MB)',
'details': 'Perfect for quick searches and exploration. Surprisingly capable!'
},
'recommended': {
'model': 'qwen3:1.7b',
'description': 'Best balance of speed and quality (~1.4GB)',
'details': 'Ideal for most users. Great analysis with good speed.'
},
'quality': {
'model': 'qwen3:4b',
'description': 'Highest quality responses (~2.5GB)',
'details': 'Excellent for coding assistance and detailed analysis.'
}
}
print("🎯 Recommended Models:")
print()
for key, info in model_options.items():
is_installed = any(info['model'] in model for model in installed_models)
status = "✅ Installed" if is_installed else "📥 Available for download"
print(f" {key.upper()}: {info['model']}")
print(f" {info['description']} - {status}")
print(f" {info['details']}")
print()
current_model = config.llm.synthesis_model
print(f"Current model: {current_model}")
print()
print("Options:")
print(" F. Select Fast model (qwen3:0.6b)")
print(" R. Select Recommended model (qwen3:1.7b)")
print(" Q. Select Quality model (qwen3:4b)")
print(" C. Keep current model")
print(" B. Back to configuration menu")
print()
choice = input("Choose option: ").strip().lower()
selected_model = None
if choice == 'f':
selected_model = model_options['fast']['model']
elif choice == 'r':
selected_model = model_options['recommended']['model']
elif choice == 'q':
selected_model = model_options['quality']['model']
elif choice == 'c':
print("Keeping current model.")
input("Press Enter to continue...")
return
elif choice == 'b':
return
else:
print("Invalid option.")
input("Press Enter to continue...")
return
# Check if model is installed
model_installed = any(selected_model in model for model in installed_models)
if not model_installed:
print(f"\n📥 Model {selected_model} not installed.")
print("Would you like to download it now?")
print("This may take 2-5 minutes depending on your internet speed.")
print()
download = input("Download now? [Y/n]: ").strip().lower()
if download != 'n' and download != 'no':
print(f"\n🔄 Downloading {selected_model}...")
print("This may take a few minutes...")
try:
result = subprocess.run(['ollama', 'pull', selected_model],
capture_output=True, text=True, check=True)
print(f"✅ Successfully downloaded {selected_model}")
model_installed = True
except subprocess.CalledProcessError as e:
print(f"❌ Download failed: {e}")
print("You can try downloading manually later with:")
print(f" ollama pull {selected_model}")
input("Press Enter to continue...")
return
else:
print("Model not downloaded. Configuration not changed.")
input("Press Enter to continue...")
return
if model_installed:
# Update configuration
config.llm.synthesis_model = selected_model
config.llm.expansion_model = selected_model # Keep them in sync
try:
config_manager.save_config(config)
print(f"\n✅ Model updated to {selected_model}")
print("Configuration saved successfully!")
except Exception as e:
print(f"❌ Error saving configuration: {e}")
print()
input("Press Enter to continue...")
def _configure_context_window(self, config_manager, config):
"""Interactive context window configuration."""
self.clear_screen()
print("🧠 Context Window Configuration")
print("===============================")
print()
print("💡 Why Context Window Size Matters for RAG")
print("==========================================")
print()
print("Context window determines how much text the AI can 'remember' during conversation:")
print()
print("❌ Default 2048 tokens = Only 1-2 responses before forgetting")
print("✅ Proper context = 5-15+ responses with maintained conversation")
print()
print("For RAG systems like FSS-Mini-RAG:")
print("• Larger context = better analysis of multiple code files")
print("• Thinking tokens consume ~200-500 tokens per response")
print("• Search results can be 1000-3000 tokens depending on chunk size")
print("• Conversation history builds up over time")
print()
print("💻 Memory Usage Impact:")
print("• 8K context ≈ 6MB memory per conversation")
print("• 16K context ≈ 12MB memory per conversation")
print("• 32K context ≈ 24MB memory per conversation")
print()
current_context = config.llm.context_window
current_model = config.llm.synthesis_model
# Get model capabilities
model_limits = {
'qwen3:0.6b': 32768,
'qwen3:1.7b': 32768,
'qwen3:4b': 131072,
'qwen2.5:1.5b': 32768,
'qwen2.5:3b': 32768,
'default': 8192
}
model_limit = model_limits.get('default', 8192)
for model_pattern, limit in model_limits.items():
if model_pattern != 'default' and model_pattern.lower() in current_model.lower():
model_limit = limit
break
print(f"Current model: {current_model}")
print(f"Model maximum: {model_limit:,} tokens")
print(f"Current setting: {current_context:,} tokens")
print()
# Context options
context_options = {
'development': {
'size': 8192,
'description': 'Fast and efficient for most development work',
'details': 'Perfect for code exploration and basic analysis. Quick responses.',
'memory': '~6MB'
},
'production': {
'size': 16384,
'description': 'Balanced performance for professional use',
'details': 'Ideal for most users. Handles complex analysis well.',
'memory': '~12MB'
},
'advanced': {
'size': 32768,
'description': 'Maximum performance for heavy development',
'details': 'For large codebases, 15+ search results, complex analysis.',
'memory': '~24MB'
}
}
print("🎯 Recommended Context Sizes:")
print()
for key, info in context_options.items():
# Check if this size is supported by current model
if info['size'] <= model_limit:
status = "✅ Supported"
else:
status = f"❌ Exceeds model limit ({model_limit:,})"
print(f" {key.upper()}: {info['size']:,} tokens ({info['memory']})")
print(f" {info['description']} - {status}")
print(f" {info['details']}")
print()
print("Options:")
print(" D. Development (8K tokens - fast)")
print(" P. Production (16K tokens - balanced)")
print(" A. Advanced (32K tokens - maximum)")
print(" C. Custom size (manual entry)")
print(" K. Keep current setting")
print(" B. Back to configuration menu")
print()
choice = input("Choose option: ").strip().lower()
new_context = None
if choice == 'd':
new_context = context_options['development']['size']
elif choice == 'p':
new_context = context_options['production']['size']
elif choice == 'a':
new_context = context_options['advanced']['size']
elif choice == 'c':
print()
print("Enter custom context size in tokens:")
print(f" Minimum: 4096 (4K)")
print(f" Maximum for {current_model}: {model_limit:,}")
print()
try:
custom_size = int(input("Context size: ").strip())
if custom_size < 4096:
print("❌ Context too small. Minimum is 4096 tokens for RAG.")
input("Press Enter to continue...")
return
elif custom_size > model_limit:
print(f"❌ Context too large. Maximum for {current_model} is {model_limit:,} tokens.")
input("Press Enter to continue...")
return
else:
new_context = custom_size
except ValueError:
print("❌ Invalid number.")
input("Press Enter to continue...")
return
elif choice == 'k':
print("Keeping current context setting.")
input("Press Enter to continue...")
return
elif choice == 'b':
return
else:
print("Invalid option.")
input("Press Enter to continue...")
return
if new_context:
# Validate against model capabilities
if new_context > model_limit:
print(f"⚠️ Warning: {new_context:,} tokens exceeds {current_model} limit of {model_limit:,}")
print("The system will automatically cap at the model limit.")
print()
# Update configuration
config.llm.context_window = new_context
try:
config_manager.save_config(config)
print(f"✅ Context window updated to {new_context:,} tokens")
print()
# Provide usage guidance
if new_context >= 32768:
print("🚀 Advanced context enabled!")
print("• Perfect for large codebases and complex analysis")
print("• Try cranking up search results to 15+ for deep exploration")
print("• Increase chunk size to 4000+ characters for comprehensive context")
elif new_context >= 16384:
print("⚖️ Balanced context configured!")
print("• Great for professional development work")
print("• Supports extended conversations and analysis")
elif new_context >= 8192:
print("⚡ Development context set!")
print("• Fast responses with good conversation length")
print("• Perfect for code exploration and basic analysis")
print("Configuration saved successfully!")
except Exception as e:
print(f"❌ Error saving configuration: {e}")
print()
input("Press Enter to continue...")
def _configure_chunk_size(self, config_manager, config):
"""Interactive chunk size configuration."""
self.clear_screen()