Context Window Management

The Context Window Management system maintains and prioritizes content within token limitations for the RAG (Retrieval Augmented Generation) system, ensuring the most relevant information is preserved when constraints are reached.

Purpose

Efficiently manage token usage while prioritizing relevant content to optimize the use of the available context window (typically 200k tokens for Claude models).

Architecture

Core Components

ContextWindowManager

Central class that manages the context window:

Tracks token usage and content prioritization
Implements automatic optimization when approaching capacity threshold
Integrates with MetricsCollector for performance tracking

ContextWindowConfig

Configuration class for the Context Window Management system:

Defines token limits, capacity thresholds, and prioritization strategies
Provides default values and integration with BaseConfig

Token Counter

Utility for counting tokens in various content types:

Supports strings, lists, dictionaries, and objects
Provides consistent token counting across the system

Error Handling

Dedicated error codes in the 2100-2199 range with specialized exceptions for different error scenarios.

Data Structures

Content Storage

Dictionary-based storage for content items with metadata:

Token count
Importance score
Last access time
Access frequency

Priority Queue

Maintains content ordered by importance:

Supports hybrid prioritization based on multiple factors
Enables efficient optimization when capacity threshold is reached

Configuration Options

class ContextWindowConfig:
    """Configuration for the Context Window Management system."""
    max_token_limit: int = 150000       # Default to 150k tokens
    capacity_threshold: float = 0.75    # Default to 75% capacity
    prioritization_strategy: str = "hybrid"  # relevance, recency, hybrid
    min_content_importance: float = 0.2  # Minimum importance to keep

Content Prioritization

The system prioritizes content based on a combination of factors:

Importance Score: Explicitly assigned importance value (0.0-1.0)
Recency: How recently the content was accessed
Access Frequency: How often the content is accessed

Prioritization Strategies

Strategy	Description
`relevance`	Prioritizes based on importance score only
`recency`	Prioritizes based on access time only
`hybrid`	Combines importance, recency, and frequency (default)

Optimization Process

When the context window approaches the capacity threshold (default 75%):

Calculate priority scores for all content items
Sort content by priority (lowest first)
Remove lowest priority items until below target capacity
Update metrics and statistics

Integration Points

MetricsCollector Integration

Records token usage for performance monitoring
Tracks optimization events and content management
Provides statistics for analysis and optimization

LifecycleManager Integration

Prepares for integration with object lifecycle management
Will coordinate with lifecycle events for content management
Enables seamless integration with the broader RAG system

BaseConfig Integration

Uses configuration values from BaseConfig when available
Falls back to sensible defaults when not configured
Provides a consistent configuration approach

Error Handling

Error Codes (2100-2199)

        flowchart TB
    CWE["ContextWindowError (2100)"] --> TCE["TokenCountError (2101)"]
    CWE --> CEE["CapacityExceededError (2102)"]
    CWE --> OFE["OptimizationFailedError (2103)"]
    CWE --> CPE["ContentPriorityError (2110)"]
    CWE --> CRE["ContentRemovalError (2111)"]
    CWE --> CIE["ContentInsertionError (2112)"]
    CWE --> LIE["LifecycleIntegrationError (2120)"]
    CWE --> MCE["MetricsCollectionError (2121)"]
    CWE --> CFE["ConfigurationError (2122)"]

See Error Codes for the complete error code reference.

Usage Examples

Basic Usage

from babylon.rag.context_window import ContextWindowManager, ContextWindowConfig
from babylon.metrics.collector import MetricsCollector

# Create configuration
config = ContextWindowConfig(
    max_token_limit=100000,
    capacity_threshold=0.8,
    prioritization_strategy="hybrid"
)

# Create manager
metrics_collector = MetricsCollector()
context_window = ContextWindowManager(
    config=config,
    metrics_collector=metrics_collector
)

# Add content
context_window.add_content(
    content_id="document1",
    content="This is a sample document",
    token_count=5,
    importance=0.8
)

# Get content
content = context_window.get_content("document1")

# Remove content
context_window.remove_content("document1")

# Get statistics
stats = context_window.get_stats()

Handling Optimization

# Manually trigger optimization
context_window.optimize(target_tokens=50000)

# Automatic optimization occurs when adding content
# that would exceed threshold
try:
    context_window.add_content(
        "large_content", "..." * 10000, 30000, 0.5
    )
except CapacityExceededError as e:
    print(f"Error {e.code}: {e}")

Current Status

Implemented:

Token counting and tracking
Content prioritization
Automatic optimization
MetricsCollector integration
Comprehensive error handling
Unit tests with >80% coverage

Pending:

LifecycleManager integration
More sophisticated prioritization algorithms
Performance optimizations for large content sets

Key Considerations

Performance is critical as this system operates in the critical path
Token counting must be consistent with the underlying model’s tokenization
Prioritization logic may need tuning based on specific use cases
Error handling should be robust to prevent cascading failures
Memory usage should be optimized for large content sets