Phase 2 Advanced Features Implementation

Date: May 2025
Features: Smart Prioritization & Cross-turn Correlation
Status: Implementation Complete - Ready for Validation

🎯 Overview

This document details the implementation of the remaining Phase 2 features:

  1. Smart Prioritization - Relevance-based snippet ranking
  2. Cross-turn Correlation - Linking related code or tool results across turns

These features build upon the successful Proactive Context Addition to create a more intelligent and coherent context management system.

✅ Feature 1: Smart Prioritization

Implementation Details

Module: devops/components/context_management/smart_prioritization.py

Purpose: Intelligently rank code snippets and tool results based on multiple relevance factors instead of simple recency/frequency sorting.

Scoring Components

  1. Content Relevance (35% weight):
    • Keyword matching with current conversation context
    • High-value keyword detection (config, setup, main, etc.)
    • File type relevance scoring
    • Code pattern recognition (functions, classes, imports)
  2. Recency Score (25% weight):
    • Turn-based decay function
    • Higher scores for recently accessed items
    • Exponential decay over 20 turns
  3. Frequency Score (15% weight):
    • Based on accumulated relevance from previous accesses
    • Normalized to 0-1 range
  4. Error Priority (15% weight):
    • Higher priority for error-related content
    • Keyword detection for error terms
    • Special handling for debugging contexts
  5. Context Coherence (10% weight):
    • File type and location relevance
    • Directory structure analysis
    • Configuration file bonuses

Integration Points

  • Code Snippets: Integrated into ContextManager.assemble_context()
  • Tool Results: Applied after collecting tool result dictionaries
  • Logging: Comprehensive debug logging with score breakdowns

Expected Benefits

  • More relevant context selection
  • Better handling of error scenarios
  • Improved code-to-documentation correlation
  • Reduced “noise” from irrelevant old snippets

✅ Feature 2: Cross-turn Correlation

Implementation Details

Module: devops/components/context_management/cross_turn_correlation.py

Purpose: Identify and link related items across conversation turns to maintain narrative coherence and help LLM understand project workflows.

Correlation Types

  1. File Similarity (30% weight):
    • Exact file matches (1.0 score)
    • Same directory (0.7 score)
    • Same file type group (0.5 score)
    • Same filename, different path (0.6 score)
  2. Content Similarity (25% weight):
    • Jaccard similarity on keywords
    • Code pattern matching (functions, classes, imports)
    • Bonus for shared programming constructs
  3. Temporal Proximity (20% weight):
    • Turn distance decay function
    • Higher scores for items close in time
    • Same turn (1.0) to 20+ turns apart (0.1)
  4. Tool Sequence Correlation (15% weight):
    • Recognizes common tool operation patterns
    • read_file → edit_file sequences
    • execute_command → read_file workflows
    • Tool family groupings (file ops, search ops)
  5. Error Continuation (10% weight):
    • Links errors to their resolutions
    • Sequential error patterns
    • Error → successful operation chains

Correlation Graph Structure

  • Snippet-to-Snippet: Links related code across files/turns
  • Tool-to-Tool: Links related operations and workflows
  • Cross-correlations: Links code snippets to relevant tool operations

Metadata Enrichment

Each context item receives correlation metadata:

{
    '_correlations': {
        'count': 3,                    # Number of related items
        'max_score': 0.847,           # Highest correlation score
        'related_indices': [1, 4, 7], # Indices of related items
        'scores': {1: 0.847, 4: 0.623, 7: 0.445}
    },
    '_cross_correlations': {
        'tools': [                     # Related tools (for snippets)
            {'tool_index': 2, 'score': 0.723, 'file_similarity': 1.0}
        ]
    }
}

Expected Benefits

  • Better narrative coherence across turns
  • Improved error resolution tracking
  • Enhanced file-based workflow understanding
  • More intelligent context clustering

🔧 Integration Architecture

Processing Pipeline

  1. Context Collection: Gather code snippets and tool results
  2. Smart Prioritization: Apply relevance-based ranking
  3. Cross-turn Correlation: Analyze relationships and add metadata
  4. Token Budget Allocation: Include items based on priority and correlations
  5. Assembly: Build final context with enhanced metadata

Performance Considerations

  • Lazy Evaluation: Correlations calculated only when needed
  • Threshold Filtering: Only meaningful correlations (>0.1 score) stored
  • Memory Efficient: Uses dictionaries instead of object graphs
  • Configurable Weights: Easy tuning of scoring factors

Logging and Debugging

Both features include comprehensive logging:

  • Score breakdowns for each item
  • Top-ranked items summary
  • Correlation statistics
  • Performance metrics

🧪 Validation Strategy

Test Scenarios

  1. File-based Workflows:
    • Test reading → editing → testing sequences
    • Verify cross-file correlations
    • Check configuration file prioritization
  2. Error Resolution Chains:
    • Test error → debugging → resolution sequences
    • Verify error priority scoring
    • Check error continuation linking
  3. Project Structure Understanding:
    • Test related file discovery
    • Verify directory-based correlations
    • Check tool sequence recognition
  4. Content Relevance:
    • Test keyword-based prioritization
    • Verify content similarity scoring
    • Check code pattern recognition

Expected Validation Results

  • Token Utilization: Should maintain ~1.7% while improving relevance
  • Context Quality: Better correlation between included items
  • Workflow Coherence: Clear linking of related operations
  • Error Handling: Improved debugging context assembly

🚀 Next Steps

  1. End-to-End Validation: Run comprehensive test scenarios
  2. Performance Monitoring: Track token utilization and correlation effectiveness
  3. Optimization: Fine-tune weights and thresholds based on real usage
  4. Phase 3 Planning: Prepare for intelligent summarization and dynamic expansion

📊 Implementation Status

Feature Implementation Integration Testing Status
Smart Prioritization ✅ Complete ✅ Complete 🔄 Pending Ready for Validation
Cross-turn Correlation ✅ Complete ✅ Complete 🔄 Pending Ready for Validation
Comprehensive Logging ✅ Complete ✅ Complete ✅ Built-in Production Ready
ContextManager Integration ✅ Complete ✅ Complete 🔄 Pending Ready for Validation

Implementation Status: ✅ COMPLETE - Ready for End-to-End Validation
Next Phase: Comprehensive testing and validation of enhanced context intelligence