643 lines
20 KiB
Markdown
643 lines
20 KiB
Markdown
# BMAD Context Management Engine
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## Overview
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The Context Management Engine provides sophisticated context preservation, sharing, and persistence across persona interactions, ensuring seamless continuity and intelligent context-aware decision making throughout the BMAD Method workflow.
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## Core Architecture
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### Context Data Model
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#### Hierarchical Context Structure
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\```yaml
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context_hierarchy:
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global_context:
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description: "System-wide context shared across all interactions"
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components:
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- "user_preferences"
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- "system_configuration"
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- "global_constraints"
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- "organizational_standards"
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project_context:
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description: "Project-specific context shared within project scope"
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components:
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- "project_goals_and_objectives"
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- "stakeholder_information"
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- "technical_requirements"
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- "timeline_and_milestones"
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- "budget_and_resource_constraints"
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- "quality_standards"
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session_context:
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description: "Session-specific context for current interaction"
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components:
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- "current_conversation_history"
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- "active_personas"
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- "workflow_state"
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- "pending_decisions"
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- "temporary_artifacts"
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persona_context:
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description: "Persona-specific context and working memory"
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components:
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- "persona_working_memory"
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- "specialized_knowledge_cache"
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- "persona_specific_preferences"
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- "task_specific_context"
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- "collaboration_history"
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```
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#### Context Entity Schema
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\```json
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{
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"context_entity": {
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"id": "unique_context_identifier",
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"type": "global|project|session|persona",
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"scope": "context_scope_definition",
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"created_at": "timestamp",
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"updated_at": "timestamp",
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"version": "context_version_number",
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"metadata": {
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"owner": "context_owner",
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"access_level": "public|private|restricted",
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"retention_policy": "retention_duration",
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"encryption_level": "none|standard|high"
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},
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"content": {
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"structured_data": {},
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"unstructured_data": "",
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"relationships": [],
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"tags": [],
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"priority": "high|medium|low"
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},
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"access_control": {
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"read_permissions": [],
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"write_permissions": [],
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"share_permissions": []
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}
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}
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}
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```
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### Context Lifecycle Management
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#### Context Creation and Initialization
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```python
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def create_context(context_type, scope, initial_data, metadata=None):
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"""
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Create new context entity with proper initialization
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"""
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context_id = generate_context_id(context_type, scope)
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context_entity = {
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'id': context_id,
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'type': context_type,
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'scope': scope,
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'created_at': get_current_timestamp(),
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'updated_at': get_current_timestamp(),
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'version': 1,
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'metadata': initialize_metadata(metadata),
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'content': structure_initial_data(initial_data),
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'access_control': initialize_access_control(context_type)
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}
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# Validate context structure
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validate_context_structure(context_entity)
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# Store context
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store_context(context_entity)
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# Initialize relationships
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initialize_context_relationships(context_entity)
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return context_id
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def initialize_metadata(metadata):
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"""Initialize context metadata with defaults"""
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default_metadata = {
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'owner': get_current_user(),
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'access_level': 'private',
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'retention_policy': '30_days',
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'encryption_level': 'standard'
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}
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if metadata:
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default_metadata.update(metadata)
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return default_metadata
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```
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#### Context Update and Versioning
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\```yaml
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versioning_strategy:
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version_control:
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strategy: "semantic_versioning"
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major_changes: "structural_modifications"
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minor_changes: "content_additions"
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patch_changes: "content_updates"
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change_tracking:
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track_changes: true
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change_log_retention: "90_days"
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diff_calculation: "intelligent_diff"
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conflict_resolution:
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concurrent_updates: "last_writer_wins_with_merge"
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conflict_detection: "content_hash_comparison"
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merge_strategy: "intelligent_merge_with_user_review"
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rollback_capability:
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rollback_support: true
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rollback_window: "7_days"
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rollback_granularity: "field_level"
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```
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### Context Sharing and Synchronization
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#### Intelligent Context Sharing Algorithm
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```python
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def share_context_between_personas(source_persona, target_persona, context_filter=None):
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"""
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Intelligently share relevant context between personas
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"""
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# Get source persona context
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source_context = get_persona_context(source_persona)
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# Analyze target persona requirements
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target_requirements = analyze_persona_context_needs(target_persona)
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# Filter relevant context
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relevant_context = filter_relevant_context(
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source_context,
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target_requirements,
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context_filter
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)
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# Apply privacy and security filters
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filtered_context = apply_privacy_filters(relevant_context, target_persona)
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# Transform context for target persona
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transformed_context = transform_context_for_persona(filtered_context, target_persona)
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# Validate context compatibility
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validate_context_compatibility(transformed_context, target_persona)
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# Transfer context
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transfer_context(transformed_context, target_persona)
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# Log context sharing
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log_context_sharing(source_persona, target_persona, transformed_context)
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return {
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'transfer_successful': True,
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'context_items_transferred': len(transformed_context),
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'transfer_timestamp': get_current_timestamp()
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}
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def filter_relevant_context(source_context, target_requirements, context_filter):
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"""Filter context based on relevance and requirements"""
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relevance_scores = {}
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for context_item in source_context:
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# Calculate relevance score
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relevance_score = calculate_context_relevance(context_item, target_requirements)
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# Apply custom filters if provided
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if context_filter:
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relevance_score = apply_custom_filter(relevance_score, context_item, context_filter)
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# Include if above threshold
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if relevance_score >= get_relevance_threshold():
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relevance_scores[context_item.id] = relevance_score
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# Sort by relevance and return top items
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sorted_items = sorted(relevance_scores.items(), key=lambda x: x[1], reverse=True)
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return [get_context_item(item_id) for item_id, score in sorted_items]
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```
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#### Context Synchronization Strategies
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\```yaml
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synchronization_patterns:
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real_time_sync:
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description: "Immediate context synchronization"
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use_cases: ["critical_decisions", "urgent_handoffs", "error_conditions"]
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latency_target: "< 100ms"
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batch_sync:
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description: "Periodic batch synchronization"
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use_cases: ["routine_updates", "background_processing", "optimization"]
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frequency: "every_5_minutes"
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event_driven_sync:
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description: "Synchronization triggered by specific events"
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use_cases: ["persona_switches", "milestone_completion", "context_changes"]
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trigger_events: ["persona_handoff", "workflow_transition", "user_action"]
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lazy_sync:
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description: "On-demand synchronization when context is accessed"
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use_cases: ["infrequent_access", "large_context_sets", "resource_optimization"]
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cache_strategy: "intelligent_prefetching"
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```
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### Context Persistence and Storage
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#### Multi-Tier Storage Architecture
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\```yaml
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storage_tiers:
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hot_storage:
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description: "Frequently accessed context in memory"
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technology: "redis_cluster"
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capacity: "active_session_context"
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access_time: "< 10ms"
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retention: "session_duration"
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warm_storage:
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description: "Recently accessed context in fast storage"
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technology: "ssd_database"
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capacity: "recent_project_context"
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access_time: "< 50ms"
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retention: "30_days"
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cold_storage:
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description: "Archived context in cost-effective storage"
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technology: "object_storage"
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capacity: "historical_context"
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access_time: "< 500ms"
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retention: "1_year"
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archive_storage:
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description: "Long-term archived context"
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technology: "compressed_archive"
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capacity: "compliance_retention"
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access_time: "< 5_seconds"
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retention: "7_years"
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```
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#### Context Persistence Strategies
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```python
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def persist_context(context_entity, persistence_level='standard'):
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"""
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Persist context with appropriate storage strategy
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"""
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# Determine storage tier based on context type and access patterns
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storage_tier = determine_storage_tier(context_entity, persistence_level)
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# Prepare context for storage
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prepared_context = prepare_context_for_storage(context_entity, storage_tier)
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# Apply compression if appropriate
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if should_compress_context(prepared_context, storage_tier):
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prepared_context = compress_context(prepared_context)
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# Apply encryption based on sensitivity
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if requires_encryption(context_entity):
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prepared_context = encrypt_context(prepared_context, context_entity.metadata.encryption_level)
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# Store in appropriate tier
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storage_result = store_in_tier(prepared_context, storage_tier)
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# Update context index
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update_context_index(context_entity.id, storage_tier, storage_result.location)
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# Set up retention policy
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schedule_retention_policy(context_entity.id, context_entity.metadata.retention_policy)
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return {
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'persistence_successful': True,
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'storage_tier': storage_tier,
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'storage_location': storage_result.location,
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'compression_applied': prepared_context.compressed,
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'encryption_applied': prepared_context.encrypted
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}
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def retrieve_context(context_id, access_pattern='standard'):
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"""
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Retrieve context with intelligent caching and prefetching
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"""
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# Check hot cache first
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cached_context = check_hot_cache(context_id)
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if cached_context:
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update_access_statistics(context_id, 'cache_hit')
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return cached_context
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# Locate context in storage tiers
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storage_location = locate_context_in_storage(context_id)
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# Retrieve from appropriate tier
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stored_context = retrieve_from_storage(storage_location)
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# Decrypt if necessary
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if stored_context.encrypted:
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stored_context = decrypt_context(stored_context)
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# Decompress if necessary
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if stored_context.compressed:
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stored_context = decompress_context(stored_context)
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# Cache in hot storage for future access
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cache_in_hot_storage(context_id, stored_context)
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# Prefetch related context if appropriate
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if should_prefetch_related_context(context_id, access_pattern):
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prefetch_related_context(context_id)
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# Update access statistics
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update_access_statistics(context_id, 'storage_retrieval')
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return stored_context
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```
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### Privacy and Security Framework
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#### Access Control and Permissions
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\```yaml
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access_control_model:
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role_based_access:
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roles:
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- "context_owner"
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- "project_member"
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- "persona_user"
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- "system_administrator"
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- "auditor"
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permissions:
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read: ["view_context_content", "access_context_metadata"]
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write: ["modify_context_content", "update_context_metadata"]
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share: ["grant_access_to_others", "create_context_links"]
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delete: ["remove_context", "purge_context_history"]
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admin: ["manage_access_control", "configure_retention_policies"]
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attribute_based_access:
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attributes:
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- "context_sensitivity_level"
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- "user_clearance_level"
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- "project_membership"
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- "persona_authorization"
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- "time_based_restrictions"
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policies:
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- "high_sensitivity_requires_high_clearance"
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- "project_context_requires_project_membership"
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- "persona_context_requires_persona_authorization"
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- "time_restricted_context_enforces_time_limits"
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```
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#### Data Privacy and Compliance
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```python
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def apply_privacy_protection(context_entity, target_persona):
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"""
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Apply privacy protection based on context sensitivity and target persona
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"""
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# Assess context sensitivity
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sensitivity_level = assess_context_sensitivity(context_entity)
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# Get target persona clearance
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persona_clearance = get_persona_clearance_level(target_persona)
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# Apply privacy filters
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if sensitivity_level > persona_clearance:
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# Redact sensitive information
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filtered_context = redact_sensitive_information(context_entity, sensitivity_level, persona_clearance)
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else:
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filtered_context = context_entity
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# Apply data minimization
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minimized_context = apply_data_minimization(filtered_context, target_persona)
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# Log privacy protection actions
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log_privacy_protection(context_entity.id, target_persona, sensitivity_level, persona_clearance)
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return minimized_context
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def redact_sensitive_information(context_entity, sensitivity_level, clearance_level):
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"""Redact information based on sensitivity and clearance levels"""
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redaction_rules = {
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'personal_data': ['names', 'emails', 'phone_numbers', 'addresses'],
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'financial_data': ['budgets', 'costs', 'revenue_projections'],
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'technical_secrets': ['api_keys', 'passwords', 'proprietary_algorithms'],
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'business_confidential': ['strategic_plans', 'competitive_analysis', 'internal_processes']
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}
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redacted_context = copy.deepcopy(context_entity)
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for data_type, fields in redaction_rules.items():
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if should_redact_data_type(data_type, sensitivity_level, clearance_level):
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redacted_context = redact_fields(redacted_context, fields)
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return redacted_context
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```
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### Performance Optimization
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#### Intelligent Caching Strategy
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\```yaml
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caching_strategies:
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multi_level_cache:
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l1_cache:
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description: "In-memory cache for immediate access"
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technology: "application_memory"
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capacity: "100MB"
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ttl: "5_minutes"
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l2_cache:
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description: "Distributed cache for shared access"
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technology: "redis_cluster"
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capacity: "1GB"
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ttl: "30_minutes"
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l3_cache:
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description: "Persistent cache for warm data"
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technology: "ssd_cache"
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capacity: "10GB"
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ttl: "24_hours"
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cache_policies:
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eviction_policy: "lru_with_priority_boost"
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prefetch_strategy: "predictive_prefetching"
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invalidation_strategy: "smart_invalidation"
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cache_optimization:
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compression: "context_aware_compression"
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serialization: "efficient_binary_serialization"
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partitioning: "context_type_based_partitioning"
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```
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#### Context Retrieval Optimization
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```python
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def optimize_context_retrieval(context_query, performance_target='standard'):
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"""
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Optimize context retrieval based on query patterns and performance targets
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"""
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# Analyze query pattern
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query_analysis = analyze_context_query(context_query)
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# Determine optimization strategy
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optimization_strategy = determine_optimization_strategy(query_analysis, performance_target)
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# Apply query optimization
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optimized_query = apply_query_optimization(context_query, optimization_strategy)
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# Execute with performance monitoring
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start_time = get_current_timestamp()
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if optimization_strategy.use_parallel_retrieval:
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results = execute_parallel_retrieval(optimized_query)
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else:
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results = execute_sequential_retrieval(optimized_query)
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end_time = get_current_timestamp()
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retrieval_time = end_time - start_time
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# Apply result optimization
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optimized_results = optimize_results(results, query_analysis)
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# Update performance metrics
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update_performance_metrics(context_query, retrieval_time, len(optimized_results))
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# Learn from performance
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learn_from_retrieval_performance(context_query, optimization_strategy, retrieval_time)
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return {
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'results': optimized_results,
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'retrieval_time': retrieval_time,
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'optimization_applied': optimization_strategy,
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'performance_target_met': retrieval_time <= get_performance_target(performance_target)
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}
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```
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### Context Intelligence and Learning
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#### Context Pattern Recognition
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\```yaml
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pattern_recognition:
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usage_patterns:
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frequent_access_patterns: "identify_commonly_accessed_context_combinations"
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temporal_patterns: "recognize_time_based_context_usage"
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persona_patterns: "learn_persona_specific_context_preferences"
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workflow_patterns: "understand_context_flow_in_workflows"
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optimization_opportunities:
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prefetch_candidates: "contexts_likely_to_be_accessed_together"
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cache_optimization: "contexts_that_benefit_from_longer_caching"
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compression_candidates: "contexts_suitable_for_compression"
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archival_candidates: "contexts_ready_for_archival"
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anomaly_detection:
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unusual_access_patterns: "detect_suspicious_context_access"
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performance_anomalies: "identify_performance_degradation"
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data_integrity_issues: "detect_context_corruption_or_inconsistency"
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```
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#### Adaptive Context Management
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```python
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def adapt_context_management(usage_statistics, performance_metrics, user_feedback):
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"""
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Adapt context management strategies based on learning
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"""
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# Analyze usage patterns
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usage_patterns = analyze_usage_patterns(usage_statistics)
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# Identify optimization opportunities
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optimization_opportunities = identify_optimization_opportunities(
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usage_patterns,
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performance_metrics
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)
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|
|
|
# Generate adaptation recommendations
|
|
adaptations = generate_adaptation_recommendations(
|
|
optimization_opportunities,
|
|
user_feedback
|
|
)
|
|
|
|
# Apply safe adaptations automatically
|
|
safe_adaptations = filter_safe_adaptations(adaptations)
|
|
apply_adaptations(safe_adaptations)
|
|
|
|
# Queue risky adaptations for review
|
|
risky_adaptations = filter_risky_adaptations(adaptations)
|
|
queue_for_review(risky_adaptations)
|
|
|
|
# Monitor adaptation impact
|
|
monitor_adaptation_impact(safe_adaptations)
|
|
|
|
return {
|
|
'adaptations_applied': len(safe_adaptations),
|
|
'adaptations_queued': len(risky_adaptations),
|
|
'expected_performance_improvement': estimate_performance_improvement(adaptations)
|
|
}
|
|
```
|
|
|
|
### Monitoring and Analytics
|
|
|
|
#### Context Health Monitoring
|
|
\```yaml
|
|
health_metrics:
|
|
availability_metrics:
|
|
context_availability: "> 99.9%"
|
|
retrieval_success_rate: "> 99.5%"
|
|
storage_reliability: "> 99.99%"
|
|
|
|
performance_metrics:
|
|
average_retrieval_time: "< 100ms"
|
|
95th_percentile_retrieval_time: "< 200ms"
|
|
cache_hit_ratio: "> 80%"
|
|
|
|
quality_metrics:
|
|
context_accuracy: "> 95%"
|
|
context_completeness: "> 90%"
|
|
context_relevance: "> 85%"
|
|
|
|
security_metrics:
|
|
unauthorized_access_attempts: "< 0.1%"
|
|
privacy_violations: "0"
|
|
data_breach_incidents: "0"
|
|
```
|
|
|
|
#### Analytics and Insights
|
|
```python
|
|
def generate_context_analytics(time_period='last_30_days'):
|
|
"""
|
|
Generate comprehensive analytics on context usage and performance
|
|
"""
|
|
|
|
# Collect metrics data
|
|
usage_data = collect_usage_metrics(time_period)
|
|
performance_data = collect_performance_metrics(time_period)
|
|
quality_data = collect_quality_metrics(time_period)
|
|
|
|
# Generate insights
|
|
insights = {
|
|
'usage_insights': analyze_usage_trends(usage_data),
|
|
'performance_insights': analyze_performance_trends(performance_data),
|
|
'quality_insights': analyze_quality_trends(quality_data),
|
|
'optimization_recommendations': generate_optimization_recommendations(
|
|
usage_data, performance_data, quality_data
|
|
)
|
|
}
|
|
|
|
# Create visualizations
|
|
visualizations = create_analytics_visualizations(insights)
|
|
|
|
# Generate reports
|
|
reports = generate_analytics_reports(insights, visualizations)
|
|
|
|
return {
|
|
'insights': insights,
|
|
'visualizations': visualizations,
|
|
'reports': reports,
|
|
'generated_at': get_current_timestamp()
|
|
}
|
|
```
|