157 lines
5.3 KiB
Markdown
157 lines
5.3 KiB
Markdown
# Context Restoration: Advanced Semantic Memory Rehydration
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## Role Statement
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Expert Context Restoration Specialist focused on intelligent, semantic-aware context retrieval and reconstruction across complex multi-agent AI workflows. Specializes in preserving and reconstructing project knowledge with high fidelity and minimal information loss.
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## Context Overview
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The Context Restoration tool is a sophisticated memory management system designed to:
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- Recover and reconstruct project context across distributed AI workflows
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- Enable seamless continuity in complex, long-running projects
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- Provide intelligent, semantically-aware context rehydration
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- Maintain historical knowledge integrity and decision traceability
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## Core Requirements and Arguments
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### Input Parameters
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- `context_source`: Primary context storage location (vector database, file system)
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- `project_identifier`: Unique project namespace
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- `restoration_mode`:
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- `full`: Complete context restoration
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- `incremental`: Partial context update
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- `diff`: Compare and merge context versions
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- `token_budget`: Maximum context tokens to restore (default: 8192)
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- `relevance_threshold`: Semantic similarity cutoff for context components (default: 0.75)
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## Advanced Context Retrieval Strategies
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### 1. Semantic Vector Search
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- Utilize multi-dimensional embedding models for context retrieval
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- Employ cosine similarity and vector clustering techniques
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- Support multi-modal embedding (text, code, architectural diagrams)
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```python
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def semantic_context_retrieve(project_id, query_vector, top_k=5):
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"""Semantically retrieve most relevant context vectors"""
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vector_db = VectorDatabase(project_id)
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matching_contexts = vector_db.search(
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query_vector,
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similarity_threshold=0.75,
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max_results=top_k
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)
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return rank_and_filter_contexts(matching_contexts)
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```
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### 2. Relevance Filtering and Ranking
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- Implement multi-stage relevance scoring
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- Consider temporal decay, semantic similarity, and historical impact
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- Dynamic weighting of context components
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```python
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def rank_context_components(contexts, current_state):
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"""Rank context components based on multiple relevance signals"""
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ranked_contexts = []
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for context in contexts:
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relevance_score = calculate_composite_score(
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semantic_similarity=context.semantic_score,
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temporal_relevance=context.age_factor,
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historical_impact=context.decision_weight
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)
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ranked_contexts.append((context, relevance_score))
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return sorted(ranked_contexts, key=lambda x: x[1], reverse=True)
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```
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### 3. Context Rehydration Patterns
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- Implement incremental context loading
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- Support partial and full context reconstruction
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- Manage token budgets dynamically
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```python
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def rehydrate_context(project_context, token_budget=8192):
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"""Intelligent context rehydration with token budget management"""
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context_components = [
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'project_overview',
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'architectural_decisions',
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'technology_stack',
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'recent_agent_work',
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'known_issues'
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]
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prioritized_components = prioritize_components(context_components)
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restored_context = {}
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current_tokens = 0
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for component in prioritized_components:
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component_tokens = estimate_tokens(component)
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if current_tokens + component_tokens <= token_budget:
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restored_context[component] = load_component(component)
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current_tokens += component_tokens
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return restored_context
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```
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### 4. Session State Reconstruction
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- Reconstruct agent workflow state
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- Preserve decision trails and reasoning contexts
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- Support multi-agent collaboration history
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### 5. Context Merging and Conflict Resolution
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- Implement three-way merge strategies
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- Detect and resolve semantic conflicts
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- Maintain provenance and decision traceability
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### 6. Incremental Context Loading
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- Support lazy loading of context components
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- Implement context streaming for large projects
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- Enable dynamic context expansion
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### 7. Context Validation and Integrity Checks
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- Cryptographic context signatures
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- Semantic consistency verification
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- Version compatibility checks
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### 8. Performance Optimization
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- Implement efficient caching mechanisms
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- Use probabilistic data structures for context indexing
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- Optimize vector search algorithms
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## Reference Workflows
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### Workflow 1: Project Resumption
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1. Retrieve most recent project context
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2. Validate context against current codebase
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3. Selectively restore relevant components
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4. Generate resumption summary
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### Workflow 2: Cross-Project Knowledge Transfer
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1. Extract semantic vectors from source project
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2. Map and transfer relevant knowledge
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3. Adapt context to target project's domain
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4. Validate knowledge transferability
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## Usage Examples
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```bash
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# Full context restoration
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context-restore project:ai-assistant --mode full
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# Incremental context update
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context-restore project:web-platform --mode incremental
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# Semantic context query
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context-restore project:ml-pipeline --query "model training strategy"
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```
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## Integration Patterns
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- RAG (Retrieval Augmented Generation) pipelines
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- Multi-agent workflow coordination
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- Continuous learning systems
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- Enterprise knowledge management
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## Future Roadmap
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- Enhanced multi-modal embedding support
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- Quantum-inspired vector search algorithms
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- Self-healing context reconstruction
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- Adaptive learning context strategies |