Enhanced Search Service
Description
Provides enhanced memory search by combining co-occurrence graph analysis and semantic vector similarity. This plugin sits between memory storage and query interfaces, offering improved relevance ranking through contextual relationships and semantic understanding.
Core Capabilities
- Unified Search: Combines co-occurrence graph expansion with semantic vector similarity
- Relevance Ranking: Multi-factor scoring (text match, co-occurrence strength, semantic similarity)
- Context Awareness: Leverages memory relationships to surface relevant but non-obvious connections
- Plugin Architecture: Independent service that can be upgraded/replaced without affecting other components
Dependencies
- Co-occurrence Engine (
co-occurrence-engine): Provides relationship graph for contextual expansion - Semantic Vector Store (
semantic-vector-store): Provides semantic similarity scoring - Memory Integration (
memory-integration): Optional, for direct memory access if needed
Usage
As a Plugin User
from enhanced_search_adapter import EnhancedSearchAdapter
adapter = EnhancedSearchAdapter()
results = adapter.enhance_search("query about memory sync", max_results=10)As a System Integrator
The plugin provides an adapter that implements the standard memory adapter interface with additional enhancement methods.
Skill Files
enhanced-search-service/
├── SKILL.md (this file)
├── scripts/
│ └── enhanced_search_service.py # Core service implementation
├── integration/
│ └── adapter/
│ └── enhanced_search_adapter.py # Adapter for star architecture
└── references/
├── api.md # API documentation
└── architecture.md # Design and integration notesConfiguration
Default configuration (can be overridden via adapter initialization):
search:
co_occurrence_weight: 0.3
semantic_weight: 0.5
text_match_weight: 0.2
max_expansion: 5
min_relevance_threshold: 0.1Integration with Star Architecture
This plugin connects to the Memory Sync Enhanced (MSE) hub through its adapter. It consumes data from:
- Co-occurrence engine (for relationship data)
- Semantic vector store (for similarity data)
It produces enhanced search results for:
- Memory Integration system
- Direct user queries
- Other plugins needing sophisticated search
Health Checks
The adapter provides health monitoring for:
- Dependency availability (co-occurrence engine, semantic vector store)
- Search performance metrics
- Result quality indicators
Version History
- v0.1.0 (initial): Basic enhancement combining co-occurrence scores with semantic similarity
- v0.2.0 (planned): Advanced fusion algorithms and caching
- v0.3.0 (planned): Learning-based weighting adaptation
Development Notes
This is a Phase 3 split from the original memory-integration plugin. The goal is to create a single-function plugin focused solely on search enhancement, following the star architecture principle of separation of concerns.