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 notes

Configuration

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: 0.1

Integration 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.