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BytesAgain β€Ί Skills β€Ί Enhanced Search Service
πŸ¦€ ClawHub

Enhanced Search Service

by @whoisme007

Enhances memory search by combining co-occurrence graph analysis and semantic similarity for improved contextual relevance and ranking.

Versionv1.0.0
Downloads466
#seo#legal
TERMINAL
clawhub install enhanced-search-service

πŸ“– About This Skill

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/
β”œβ”€β”€ SKILL.md (this file)
β”œβ”€β”€ scripts/
β”‚   └── enhanced_search_service.py  # Core service implementation
β”œβ”€β”€ integration/
β”‚   └── adapter/
β”‚       └── enhanced_search_adapter.py  # Adapter for star architecture
└── references/
    β”œβ”€β”€ 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.

    πŸ’‘ Examples

    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.

    βš™οΈ 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