a5c-ai Semantic Similarity Skill
Overview
The Semantic Similarity skill provides advanced capabilities for computing and leveraging semantic relationships between content in knowledge management systems. Using modern embedding models and vector similarity techniques, this skill enables intelligent content discovery, recommendation, and organization beyond traditional keyword matching.
Capabilities
Document Embedding Generation
- Generate embeddings for documents and content
- Configure embedding models (OpenAI, Cohere, open-source)
- Implement batch embedding pipelines
- Manage embedding storage and retrieval
- Optimize embedding dimensions for use case
Sentence Transformer Models
- Configure sentence-transformers models
- Fine-tune models for domain-specific content
- Implement multi-lingual embedding models
- Design model selection strategies
Similarity Search and Clustering
- Implement vector similarity search (cosine, dot product)
- Configure approximate nearest neighbor (ANN) algorithms
- Design content clustering pipelines
- Implement hierarchical clustering for organization
Related Content Recommendation
- Build content recommendation systems
- Configure "More Like This" functionality
- Implement collaborative filtering with embeddings
- Design hybrid recommendation approaches
Duplicate Detection
- Identify duplicate and near-duplicate content
- Configure similarity thresholds for detection
- Implement deduplication workflows
- Design merge and consolidation strategies
Topic Modeling
- Implement LDA (Latent Dirichlet Allocation)
- Configure BERTopic for modern topic modeling
- Design topic hierarchies and taxonomies
- Enable dynamic topic tracking
Semantic Search Integration
- Configure semantic search pipelines
- Implement hybrid search (keyword + semantic)
- Design query expansion using embeddings
- Enable cross-lingual semantic search
Content Gap Analysis
- Identify missing content through similarity analysis
- Map content coverage using embeddings
- Detect underserved topics and areas
- Design content planning recommendations
Concept Extraction
- Extract key concepts from documents
- Build concept graphs from embeddings
- Implement keyphrase extraction
- Design concept tagging pipelines
Dependencies
- Sentence-transformers library
- OpenAI Embeddings API
- Cohere Embed API
- Pinecone vector database
- Weaviate
- Milvus
- FAISS (Facebook AI Similarity Search)
- scikit-learn for clustering
Process Integration
This skill integrates with:
- search-optimization.js: Semantic search and related content features
- knowledge-base-content.js: Content recommendations and gap analysis
- tacit-to-explicit-conversion.js: Knowledge representation and concept extraction
Usage
Generate Document Embeddings
task: Generate embeddings for knowledge base content
skill: semantic-similarity
parameters:
source: knowledge-base
model: text-embedding-3-small
batch_size: 100
output: vector-store
dimensions: 1536
Configure Similarity Search
task: Set up semantic similarity search
skill: semantic-similarity
parameters:
vector_store: pinecone
index_name: kb-embeddings
similarity_metric: cosine
top_k: 10
hybrid_search: true
keyword_weight: 0.3
Duplicate Detection
task: Identify duplicate content
skill: semantic-similarity
parameters:
threshold: 0.92
scope: all-documents
output: duplicate-report.json
action: flag_for_review
Topic Modeling
task: Generate topic model for knowledge base
skill: semantic-similarity
parameters:
method: bertopic
min_topic_size: 10
nr_topics: auto
output: topic-model
visualizations: true
Best Practices
- Choose appropriate embedding models - Match model to content type and language
- Normalize embeddings - Ensure consistent similarity scores across documents
- Set appropriate thresholds - Tune similarity thresholds for your use case
- Implement hybrid search - Combine semantic and keyword search for best results
- Monitor embedding drift - Re-embed content periodically as models improve
- Consider latency - Cache frequently used embeddings for performance
- Plan for scale - Use ANN indexes for large document collections
- Handle long documents - Implement chunking strategies for lengthy content
Architecture Patterns
Basic Semantic Search Pipeline
Document -> Chunking -> Embedding -> Vector Store -> Query -> Results
Hybrid Search Architecture
Query -> [Keyword Search] -> Results
-> [Semantic Search] -> Results
-> [Reranking] -> Final Results
Recommendation Pipeline
User Context -> Find Similar Content -> Filter by Metadata -> Personalize -> Recommend
Metrics
Key metrics for semantic similarity systems:
| Metric | Description | Target |
|---|---|---|
| Retrieval Precision | Relevant results in top-k | > 80% |
| Search Latency | Time for similarity search | < 200ms |
| Duplicate Detection F1 | Accuracy of duplicate finding | > 90% |
| Topic Coherence | Quality of topic models | > 0.5 |
| User Satisfaction | Relevance ratings | > 4.0/5.0 |
Related Skills
- knowledge-graph (SK-008): Graph-based semantic relationships
- search-engine (SK-005): Enterprise search integration
- content-curation (SK-010): Quality-based content management
Related Agents
- kg-specialist (AG-008): Knowledge graph and semantic expertise
- search-expert (AG-004): Search optimization guidance
- knowledge-architect (AG-001): Overall KM strategy alignment