Agentic Design

Patterns
🎯

Context Select Patterns(CSEL)

Dynamic retrieval and assembly of relevant context through RAG, semantic search, and intelligent context curation

Complexity: highContext Management

🎯 30-Second Overview

Pattern: Dynamic retrieval and assembly of relevant context through RAG, semantic search, and intelligent context curation

Why: Enables precise context selection from large knowledge bases with optimal relevance and token efficiency

Key Insight: Semantic similarity with intelligent ranking delivers contextually relevant information within token constraints

⚡ Quick Implementation

1Task Analysis:Extract context requirements from task specifications
2Semantic Search:Perform similarity-based context retrieval
3Ranking & Scoring:Apply relevance scoring and prioritization
4Assembly:Dynamically compose selected context components
5Optimization:Optimize for token budget and quality balance
Example: analyze_task → semantic_search → rank_relevance → assemble_context → optimize_budget

📋 Do's & Don'ts

✅Use vector embeddings for semantic similarity matching
✅Implement hybrid search combining semantic + keyword
✅Cache frequently accessed context patterns
✅Use relevance scoring with multiple criteria
✅Implement dynamic context assembly based on task complexity
❌Rely solely on keyword matching for context selection
❌Select context without considering task relevance
❌Ignore token budget constraints during selection
❌Use static context selection for dynamic tasks
❌Skip quality assessment of retrieved context

🚦 When to Use

Use When

  • • Knowledge-intensive applications
  • • Dynamic context curation needs
  • • Enterprise RAG implementations
  • • Intelligent search and retrieval systems

Avoid When

  • • Static context requirements
  • • Simple predefined knowledge bases
  • • High-latency sensitive applications
  • • Limited computational resources

📊 Key Metrics

Relevance Precision
% selected context actually relevant
Context Coverage
% task requirements covered by selection
Selection Speed
Time to complete context assembly
Token Efficiency
Information density per token used
Cache Hit Rate
% contexts served from cache
Quality Score
Human/automated quality assessment

💡 Top Use Cases

Enterprise RAG: analyze_query → search_knowledge_base → rank_documents → assemble_context → optimize_tokens
Research Assistant: identify_topics → semantic_retrieval → source_ranking → context_fusion → knowledge_synthesis
Customer Support: analyze_issue → retrieve_solutions → score_relevance → compose_response → quality_check
Code Assistant: parse_requirements → search_codebase → rank_examples → assemble_context → generate_solution
Content Curation: understand_intent → multi_source_search → relevance_filtering → context_assembly → presentation_optimization

References & Further Reading

Deepen your understanding with these curated resources

Academic Papers

Dense Passage Retrieval for Open-Domain Question Answering (Karpukhin et al., 2020)

ColBERT: Efficient and Effective Passage Search (Khattab & Zaharia, 2020)

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)

Learning Dense Representations for Entity Retrieval (Yamada et al., 2019)

Implementation Guides

Pinecone Vector Database - Semantic Search

Weaviate - Vector Search Engine

Elasticsearch - Search and Analytics Engine

Qdrant - Vector Similarity Search Engine

Tools & Libraries

FAISS - Facebook AI Similarity Search

Annoy - Approximate Nearest Neighbors

Sentence Transformers - Dense Vector Representations

Haystack - End-to-End NLP Framework

Community & Discussions

Vector Database Community

Information Retrieval Community

Semantic Search Best Practices

RAG Implementation Patterns

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