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Context Select Patterns(CSEL)
Dynamic retrieval and assembly of relevant context through RAG, semantic search, and intelligent context curation
In 30 seconds
- What
- Dynamically retrieves and ranks relevant context from multiple sources using semantic search and RAG, then assembles top results for each task.
- When to use
- Large knowledge bases where full context exceeds token limits and relevance varies by task type.
- Watch out
- Retrieved context may miss domain-specific nuance or contradict itself, requiring validation before agent acts on it.
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Context Select Patterns: Overview
Dynamic retrieval and assembly of relevant context through RAG, semantic search, and intelligent context curation
- Dynamic context assembly based on task requirements
- Semantic similarity-based context retrieval
- RAG integration for enterprise knowledge bases
- Intelligent context prioritization and ranking
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References
The papers, specifications, and repositories this pattern is based on.
- Dense Passage Retrieval for Open-Domain Question Answering (Karpukhin et al., 2020)arXiv:2004.04906
- ColBERT: Efficient and Effective Passage Search (Khattab & Zaharia, 2020)arXiv:2004.12832
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)arXiv:2005.11401
- Learning Dense Representations for Entity Retrieval (Yamada et al., 2019)arXiv:1909.10506
- Pinecone Vector Database - Semantic Search
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