Elasticsearch Relevance Engine (ESRE)
Build advanced relevance with semantic search for AI applications.
Overview
The Elasticsearch Relevance Engine (ESRE) is a collection of features within the Elastic Stack designed to power modern AI search applications. It combines traditional keyword search (BM25) with dense vector search and a proprietary sparse vector model (ELSER) for semantic search. ESRE provides all the necessary tools for building end-to-end RAG pipelines, from data ingestion and enrichment to hybrid retrieval and integration with LLMs.
✨ Key Features
- Hybrid search (keyword, dense vector, sparse vector)
- Elastic Learned Sparse Encoder (ELSER) for semantic search
- Vector database capabilities
- Reciprocal Rank Fusion (RRF) for result blending
- Integration with third-party LLMs (e.g., OpenAI)
- Scalable and battle-tested infrastructure
🎯 Key Differentiators
- Unified platform for keyword, vector, and semantic search
- Proprietary ELSER model for out-of-the-box semantic search
- Mature, scalable, and widely adopted platform
Unique Value: Provides a single, unified platform for all types of search—keyword, vector, and semantic—enabling organizations to build powerful RAG applications without stitching together multiple tools.
🎯 Use Cases (4)
✅ Best For
- Log analysis for threat detection
- Product search for large retailers
- Internal corporate document search
💡 Check With Vendor
Verify these considerations match your specific requirements:
- Teams looking for a simple, managed vector-only database
🏆 Alternatives
Unlike pure vector databases, Elasticsearch offers a mature, battle-tested solution for hybrid search, which is often more effective for RAG than vector search alone.
💻 Platforms
✅ Offline Mode Available
🔌 Integrations
🛟 Support Options
- ✓ Email Support
- ✓ Phone Support
- ✓ Dedicated Support (Platinum and above tier)
🔒 Compliance & Security
💰 Pricing
✓ 14-day free trial
Free tier: Free Basic tier with core features.
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