Overview
Vector search excels at semantic similarity but performs poorly on exact keyword matches, product codes, or rare terminology. ElasticSearch fills this gap in the AI Data Lakehouse with its proven BM25 retrieval engine — and its native support for hybrid queries that combine sparse and dense signals in a single request.
We also use ElasticSearch as the observability backbone for our agentic systems: agent action logs, tool call traces, and system events are indexed and searchable in real time, supporting both debugging and compliance audit workflows.
Role in the Lakehouse
BM25 Full-Text Retrieval
Classic term-frequency–inverse-document-frequency scoring for keyword-centric search — critical when users query exact product names, legal citations, or regulation identifiers that semantic embeddings may miss.
Hybrid Sparse-Dense Search
ElasticSearch's reciprocal rank fusion (RRF) combines BM25 scores with dense vector scores from ELSER or kNN, delivering retrieval that outperforms either method alone.
Agent Log Analytics
Every agent action, tool call, and LLM response is indexed as a structured document. Kibana dashboards provide real-time visibility; SIEM integrations satisfy federal audit requirements.
Structured Data Index
Metadata records from MinIO objects, entity attributes from Neo4J, and document properties are indexed in ElasticSearch for rapid faceted filtering and aggregation queries.
Hybrid Retrieval Architecture
The query router sends retrieval requests to both ElasticSearch (BM25) and Qdrant (vector) in parallel, then merges ranked results using RRF before returning the top-k context passages to the agent.
- Query expansion — Agent queries are expanded with synonyms and domain-specific terminology before BM25 scoring to improve recall on specialized corpora.
- Result fusion — Reciprocal rank fusion combines BM25 and vector ranks without requiring calibrated scores across different retrieval systems.
- Relevance feedback — Agent tool results feed back into query re-ranking, progressively refining retrieval accuracy across multi-step agent interactions.
Collaborate
Building hybrid retrieval for RAG?
We design hybrid ElasticSearch + Qdrant retrieval pipelines that outperform single-store RAG across enterprise and federal document corpora.
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