Hybrid retrieval affirmed as production baseline (dense + sparse + reranker + graph + KG + vectorless)

Key Questions

What is hybrid retrieval in RAG systems?

Hybrid retrieval combines dense vectors, sparse search (BM25), rerankers, graph, KG, and vectorless methods for optimal recall and speed. Redis RediSearch achieves ≥98% recall with low latency in Python. It serves as a production baseline to curb hallucinations.

How does Redis RediSearch perform in hybrid RAG?

Redis RediSearch with BM25+vector delivers ≥98% recall and is the fastest for low-latency Python apps. Benchmarks compare Zvec vs Qdrant/Milvus. OpenSearch, Azure AI Search, and Vertex AI RAG Engine use hybrids effectively.

What are benefits of pg_search and pgvector in hybrid setups?

pg_search offers 100x performance, ACID WAL, TurboQuant, and 58% TCO savings with EDB VectorChord. Chroma adds BM25+SPLADE, pgvector supports Docling/Java SpringAI for financial PDFs. They enable hybrid no-vecDB chunking like HyDE.

Why use rerankers in production RAG pipelines?

Rerankers like 5 strategies in code improve hybrid search with BM25/semantic/rewrite, boosting context 40-60%. Layered TF-IDF routing PoCs use sklearn/NumPy for agentic retail. They fix issues like HyDE and recursive layout-aware chunking.

What is vectorless RAG and PageIndex?

Vectorless RAG uses PageIndex TOC hybrids without vecDBs or chunking, improving legal recall from 23% to 91%. It employs semantic/recursive methods. Tutorials demonstrate no-chunking setups for efficient retrieval.

How do graph and KG enhance hybrid retrieval?

Graph RAG and KG integration outperform traditional RAG for complex queries. Tools like Weaviate, Haystack, Milvus support agentic multi-tool routing with RRF. Harrier enables multilingual video search.

What databases support hybrid search effectively?

Databases like Chroma (BM25+SPLADE), Weaviate (C# API), Milvus, Haystack, MariaDB, WaveflowDB, SQL FTS excel in hybrids. pgvector and Elastic handle context expansion. Azure RAG and Vertex AI provide managed hybrids.

What chunking strategies work with hybrid RAG?

Strategies include layout-aware, HyDE-fixed, semantic, and recursive chunking, boosting legal recall. No-vecDB PageIndex avoids traditional chunking. Videos explain optimal chunking for production RAG.

Redis RediSearch BM25+vector ≥98% recall fastest Python low-latency; Zvec vs Qdrant/Milvus benchmarks; OpenSearch KubeCon/Azure AI Search/Vertex AI RAG Engine hybrids curb hallucinations; pg_search 100x/ACID WAL/TurboQuant/EDB VectorChord TCO 58%; Chroma BM25+SPLADE/Weaviate C# free API/Milvus/Haystack/MariaDB/WaveflowDB/SQL FTS RRF agentic multi-tool routing BM25/semantic/rewrite/Elastic context 40-60%/PageIndex TOC hybrids no-vecDB/chunking HyDE fixed/semantic/recursive layout-aware legal 23->91%; layered TF-IDF routing PoCs sklearn/NumPy agentic retail/rerankers 5-strats code; Harrier multilingual video search; pgvector Docling/Java SpringAI local financial PDFs; Webcrawlerapi ingestion.

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