Hybrid RAG: Dense and Sparse Retrieval for Better AI Answers

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Quick facts

What it is	Retrieval architecture combining vector (dense) search with keyword (sparse) search
Why it matters	26–31% NDCG improvement over dense-only retrieval on benchmark datasets
Key techniques	Dense (vector/embedding), Sparse (BM25/TF-IDF), Fusion (RRF or weighted alpha)
Platform support	Weaviate, Qdrant, Pinecone, Elasticsearch, pgvector — all ship native hybrid
When to use	Production RAG handling diverse query types: exact terms, concepts, technical IDs

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What is hybrid RAG?

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Hybrid RAG is a retrieval architecture that runs dense vector search and sparse keyword search in parallel, then fuses the ranked results into a single list for the LLM’s context window. It solves the core failure mode of single-method retrieval-augmented generation: dense search misses exact terms; sparse search misses semantics. Hybrid handles both.

The reason hybrid became the production default — not an advanced option — comes down to query reality. Real-world enterprise corpora contain two fundamentally different query types that no single retrieval method handles well:

• Semantic queries — “explain data lineage,” “what does this pipeline do,” “why is my dashboard wrong” — these require understanding meaning, synonyms, and context
• Exact-term queries — “GDPR Article 17 compliance,” “error code ORA-00942,” “customer_id column” — these require precise keyword matching against specific identifiers, codes, and names

Pure dense vector search scores approximately 0.72 NDCG@10 on mixed-query benchmarks across benchmark studies. It is strong on semantic questions, but degrades significantly on product codes, error strings, and identifiers. Pure sparse retrieval (BM25) scores approximately 0.58 NDCG@10 on the same mixed-query corpora. It handles exact terms precisely but fails when vocabulary mismatches occur: “bicycle repair” retrieves nothing when the corpus says “fixing a bike.”

Real query distributions exist on a spectrum, but the key insight is that dense and sparse methods fail on different ends of it. Research shows that reaching recall@1,000 of 0.98 requires both sparse and dense retrieval. Neither method alone achieves this threshold. For most enterprise knowledge bases, where the query distribution spans both conceptual questions and operational identifiers, hybrid is the safe default precisely because you rarely know in advance how much of each query type your users will send.

The platform landscape reflects this consensus: Weaviate, Qdrant, Pinecone, Elasticsearch, and OpenSearch all ship native hybrid semantic search support. When every major platform converges on the same architectural pattern, that pattern has become the practitioner default rather than a theoretical aspiration.

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How hybrid RAG works: dense, sparse, and fusion

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Hybrid RAG runs a dense retrieval pass — encoding the query as an embedding vector and finding nearest neighbors in a vector index — alongside a sparse retrieval pass using BM25 term scoring against an inverted index. The two ranked lists are then fused, typically via Reciprocal Rank Fusion, before the top-k results are passed to the LLM.

Dense retrieval (vector/embedding search)

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A transformer encoder (BERT, BGE, text-embedding-3-large) maps both the query and documents into high-dimensional embedding vectors — typically 768 to 1,536 dimensions. At query time, an approximate nearest neighbor index (HNSW or FAISS) retrieves documents by cosine similarity or dot product.

Dense retrieval’s core strength is semantic understanding. “Automobile” retrieves documents about “cars” and “vehicles” because their embedding vectors are close in space. On open-domain question answering, DPR achieves 75.4% Top-1 accuracy on Natural Questions compared to BM25’s 54.0%. NDCG@10 on mixed benchmarks runs at approximately 0.72.

Dense retrieval’s weaknesses are equally specific. Standard embedding models struggle with exact-match terms: product codes, error messages, version strings, specific identifiers. Late-interaction models like ColBERT partially mitigate this through token-level matching, but at significantly higher computational cost. The computation for standard dense retrieval is intensive: GPU indexing can run 5–10 times more expensive than inverted indexes. When new embedding models are adopted, the entire index requires offline reindexing.

Sparse retrieval (BM25/TF-IDF/keyword)

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Sparse retrieval represents documents as high-dimensional vectors where each dimension corresponds to a vocabulary term. BM25 (Best Match 25) extends TF-IDF with two critical improvements: term frequency saturation (diminishing returns for repeated terms) and document length normalization. Queries are matched via an inverted index that returns results by term overlap.

Sparse retrieval’s strengths are the mirror of dense retrieval’s weaknesses. Exact keyword matching works precisely for statute names, error codes, product identifiers, and technical jargon. Inverted indexes handle billions of documents in single-digit milliseconds. Sparse indexes are significantly more memory-efficient than dense indexes for equivalent corpus sizes, which matters at enterprise scale. BM25 also generalizes zero-shot across domains without domain-specific fine-tuning.

NDCG@10 on mixed-query benchmarks runs at approximately 0.58. Vocabulary mismatch is the dominant failure mode: “how to repair a bicycle” retrieves nothing if the corpus contains “fixing a bike.”

Fusion methods (how scores are combined)

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Reciprocal Rank Fusion (RRF) — the production default. The formula is score(d) = Σ 1/(k + rank_i(d)) where k=60. Each document is scored by its rank position across retriever lists, not by its raw similarity score. Documents appearing high in both ranked lists accumulate the highest fused scores.

RRF wins in practice for one critical reason: it is scale-agnostic. Cosine similarity scores run from 0 to 1; BM25 scores are unbounded. Normalizing across these different scales is non-trivial and often unstable. RRF sidesteps this problem entirely by working only with rank positions. Assembled.com documented switching from weighted score fusion to RRF specifically because varying similarity scores across customer segments made weighted fusion impractical. RRF requires no parameter tuning. k=60 is universal across datasets and domains. As of 2026, RRF is native in Elasticsearch (rrf retriever), OpenSearch, Weaviate (default fusion), and Qdrant (Fusion.RRF).

Weighted alpha fusion uses score(d) = α × dense_score + (1-α) × sparse_score. Weaviate’s default is α=0.75 (75% dense, 25% BM25). LlamaIndex exposes alpha tuning as a first-class feature. The limitation: score normalization between the two systems is required before combining, and normalization is sensitive to query-distribution shifts.

SPLADE uses masked language modeling to generate sparse learned vectors with implicit term expansion. A SPLADE document for “automobile” gets sparse weights assigned to “car,” “vehicle,” and “motor” — combining BM25-style sparsity with semantic awareness. SPLADE has outperformed traditional BM25 on BEIR benchmarks and is used as the sparse component in Qdrant and Pinecone hybrid pipelines.

Learned fusion (Weaviate Hybrid Search 2.0, October 2025) trains a small model on query patterns to predict optimal weights dynamically. It achieves the highest accuracy ceiling but requires labeled training data. The Weaviate 2.0 release introduced a unified index for both vector and keyword search, reducing query overhead and storage costs.

Comparison: dense vs sparse vs hybrid

Aspect	Dense (vector)	Sparse (BM25)	Hybrid (RRF)
Semantic understanding	High	Low	High
Exact term matching	Low	High	High
NDCG@10 (mixed queries)	~0.72*	~0.58*	~0.85*
Recall@1,000	~0.87	~0.82	~0.98
Implementation complexity	Medium	Low	Medium–High
Framework support	Universal	Universal	All major platforms

*Approximate values across mixed-query benchmark analyses; exact figures vary by corpus and query distribution.

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When hybrid RAG outperforms pure retrieval (with benchmarks)

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Hybrid RAG outperforms dense-only retrieval by 26–31% NDCG on standard benchmarks. The gains are largest on mixed corpora — enterprise knowledge bases, technical documentation, domain-specific datasets — where both semantic queries and exact-term queries coexist.

When hybrid wins (specific scenarios):

• Technical domains with specialized jargon. Legal (statute names), medical (ICI codes), financial (ticker symbols), engineering (error strings). BM25 handles exact terms while dense handles conceptual queries. Neither method alone covers the full query distribution.
• Enterprise knowledge bases. Product names, customer IDs, SKUs, pipeline identifiers. Semantic search misses exact matches. Any corpus with operational identifiers needs sparse retrieval.
• Multi-domain corpora. Hybrid covers the full query distribution without domain-specific fine-tuning. This matters for enterprises with mixed data assets across departments and systems.

Benchmark data:

The Blended RAG paper (arXiv:2404.07220) from IBM Research provides the clearest production-grade evidence. Hybrid retrieval achieves NDCG@10 of 0.87 on TREC-COVID — 8.2% above COCO-DR Large — and NDCG@10 of 0.67 on Natural Questions — 5.8% above monoT5-3B. On SQuAD, the F1 score reaches 68.4, which is 50% higher than fine-tuned RAG-end2end at 52.63. These gains are achieved without domain-specific fine-tuning.

BEIR benchmark data (arXiv:2104.08663) shows hybrid models improving nDCG@10 from 43.42 (BM25 alone) to 52.59 — a 9.17-point gain. On MS MARCO recall tasks, weighted fusion of dense and sparse retrieval improves recall by up to 580% compared to single-method approaches on recall-intensive tasks.

Both sparse and dense retrieval are required to reach recall@1,000 of 0.98. Neither method alone achieves this threshold. This is the strongest argument for hybrid as the production baseline: it is not about marginal gains, but about covering the full query space.

When dense-only is acceptable (counter-cases):

The “hybrid as default” claim is a probability argument, not a universal rule. In three specific scenarios, pure dense retrieval can match or approach hybrid performance:

• Semantically homogeneous corpora with well-tuned domain embeddings. When the corpus is narrow (single domain, consistent terminology) and embeddings are fine-tuned on that domain, hybrid’s gains are smaller. One practitioner analysis showed marginal hybrid improvement in this setup.
• Hard latency constraints. The dual pipeline adds retrieval overhead. For mobile or real-time applications where sub-50ms response is required, a single retriever with caching may outperform the dual approach.
• Early-stage prototypes. Hybrid adds operational complexity — two indexes, deduplication logic, fusion tuning. For validating a use case before investing in full infrastructure, pure dense is the faster starting point.

The key qualifier: if you know your query distribution is fully semantic and your corpus has no operational identifiers, pure dense with a reranker may be sufficient. Most enterprise teams do not have this certainty upfront — which is why hybrid is the safe starting point.

The From BM25 to Corrective RAG benchmark survey (arXiv:2604.01733) confirms that hybrid approaches consistently outperform single-method retrieval across diverse text-and-table document tasks. The exceptions are narrow and domain-specific; they do not invalidate hybrid as the default for mixed-query enterprise environments. Teams building on the enterprise context layer consistently find that hybrid retrieval is the minimum viable retrieval architecture before metadata enrichment is applied.

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How to implement hybrid RAG

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The benchmark evidence makes the case for hybrid. The implementation question is whether your corpus and query distribution fall into the “use hybrid” category — which, for most enterprise knowledge bases, they do. If you have a mix of conceptual and exact-term queries, or if you cannot characterize your query distribution reliably, hybrid is the right architecture to implement.

Implementing hybrid RAG requires configuring a vector database with native hybrid support, setting up both a dense index (embeddings) and a sparse index (BM25 or SPLADE), choosing a fusion method (RRF is the safe default), and evaluating the full pipeline on a representative query set before production deployment.

Prerequisites checklist:

• Vector DB with native hybrid support (Weaviate, Qdrant, Pinecone, Elasticsearch)
• Embedding model for dense vectors (BGE, text-embedding-3-large, or domain-tuned)
• Tokenization pipeline for BM25, or SPLADE for learned sparse representations
• Evaluation dataset: a representative sample of your actual query distribution

Step-by-step implementation:

1. Choose a vector DB with hybrid support. Prefer one where hybrid is native, not bolted on. Weaviate and Qdrant are purpose-built for this. Elasticsearch works well for teams already on Elastic who want to extend an existing deployment.

2. Configure your sparse index. BM25 is supported everywhere and is the sensible starting point. Consider SPLADE if you need exact-term coverage with implicit semantic expansion, without manually tuning alpha. Ensure proper tokenization for your domain’s specific terminology.

3. Set fusion method and parameters. Start with RRF (k=60). It requires no score normalization, no parameter tuning, and performs consistently across domains. If you need weighted fusion, normalize dense and sparse scores before combining — never combine raw cosine similarity with raw BM25 scores directly.

4. Tune the RRF k constant or alpha. Use a representative query set from your actual workload. LlamaIndex exposes alpha tuning as a first-class feature. As a rule of thumb, α=0.75 (Weaviate’s default) works well for most mixed-query corpora.

5. Evaluate on precision, recall, and latency. Target Recall@10 and NDCG@10 as primary quality metrics. Track the latency overhead of dual retrieval compared to single-method. The HyPA-RAG framework (arXiv:2409.09046) provides an adaptive approach for domain-specific applications where query types shift across different user segments.

Common pitfalls:

• Score normalization without calibration. Raw cosine + raw BM25 produces unstable rankings because the scores live on different scales. Use RRF to sidestep the problem entirely.
• Blind alpha at 0.5. Equal weighting is rarely optimal. Tune on a representative query sample — a 75/25 dense-to-sparse split is a better starting point than 50/50.
• Skipping deduplication. When both retrievers return the same document, merge before passing to the LLM. Duplicate documents waste context window tokens and dilute the evidence available to the model.
• Treating retrieval as the full solution. Hybrid improves recall, but ranking instability remains. A cross-encoder reranker is the recommended third stage in any production RAG pipeline. Hybrid retrieval surfaces the evidence; the reranker orders it correctly.

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Hybrid RAG platforms and tools

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Every major vector database now ships native hybrid retrieval. The implementation details differ — fusion mode, sparse vector type, tuning interface — but RRF is available across all primary platforms. The choice of platform depends on operational fit, not hybrid search capability gaps. None of the major platforms require you to build fusion logic from scratch.

Platform comparison:

Platform	Hybrid implementation	Fusion options	Notes
Weaviate	BM25F + dense vector; native hybrid query API	RRF (default), Relative Score Fusion, Learned Fusion (v2.0)	Hybrid Search 2.0 (Oct 2025): unified index for vector and keyword search
Qdrant	Native sparse vectors (sparse_vectors field) + dense	Fusion.RRF (first-class); weighted	SPLADE sparse vectors supported
Elasticsearch	BM25 full-text + kNN vector search	Native rrf retriever; score-based	Battle-tested at scale; incumbent for enterprises already on Elastic
Pinecone	Single hybrid index (sparse-dense co-located)	Sparse-dense dot product	Managed service, minimal ops; SPLADE sparse vectors
pgvector	tsvector full-text + vector similarity in SQL	Custom fusion logic (no native RRF)	Best for PostgreSQL teams avoiding a separate vector DB
Milvus/Zilliz	Sparse + dense vectors in one collection	Native hybrid query APIs	Strong for on-premise deployments

Framework support:

• LangChain — EnsembleRetriever combines BM25Retriever and VectorStoreRetriever with configurable weights. RRF fusion logic is built in. Good for teams already using LangChain who want minimal additional abstraction.
• LlamaIndex — QueryFusionRetriever with first-class alpha parameter for BM25 versus semantic weighting. Integrates with all major platforms and provides the most granular hybrid tuning interface available in any framework.
• RAGatouille — ColBERT late-interaction model for token-level matching that achieves hybrid-like dense retrieval. Persists indices on disk for production deployment.

AWS OpenSearch provides a documented production implementation pattern for teams using Amazon services.

For teams deciding between retrieval architectures more broadly, AI memory vs RAG covers the full architectural space before committing to a stack.

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How Atlan’s governed metadata improves hybrid RAG accuracy

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Hybrid RAG solves how to retrieve. The ceiling on retrieval accuracy is set by the quality of the corpus it searches.

In enterprise environments, that corpus is not a clean document set. It is tables, dashboards, pipelines, and reports — each with ownership, certification status, business glossary mappings, and lineage relationships that live outside the text content. Hybrid retrieval over raw text chunks, even with RRF fusion, misses these critical signals. Both the dense embedding and the sparse keyword index operate on impoverished representations when the content lacks structured context.

The Atlan contribution to hybrid retrieval:

1. Metadata-enriched embeddings. Embedding a table’s description alongside its business glossary terms, ownership tags, and certification status produces higher-quality dense vectors than embedding schema text alone. Adding this metadata context boosts retrieval accuracy from 33% to 55% — a 22-point lift that comes entirely from enriching what is indexed, not from changing the retrieval architecture.

2. Structured signals for the sparse component. Business term names, column names, and data asset tags are exact-match targets that BM25 excels at finding. Atlan’s governed catalog surfaces these reliably at indexing time, giving the sparse retriever the precise vocabulary it needs to match operational queries.

3. Lineage-aware retrieval. Atlan knows when an upstream source is stale. When an agent retrieves a dashboard that depends on a broken pipeline, lineage context changes what the agent should prioritize — even when embedding similarity is high. This is a form of context engineering for AI agents that purely technical retrieval architectures cannot replicate.

4. Access governance at retrieval time. Atlan’s MCP server enforces permissions at the metadata layer. Only authorized context reaches the agent’s context window. This is not a security overlay — it is retrieval-time governance that prevents agents from surfacing data their users are not authorized to see.

The framing: hybrid RAG is the retrieval architecture. Atlan is the knowledge foundation that determines how high the ceiling of that architecture actually sits. The progression is not naive RAG to hybrid RAG to something exotic. It is naive RAG to hybrid RAG to governed hybrid RAG with metadata enrichment — and each step is a meaningful, measurable improvement.

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Real stories from real customers: Governed retrieval at scale

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Hybrid search gets you to the data. Governed context makes it trustworthy.

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Hybrid RAG is the right starting point for any production retrieval system. The benchmarks are clear: 26–31% NDCG improvement, recall@1,000 reaching 0.98, F1 rising 50% on SQuAD versus fine-tuned baselines. These are not marginal gains from an optional optimization. They are the expected results of adopting the minimum viable retrieval architecture for mixed-query corpora — which describes almost every enterprise knowledge base.

The argument for hybrid is not that it is better than pure dense retrieval in some abstract sense. It is that pure dense retrieval fails precisely on the queries that matter most in production: specific identifiers, technical terms, policy document titles, and compliance codes. BM25 handles exactly those cases. Running both and fusing with RRF covers the full query space without domain-specific fine-tuning.

What hybrid retrieval does not solve is corpus quality. When the underlying data assets lack ownership context, certification status, lineage relationships, and governed business vocabulary, both the dense and sparse components index impoverished representations. Adding structured metadata before embedding — the approach Atlan takes through its context graph architecture — pushes retrieval accuracy from 33% to 55%. That 22-point gain is not an incremental improvement. It is the difference between an AI system that retrieves the right evidence and one that retrieves plausible-looking evidence that happens to be stale, uncertified, or outside the user’s access scope.

The progression from naive RAG to hybrid RAG to governed hybrid RAG is the path most enterprise teams will take. Hybrid is the retrieval architecture. Governed metadata is what makes it trustworthy at enterprise scale.

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FAQs about hybrid RAG

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What is the difference between dense and sparse retrieval in RAG?

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Dense retrieval encodes queries and documents as high-dimensional embedding vectors and finds matches by semantic similarity — it understands that “automobile” and “car” are related. Sparse retrieval (BM25) matches exact keywords via an inverted index — it handles product codes, error strings, and proper nouns precisely. Hybrid RAG runs both and fuses the results for comprehensive coverage.

When should I use hybrid search instead of semantic search alone?

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Use hybrid search when your corpus contains a mix of semantic queries (conceptual questions) and exact-term queries (IDs, codes, names, technical jargon). In practice, almost every enterprise knowledge base contains both. Pure semantic search fails on exact-match lookups; pure keyword search fails on conceptual questions. If you are unsure, hybrid is the safe default.

What is reciprocal rank fusion (RRF) and how does it work?

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Reciprocal Rank Fusion (RRF) combines ranked lists from multiple retrievers using the formula: score(d) = sum of 1/(k + rank), where k=60. Each document’s score is based on its rank position — not its raw similarity score — making RRF scale-agnostic. Documents appearing high in both lists accumulate the highest fused scores. No normalization between cosine similarity and BM25 scores is required.

Does hybrid RAG always outperform vector-only search?

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Not always. On semantically homogeneous corpora with well-tuned, domain-specific embedding models, pure dense retrieval can approach hybrid performance. Hybrid’s advantages are largest on mixed corpora where exact-term queries are common. If your query distribution is nearly all semantic — and your corpus lacks technical IDs or proper nouns — pure dense with a cross-encoder reranker may be sufficient.

What vector databases support hybrid search natively?

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Weaviate, Qdrant, Pinecone, Elasticsearch, and Amazon OpenSearch all support hybrid search natively as of 2026. Weaviate and Qdrant offer RRF as a first-class fusion mode. Elasticsearch ships a native rrf retriever. Pinecone supports sparse-dense co-located indexes. pgvector can approximate hybrid search using PostgreSQL full-text search alongside vector similarity, though fusion logic must be implemented manually.

Is hybrid RAG more expensive than dense-only RAG?

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Hybrid RAG requires two indexes — a vector index for dense retrieval and an inverted index for sparse retrieval — which adds storage and query latency overhead compared to single-method retrieval. Sparse indexes are significantly more memory-efficient than dense indexes for equivalent corpus sizes, which partially offsets the storage increase. For most production use cases, the accuracy gains justify the overhead.

What is SPLADE and how does it differ from BM25?

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SPLADE (Sparse Learned Representation for Dense Retrieval Acceleration) uses masked language modeling to generate sparse vectors with implicit term expansion. Unlike BM25, which relies purely on exact vocabulary overlap, SPLADE assigns sparse weights to semantically related terms — so “automobile” gets weights on “car,” “vehicle,” and “motor.” SPLADE achieves higher recall on BEIR benchmarks than BM25 while retaining the memory efficiency and speed of sparse indexing.

How does hybrid retrieval reduce hallucinations in AI answers?

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Hybrid retrieval reduces hallucinations by improving the probability that the relevant evidence actually appears in the LLM’s context window. Dense-only retrieval fails on exact-term queries, meaning the LLM may never see the specific document that contains the correct answer and generates a plausible-sounding substitute instead. By combining dense and sparse retrieval, hybrid RAG provides higher recall across both semantic and exact-match query types, giving the LLM better evidence to cite rather than synthesize.

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Sources

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1. Blended RAG: Improving RAG Accuracy with Semantic Search and Hybrid Query-Based Retrievers, arXiv

2. From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documents, arXiv

3. BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models, arXiv

4. Hybrid Dense-Sparse Retrieval for High-Recall Information Retrieval, ResearchGate

5. RAG-Fusion: A New Take on Retrieval-Augmented Generation, arXiv

6. HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System, arXiv

7. Integrate Sparse and Dense Vectors to Enhance Knowledge Retrieval in RAG Using Amazon OpenSearch Service, AWS

8. Weaviate Hybrid Search 2.0, ailog.fr

9. Hybrid Search Explained, Weaviate

10. Qdrant Hybrid Queries, Qdrant