Why You Should Be Using Hybrid Search in Your RAG Systems

July 17, 2024

As developers working on Retrieval Augmented Generation (RAG) systems, we're constantly seeking ways to improve search accuracy, relevance, and efficiency. One approach that has gained significant traction is hybrid search, which combines the strengths of keyword-based (sparse) and semantic (dense) search methods. This post will dive deep into why hybrid search is superior to traditional dense embedding approaches and why you should seriously consider implementing it in your RAG systems.

The Limitations of Dense Embeddings: A Developer's Perspective

Many of us have relied on traditional dense embedding approaches in our RAG systems due to their ability to capture semantic relationships. However, as our systems scale and requirements become more complex, we've encountered several drawbacks:

High computational cost: Dense embeddings require significant computational resources for both indexing and searching. This translates to higher infrastructure costs and potential bottlenecks in our pipelines ¹.
Large index size: Dense vector indexes can be substantially larger than traditional keyword-based indexes. For instance, OpenSearch benchmarks show that dense encoding indexes can be up to 65.4 GB compared to just 1 GB for BM25 indexes ¹. This increased storage requirement can impact our system's scalability and performance.
Reduced performance on unfamiliar datasets: Dense encoders often struggle when encountering content outside their training domain. This lack of adaptability can be a significant issue when dealing with diverse or specialized content in enterprise settings ¹.
Latency concerns: Dense vector searches typically have higher latency compared to traditional keyword searches. OpenSearch benchmarks show a P50 latency of 56.6 ms for dense searches compared to just 8 ms for BM25 ¹. This difference can be crucial for applications requiring real-time responses.

Enter Hybrid Search: The Best of Both Worlds

Hybrid search addresses these limitations by combining sparse (keyword-based) and dense (semantic) search methods. Let's explore why it's superior and how it can benefit our RAG systems:

1. Improved Relevance: Delivering Better Results

Hybrid search consistently outperforms both pure keyword-based and dense embedding approaches in terms of search relevance. This is crucial for RAG systems where the quality of retrieved information directly impacts the generated output.

According to benchmarks conducted by OpenSearch:

Hybrid search achieved the highest NDCG@10 scores across various datasets, including BEIR and Amazon ESCI ¹.
On average, hybrid search ranked 2.71 out of 5 methods tested, compared to 4.29 for dense embeddings alone ¹.

What does this mean for us as developers? By implementing hybrid search, we can significantly improve the quality of information retrieval in our RAG systems, leading to more accurate and contextually relevant generated content.

2. Efficient Resource Utilization: Optimizing Performance

As developers, we're always looking for ways to optimize resource usage. Hybrid search, particularly when using sparse encoders, offers significant efficiency gains:

Smaller index size: Sparse encoding results in an index size that is only 7.2% to 10.4% of a dense encoding index ¹. This dramatic reduction in index size means we can handle larger datasets with less storage infrastructure.
Reduced RAM usage: Unlike dense encoding, which incurs a 7.9% increase in RAM cost at search time, sparse encoding uses native Lucene indexes with minimal RAM overhead ¹. This efficiency allows us to allocate more resources to other critical parts of our RAG pipeline.
Scalability: The reduced resource requirements of hybrid search make it easier to scale our systems as data volumes grow, without a proportional increase in infrastructure costs.

3. Improved Performance on Diverse Datasets: Handling Real-World Complexity

In real-world applications, we often deal with diverse and domain-specific content. Hybrid search excels in handling this complexity:

Sparse encoders can fall back on keyword-based matching when encountering domain-specific content, ensuring results are at least as good as traditional BM25 ¹.
This adaptability makes hybrid search particularly effective for enterprise knowledge discovery, where data repositories contain diverse types of information ².

For RAG systems dealing with specialized or constantly evolving content, this flexibility is invaluable. It allows our systems to maintain high performance even when faced with unfamiliar terms or concepts.

4. Faster Search Speed: Meeting Real-Time Demands

In many RAG applications, search speed is critical. Hybrid search, especially in document-only mode using sparse encoders, can achieve search latency comparable to BM25:

P50 latency of 10.2ms compared to 8ms for BM25 and 56.6ms for dense embeddings ¹.
Maximum throughput of 1797.9 op/s, nearly matching BM25's 2215.8 op/s and far exceeding dense embedding's 318.5 op/s ¹.

This speed advantage allows us to build more responsive RAG systems, capable of handling real-time queries and generating content with minimal delay.

Implementing Hybrid Search: Practical Considerations

Now that we've established the benefits of hybrid search, let's look at how we can implement it in our RAG systems:

Use pre-trained sparse encoding models: OpenSearch provides models like opensearch-neural-sparse-encoding-v1 for bi-encoder mode and opensearch-neural-sparse-encoding-doc-v1 for document-only mode ¹. These pre-trained models can serve as excellent starting points, allowing us to quickly integrate hybrid search capabilities into our systems.

# Example of registering a sparse encoding model in OpenSearch
POST /_plugins/_ml/models/_register?deploy=true
{
    "name": "opensearch-neural-sparse-encoding",
    "version": "1.0.0",
    "model_format": "TORCH_SCRIPT",
    "function_name": "SPARSE_ENCODING",
    "url": "https://artifacts.opensearch.org/models/ml-models/amazon/neural-sparse/opensearch-neural-sparse-encoding-v1/1.0.1/torch_script/neural-sparse_opensearch-neural-sparse-encoding-v1-1.0.1-torch_script.zip"
}

Leverage AI orchestration: Platforms like Lucidworks offer AI orchestration capabilities to seamlessly integrate various AI models, including those for keyword extraction and content summarization ³. This approach allows us to create more sophisticated hybrid search systems that can adapt to different types of queries and content.

Implement Reciprocal Rank Fusion (RRF): This algorithm combines search scores from multiple ranked results to generate a unified result set, effectively merging sparse and dense vector search results ². RRF can be particularly useful when we need to balance the strengths of different search methods.

# Pseudo-code for implementing RRF
def reciprocal_rank_fusion(rankings, k=60):
    fused_scores = defaultdict(float)
    for ranking in rankings:
        for rank, item in enumerate(ranking, 1):
            fused_scores[item] += 1 / (rank + k)
    return sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)

Fine-tune for your domain: While pre-trained models are a great start, consider fine-tuning them on your specific domain data. This can significantly improve performance for specialized use cases.
Monitor and iterate: Implement logging and monitoring to track the performance of your hybrid search system. Use this data to continuously refine your approach, adjusting the balance between sparse and dense methods as needed.

Conclusion: Embracing the Future of Search in RAG

Hybrid search represents a significant advancement in information retrieval, offering improved relevance, efficiency, and adaptability compared to traditional dense embedding approaches. By combining the precision of keyword search with the contextual understanding of semantic search, hybrid search provides a powerful tool for developers building RAG systems.

As we continue to push the boundaries of what's possible with RAG, embracing hybrid search techniques will be crucial for delivering accurate, efficient, and context-aware results to users across various domains. The ability to handle diverse datasets, reduce computational overhead, and maintain high relevance makes hybrid search an indispensable tool in our development toolkit.

By implementing hybrid search in our RAG systems, we're not just optimizing for today's challenges – we're future-proofing our applications for the evolving landscape of AI-driven content generation and information retrieval.

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