Mastering Hybrid Search in OpenSearch 3.3: The Best of Both Worlds

In the world of search, we have long been torn between two powerful approaches. On one side, we have the trusty keyword search (lexical search), the backbone of search engines for decades. It is precise, exact, and great at finding specific terms. On the other side, we have the rising star, semantic search (vector search). It understands context, intent, and the "vibe" of a query, even if the exact words do not match.

For a long time, you had to choose. But why settle? With OpenSearch hybrid search, you can have your cake and eat it too. And with the latest updates in OpenSearch 3.3, this combination is faster and more powerful than ever.

What is Hybrid Search?

Hybrid search is not just a buzzword; it is a practical architecture that combines the precision of lexical search with the understanding of semantic search.

Imagine a user searches for "warm winter coat."

• Lexical search looks for the exact words "warm," "winter," and "coat." It might miss a "thermal parka" because the words do not match.
• Semantic search understands that a "thermal parka" is a type of "warm winter coat" and retrieves it.

By combining these two, you get results that are both keyword-accurate and contextually relevant. This leads to a significantly better user experience and higher conversion rates.

How It Works Under the Hood

Implementing hybrid search involves a few key steps:

1. Ingestion: You index your data into OpenSearch. During this process, you use an ingest pipeline to generate vector embeddings for your text fields. These embeddings are mathematical representations of the text's meaning.
2. Querying: When a user searches, you run two queries simultaneously: a standard BM25 query (lexical) and a k-NN query (semantic).
3. Score Normalization: This is where the magic happens. Lexical queries and vector queries produce scores on vastly different scales. A BM25 score might be 15.2, while a cosine similarity score from a vector search is between 0 and 1. You cannot just add them up. You need to normalize them.
4. Combination: Once normalized, the scores are combined (often using a weighted average) to produce a final ranking.

New in OpenSearch 3.3: Performance and Precision

The OpenSearch 3.3 release (and the 3.x series in general) has brought massive improvements to this workflow.

1. Enhanced Score Normalization and Ranking

OpenSearch now offers more robust score normalization techniques. The normalization-processor can standardize scores using methods like Min-Max or L2 normalization.

Even more exciting is the introduction of the Score Ranker processor. This allows you to use sophisticated rank fusion techniques to combine your results. Instead of simple math, you can use algorithms that respect the relative ranking of documents from each stream, ensuring the "best" results truly float to the top.

2. Performance Gains

Hybrid search can be computationally expensive. You are running two heavy queries instead of one. However, OpenSearch 3.3 includes significant optimizations. Benchmarks show up to a 20% improvement in latency for lexical sub-queries within a hybrid context. This means you can deliver that superior relevance without sacrificing the snappy speed your users expect.

Implementing Hybrid Search

Here is a simplified example of how you might structure a hybrid query in OpenSearch:

POST /my-index/_search?search_pipeline=hybrid_search_pipeline
{
  "query": {
    "hybrid": {
      "queries": [
        {
          "match": {
            "text": {
              "query": "warm winter coat"
            }
          }
        },
        {
          "neural": {
            "embedding": {
              "query_text": "warm winter coat",
              "model_id": "your_embedding_model",
              "k": 10
            }
          }
        }
      ]
    }
  }
}

In this setup, the hybrid_search_pipeline handles the normalization and combination logic, keeping your application code clean.

Conclusion

Hybrid search represents the future of search experiences. It bridges the gap between what users say and what they mean. With the performance boosts and advanced ranking capabilities in OpenSearch 3.3, there has never been a better time to upgrade your search strategy.

Ready to implement hybrid search? Check out our OpenSearch Consulting services to get expert help.