Try semantic search with the Amazon OpenSearch Service vector engine
Amazon OpenSearch Service has long supported both lexical and vector search, since the introduction of its kNN plugin in 2020. With recent developments in generative AI, including AWS’s launch of Amazon Bedrock earlier in 2023, you can now use Amazon Bedrock-hosted models in conjunction with the vector database capabilities of OpenSearch Service, allowing you to implement semantic search, retrieval augmented generation (RAG), recommendation engines, and rich media search based on high-quality vector search. The recent launch of the vector engine for Amazon OpenSearch Serverless makes it even easier to deploy such solutions.
OpenSearch Service supports a variety of search and relevance ranking techniques. Lexical search looks for words in the documents that appear in the queries. Semantic search, supported by vector embeddings, embeds documents and queries into a semantic high-dimension vector space where texts with related meanings are nearby in the vector space and therefore semantically similar, so that it returns similar items even if they don’t share any words with the query.
We’ve put together two demos on the public OpenSearch Playground to show you the strengths and weaknesses of the different techniques: one comparing textual vector search to lexical search, the other comparing cross-modal textual and image search to textual vector search. With OpenSearch’s Search Comparison Tool, you can compare the different approaches. For the demo, we’re using the Amazon Titan foundation model hosted on Amazon Bedrock for embeddings, with no fine tuning. The dataset consists of a selection of Amazon clothing, jewelry, and outdoor products.
A search engine is a special kind of database, allowing you to store documents and data and then run queries to retrieve the most relevant ones. End-user search queries usually consist of text entered in a search box. Two important techniques for using that text are lexical search and semantic search. In lexical search, the search engine compares the words in the search query to the words in the documents, matching word for word. Only items that have all or most of the words the user typed match the query. In semantic search, the search engine uses a machine learning (ML) model to encode text from the source documents as a dense vector in a high-dimensional vector space; this is also called embedding the text into the vector space. It similarly codes the query as a vector and then uses a distance metric to find nearby vectors in the multi-dimensional space. The algorithm for finding nearby vectors is called kNN (k Nearest Neighbors). Semantic search does not match individual query terms—it finds documents whose vector embedding is near the query’s embedding in the vector space and therefore semantically similar to the query, so the user can retrieve items that don’t have any of the words that were in the query, even though the items are highly relevant.
Textual vector search
The demo of textual vector search shows how vector embeddings can capture the context of your query beyond just the words that compose it.
In the text box at the top, enter the query tennis clothes. On the left (Query 1), there’s an OpenSearch DSL (Domain Specific Language for queries) semantic query using the
amazon_products_text_embedding index, and on the right (Query 2), there’s a simple lexical query using the
amazon_products_text index. You’ll see that lexical search doesn’t know that clothes can be tops, shorts, dresses, and so on, but semantic search does.
Similarly, in a search for warm-weather hat, the semantic results find lots of hats suitable for warm weather, whereas the lexical search returns results mentioning the words “warm” and “hat,” all of which are warm hats suitable for cold weather, not warm-weather hats. Similarly, if you’re looking for long dresses with long sleeves, you might search for long long-sleeved dress. A lexical search ends up finding some short dresses with long sleeves and even a child’s dress shirt because the word “dress” appears in the description, whereas the semantic search finds much more relevant results: mostly long dresses with long sleeves, with a couple of errors.
Cross-modal image search
The demo of cross-modal textual and image search shows searching for images using textual descriptions. This works by finding images that are related to your textual descriptions using a pre-production multi-modal embedding. We’ll compare searching for visual similarity (on the left) and textual similarity (on the right). In some cases, we get very similar results.
For example, sailboat shoes does a good job with both approaches, but white sailboat shoes does much better using visual similarity. The query canoe finds mostly canoes using visual similarity—which is probably what a user would expect—but a mixture of canoes and canoe accessories such as paddles using textual similarity.
If you are interested in exploring the multi-modal model, please reach out to your AWS specialist.
Building production-quality search experiences with semantic search
These demos give you an idea of the capabilities of vector-based semantic vs. word-based lexical search and what can be accomplished by utilizing the vector engine for OpenSearch Serverless to build your search experiences. Of course, production-quality search experiences use many more techniques to improve results. In particular, our experimentation shows that hybrid search, combining lexical and vector approaches, typically results in a 15% improvement in search result quality over lexical or vector search alone on industry-standard test sets, as measured by the NDCG@10 metric (Normalized Discounted Cumulative Gain in the first 10 results). The improvement is because lexical outperforms vector for very specific names of things, and semantic works better for broader queries. For example, in the semantic vs. lexical comparison, the query saranac 146, a brand of canoe, works very well in lexical search, whereas semantic search doesn’t return relevant results. This demonstrates why the combination of semantic and lexical search provides superior results.
OpenSearch Service includes a vector engine that supports semantic search as well as classic lexical search. The examples shown in the demo pages show the strengths and weaknesses of different techniques. You can use the Search Comparison Tool on your own data in OpenSearch 2.9 or higher.
For further information about OpenSearch’s semantic search capabilities, see the following:
- Amazon OpenSearch Service’s vector database capabilities explained
- Building a semantic search engine in OpenSearch
- The ABCs of semantic search in OpenSearch: Architectures, benchmarks, and combination strategies
- Using OpenSearch as a Vector Database
- Partner Highlight: Using OpenSearch to set up a hybrid system that uses text and vector search
- Documentation for Neural Search
About the author
Stavros Macrakis is a Senior Technical Product Manager on the OpenSearch project of Amazon Web Services. He is passionate about giving customers the tools to improve the quality of their search results.