Beyond keyword search: A guide to AI-powered search in OpenSearch

When building real-world search experiences such as e-commerce catalogs, enterprise search, and documentation portals, you quickly run into the limits of keyword search. Search for “car rental” and you will miss documents that say “vehicle hire.” Traditional engines match on exact terms; they have no concept of meaning, only vocabulary overlap.

OpenSearch AI Search addresses this by building a comprehensive suite of AI-powered search methods directly into the platform, so you do not need a separate vector database or custom middleware to get started.

This article walks through each AI search method OpenSearch offers, what makes it unique, and when to use it.

What Is AI Search in OpenSearch?

AI search in OpenSearch is built on vector embeddings to help search engines understand meaning instead of relying only on exact keyword matches. A machine learning model converts your text into a numerical representation that captures meaning, so documents that are conceptually similar end up numerically close, regardless of the specific words used.

OpenSearch automates this process end to end. At index time, your documents are converted to embeddings automatically. At query time, your search text is converted the same way and matched against the closest documents in your index. This is why AI search finds “vehicle hire” when you search “car rental,” and why it is the practical answer to keyword search’s biggest blind spot.

Before using any AI search method, you need to connect an ML model. OpenSearch gives you three options:

• OpenSearch pretrained models – ready to use out of the box
• Custom local models – upload and host your own model inside OpenSearch
• Externally hosted models – connect to Amazon Bedrock, OpenAI, Cohere, or similar services via a connector

Once your model is connected, you choose your search method. OpenSearch offers six.

Method 1: Semantic search for OpenSearch Vector Search

The core idea: Semantic search finds documents that mean the same thing as your query, not just documents that share the same words.

A user searching “affordable places to stay” should find results mentioning “budget hotel” and “inexpensive accommodation” even though none of those words appeared in the query. Semantic search makes that possible using dense retrieval over a k-NN vector index.

OpenSearch also provides a simplified setup path called the semantic field type. You declare a field as semantic in your index mapping, point it at a model, and OpenSearch handles everything automatically, with no ingest pipeline configuration needed.

When to use it: Knowledge bases, documentation search, job listings, product catalogs, anywhere users search in natural language and keyword matching falls short.

Method 2: Hybrid search for AI powered search in OpenSearch

The core idea: Hybrid search combines keyword search and semantic search to get the best of both.

Neither approach is complete on its own. Keyword search is precise for specific terms, product codes, and names, but misses synonyms and paraphrases. Semantic search understands meaning but can surface results that are merely thematically related rather than genuinely relevant. Hybrid search runs both at once and blends the results.

OpenSearch offers two strategies for combining scores:

• Normalization processor – normalizes scores from each query to a common scale and combines them with configurable weights, so you control how much each approach contributes.
• Score ranker processor – uses rank fusion instead of raw scores, which is more stable when the two queries produce very differently scaled results.

When to use it: This is a sensible default for most production search experiences. It handles both specific queries (“Nike Air Max 270 size 10”) and conceptual queries (“comfortable running shoes for flat feet”) in the same search box.

Method 3: Neural sparse search

The core idea: Neural sparse search delivers semantic relevance at the resource cost of traditional keyword search.

Dense semantic search is powerful but resource-intensive. Storing and searching full high-dimensional vectors at scale requires significant memory and compute. Neural sparse search offers a practical middle ground.

Instead of a dense vector, a sparse model produces a compact token-weight map, a short list of the most significant tokens and their relevance scores. This representation is stored and searched using an inverted index, making it nearly as computationally efficient as BM25 while still capturing semantic meaning. For large deployments, OpenSearch supports a two-phase search pipeline that first narrows down candidates quickly and then rescores only the top results, improving throughput without sacrificing relevance.

When to use it: When memory and compute costs are a constraint, or when you are scaling semantic search to a large corpus and need more predictable resource usage.

Method 5: Multimodal Search

The core idea: Multimodal search lets you search across both text and images using a single unified vector space.

Multimodal embedding models understand both text and images and project them into the same embedding space. This means you can search with a text query and return matching images, search with an image and return related text, or combine both in a single query for the most targeted results.

OpenSearch supports multimodal embeddings from Amazon Bedrock Titan Multimodal and Cohere multimodal models. At index time, the text and image fields are processed together into a single combined vector. At search time, your query, whether text, image, or both, is matched against those vectors.

When to use it: E-commerce product search, media asset management, fashion retail, and any domain where your data pairs descriptions with images.

Method 6: Conversational search with RAG

The core idea: Conversational search with RAG lets users ask questions in natural language, get direct answers grounded in your data, and follow up in a natural conversation.

This method combines two components. RAG retrieves relevant documents from your index and passes them as context to an LLM, which generates a direct, synthesized answer rather than a ranked list of results. This grounds the LLM’s response in your actual data, not just its training knowledge.

Conversation memory stores the history of the exchange so follow-up questions resolve correctly. A user can ask “What is our refund policy for international orders?”, get a direct answer, then follow up with “And what about exchanges?” and receive a coherent, context-aware response without repeating themselves.

OpenSearch supports this with OpenAI GPT models, Anthropic Claude on Amazon Bedrock, and Cohere Command models.

When to use it: Internal knowledge assistants, customer support chatbots, documentation Q&A, and compliance query tools, anywhere users need answers rather than search results.

Which OpenSearch AI search method should you choose?

A simple starting point:

• Default choice: Hybrid search covers the widest range of queries
• Cost-sensitive at scale: Neural sparse search
• Your data includes images: Multimodal search
• Users need answers, not links: Conversational search with RAG

Conclusion

OpenSearch treats AI search as a configuration problem rather than a custom engineering effort. You connect a model, declare your intent, and OpenSearch handles embedding generation, vector indexing, score normalization, and LLM integration behind the scenes, using the same REST APIs you already know.

These methods are not mutually exclusive; they complement one another. Most production systems start with one and combine several as their needs grow.

Ready to try OpenSearch AI search? Connect a model, declare a semantic field or build a hybrid pipeline, and run your first AI-powered query on the NetApp Instaclustr Managed Platform for free.