You're viewing version 2.17 of the OpenSearch documentation. This version is no longer maintained. For the latest version, see the current documentation. For information about OpenSearch version maintenance, see Release Schedule and Maintenance Policy.
Neural search
Neural search transforms text into vectors and facilitates vector search both at ingestion time and at search time. During ingestion, neural search transforms document text into vector embeddings and indexes both the text and its vector embeddings in a vector index. When you use a neural query during search, neural search converts the query text into vector embeddings, uses vector search to compare the query and document embeddings, and returns the closest results.
Before you ingest documents into an index, documents are passed through a machine learning (ML) model, which generates vector embeddings for the document fields. When you send a search request, the query text or image is also passed through the ML model, which generates the corresponding vector embeddings. Then neural search performs a vector search on the embeddings and returns matching documents.
Prerequisite
Before using neural search, you must set up an ML model. When selecting a model, you have the following options:
-
Use a pretrained model provided by OpenSearch. For more information, see OpenSearch-provided pretrained models.
-
Upload your own model to OpenSearch. For more information, see Custom local models.
-
Connect to a foundation model hosted on an external platform. For more information, see Connecting to remote models.
Tutorial
For a step-by-step tutorial, see Neural search tutorial.
Using an ML model for neural search
Once you set up an ML model, choose one of the following search methods to use your model for neural search.
Semantic search
Semantic search uses dense retrieval based on text embedding models to search text data. For detailed setup instructions, see Semantic search.
Hybrid search
Hybrid search combines keyword and neural search to improve search relevance. For detailed setup instructions, see Hybrid search.
Multimodal search
Multimodal search uses neural search with multimodal embedding models to search text and image data. For detailed setup instructions, see Multimodal search.
Sparse search
Sparse search uses neural search with sparse retrieval based on sparse embedding models to search text data. For detailed setup instructions, see Sparse search.
Conversational search
With conversational search, you can ask questions in natural language, receive a text response, and ask additional clarifying questions. For detailed setup instructions, see Conversational search.