Searching vector data
OpenSearch supports various methods for searching vector data, tailored to how the vectors were created and indexed. This guide explains the query syntax and options for raw vector search and auto-generated embedding search.
Search type comparison
The following table compares the search syntax and typical use cases for each vector search method.
| Feature | Query type | Input format | Model required | Use case |
|---|---|---|---|---|
| Raw vectors | knn | Vector array | No | Raw vector search |
| Auto-generated embeddings | neural | Text or image data | Yes | AI search |
Searching raw vectors
To search raw vectors, use the knn query type, provide the vector array as input, and specify the number of returned results k:
GET /my-raw-vector-index/_search
{
"query": {
"knn": {
"my_vector": {
"vector": [0.1, 0.2, 0.3],
"k": 2
}
}
}
}
Searching auto-generated embeddings
OpenSearch supports AI-powered search methods, including semantic, hybrid, multimodal, and conversational search with retrieval-augmented generation (RAG). These methods automatically generate embeddings from query input.
To run an AI-powered search, use the neural query type. Specify the query_text input, the model ID of the embedding model you configured in the ingest pipeline, and the number of returned results k. To exclude embeddings from being returned in search results, specify the embedding field in the _source.excludes parameter:
GET /my-ai-search-index/_search
{
"_source": {
"excludes": [
"output_embedding"
]
},
"query": {
"neural": {
"output_embedding": {
"query_text": "What is AI search?",
"model_id": "mBGzipQB2gmRjlv_dOoB",
"k": 2
}
}
}
}
Working with sparse vectors
OpenSearch also supports sparse vectors. For more information, see Neural sparse search.