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Vector search
OpenSearch vector search provides a complete vector database solution for building efficient AI applications. Store and search vector embeddings alongside your existing data, making it easy to implement semantic search, retrieval-augmented generation (RAG), recommendation systems, and other AI-powered applications.
Overview
Watch this video to learn about key vector search features in OpenSearch and discover how to use OpenSearch as a vector database through a step-by-step demo.
To follow the demo, use these steps.
Steps
Prerequisites
Download the sample data for this demo:
wget https://amazon-pqa.s3.amazonaws.com/amazon_pqa_headsets.json
Prepare data for bulk indexing into OpenSearch:
head -n 5000 amazon_pqa_headsets.json | awk '{ print "{\"index\":{\"_index\":\"neural_search_pqa\"}}"; print;}' > neural_search_amazon_pqa_headsets.json
Enable running machine learning (ML) models on data nodes (not recommended for production environments):
PUT /_cluster/settings
{
"persistent": {
"plugins.ml_commons.only_run_on_ml_node": false
}
}
Step 1: Register and deploy a model
Register and deploy an ML model provided by OpenSearch:
POST /_plugins/_ml/models/_register?deploy=true
{
"name": "huggingface/sentence-transformers/all-distilroberta-v1",
"version": "1.0.1",
"model_format": "TORCH_SCRIPT"
}
Registering a model is an asynchronous task. OpenSearch returns a task ID for this task. Check the status of the task by using the Tasks API:
GET /_plugins/_ml/tasks/<task_id>
Once the task is complete, the task state will change to COMPLETED and the Tasks API response will contain a model ID for the registered model. Note the model ID; you’ll use it in the following steps.
Step 2: Create an ingest pipeline
Create an ingest pipeline that will generate vector embeddings from text:
PUT _ingest/pipeline/nlp-index-pipeline
{
"processors" : [
{
"text_embedding": {
"model_id": "<model_id>",
"field_map": {
"question_text": "question_vector"
}
}
}
]
}
Test the ingest pipeline:
POST /_plugins/_ml/_predict/text_embedding/<model_id>
{
"text_docs":[ "what does the package contain?"],
"return_number": true,
"target_response": ["sentence_embedding"]
}
Step 3: Create an index
Create a vector index and set the default ingest pipeline to the ingest pipeline created in the previous step:
PUT /neural_search_pqa
{
"settings": {
"index.knn": true,
"default_pipeline": "nlp-index-pipeline"
},
"mappings": {
"properties": {
"question_vector": {
"type": "knn_vector",
"dimension": 768
}
}
}
}
Step 4: Ingest data
Ingest the data you prepared in the Prerequisites section:
curl -XPOST -u "<username>:<password>" -k https://localhost:9200/_bulk --data-binary @neural_search_amazon_pqa_headsets.json -H 'Content-Type: application/json'
If you’re not running the Security plugin, omit the username and password:
curl -XPOST http://localhost:9200/_bulk --data-binary @neural_search_amazon_pqa_headsets.json -H 'Content-Type: application/json'
Test the vector generation:
GET /neural_search_pqa/_search
Step 5: Search the data
Now search the data using the following search methods.
Semantic search
To run a semantic search, send the following request:
GET /neural_search_pqa/_search
{
"size": 5,
"query": {
"neural": {
"question_vector": {
"query_text": "what does the package contain?",
"model_id": "<model_id>",
"k": 5
}
}
}
}
Raw vector search
To run a raw vector search using test embeddings, send the following request:
Request
GET /neural_search_pqa/_search
{
"query": {
"knn": {
"question_vector": {
"vector": [
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0.017786622,
0.023883764,
0.011552316,
0.06438146,
0.0033594605,
0.022067433,
-0.035531327
],
"k": 5
}
}
}
}
Lexical search
To run a lexical search, send the following request:
GET /neural_search_pqa/_search
{
"query": {
"match": {
"question_text": "what does the package contain?"
}
}
}
Hybrid search
Create a search pipeline for hybrid search:
PUT /_search/pipeline/hybrid-search-pipeline
{
"phase_results_processors": [
{
"normalization-processor": {
"normalization": {
"technique": "min_max"
},
"combination": {
"technique": "arithmetic_mean",
"parameters": {
"weights": [
0.3,
0.7
]
}
}
}
}
]
}
Set this pipeline as the default search pipeline for the index:
PUT /neural_search_pqa/_settings
{
"index.search.default_pipeline": "hybrid-search-pipeline"
}
To run a hybrid search, send the following request:
GET /neural_search_pqa/_search
{
"_source": "question_text",
"query": {
"hybrid": {
"queries": [
{
"match": {
"question_text":"what does the package contain?"
}
},
{
"neural": {
"question_vector": {
"query_text": "what does the package contain?",
"model_id": "<model_id>",
"k": 5
}
}
}
]
}
}
}
Clean up
Undeploy the model:
POST /_plugins/_ml/models/<model_id>/_undeploy
Delete the model:
DELETE /_plugins/_ml/models/<model_id>
Delete the index:
DELETE /neural_search_pqa
Getting started
You can bring your own vectors or let OpenSearch generate embeddings automatically from your data. See Preparing vectors.
1
Create a vector index for storing your embeddings.
2
Ingest your data into the index.
3
Use raw vector search or AI-powered methods like semantic, hybrid, multimodal, or neural sparse search. Add RAG to build conversational search.