# Understanding OpenSearch® knn_vector: A Developer’s Guide to Semantic Search
[Blog](/blog/)>[Technology](/blog/category/technical/)>Understanding OpenSearch® knn\_vector: A Developer’s Guide to Semantic Search
Understanding OpenSearch® knn\_vector: A Developer’s Guide to Semantic Search
=============================================================================
April 14, 2026 | By [ Ramya Ravi](https://www.instaclustr.com/blog/author/ramya-ravi/)
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What Is knn\_vector in OpenSearch?
----------------------------------
knn\_vector is OpenSearch’s dedicated field type for storing dense vector embeddings and performing semantic similarity search. Unlike keyword-based search, which matches exact terms, knn\_vector enables applications to retrieve results based on meaning—making it foundational for AI-powered applications such as chatbots, recommendation engines, and Retrieval-Augmented Generation (RAG) pipelines.
When you store content as dense numerical vectors using knn\_vector, OpenSearch can find the most semantically similar results to any query, even when the exact words don’t match.
This is Part 1 of a two-part series. Part 1 covers the `knn_vector` field type. [Part 2](https://www.instaclustr.com/blog/understanding-opensearch-vector-field-type-part-2-sparse_vector/) covers the `sparse_vector` field type for neural sparse search.
OpenSearch Vector Field Types: A Quick Comparison
-------------------------------------------------
OpenSearch provides two dedicated vector field types for building intelligent search applications:
**Field type****Search type****Best for**knn\_vectorDense semantic similarity searchMeaning-based retrieval, RAG pipelines, recommendationssparse\_vectorNeural sparse searchToken-weighted precision, term-level relevanceTogether, these two field types form a complete foundation for production-ready AI search systems.
knn\_vector Configuration Parameters
------------------------------------
When defining a knn\_vector field in an index mapping, the following parameters control search behavior:
**Parameter****Description****Common options**space\_typeHow similarity is measuredl2 (Euclidean), cosinesimil (cosine similarity)engineUnderlying search libraryFacebook AI Similarity Search (FAISS is the default value), Lucenemethod (algorithm)How the vector index is builtHierarchical Navigable Small World (HNSW supported by both FAISS and Lucene) and Inverted File Index (IVF supported via FAISS)data\_typePrecision of stored vectorsfloat (default), byte, binarydimensionNumber of dimensions in the vectorMust match your embedding model’s output**Important notes**
- Non-Metric Space Library (NMSLIB) was deprecated in OpenSearch 2.16 and **removed in OpenSearch 3.0**
- The dimension parameter is required when using the method definition approach
- Real-world embedding models typically produce vectors of **384** to **1536 dimensions**
### Two Ways to Define a knn\_vector Field
- **Method definition** — Explicitly specify engine, algorithm, and space\_type
- **Model ID** — Inherit configuration from a pre-trained model already registered in OpenSearch
### Prerequisites: Enabling k-NN Search
Before using knn\_vector, you must:
- Enable either the **k-NN Plugin** (Similarity Search Engine) or the **AI Search Plugin** when provisioning your cluster (enabling the AI Search Plugin automatically enables the k-NN plugin)
- Enable k-NN search in the index settings: `"index.knn": true`
- Define the `knn_vector` field in the index mapping
How to Use knn\_vector on Instaclustr: Step-by-Step
---------------------------------------------------
The following example uses the Dev Tools console in OpenSearch Dashboards on a cluster provisioned via the NetApp Instaclustr Managed Platform. Running OpenSearch on Instaclustr reduces infrastructure management overhead, allowing teams to focus on building search experiences.
### Step 1: Create a Vector Index
Create an index with k-NN enabled and define an embedding field to store vectors.
PUT instaclustr\_knn\_demo { "settings": { "index.knn": true }, "mappings": { "properties": { "embedding": { "type": "knn\_vector", "dimension": 4 } } } }
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PUT instaclustr\_knn\_demo
{
"settings": {
"index.knn": true
},
"mappings": {
"properties": {
"embedding": {
"type": "knn\_vector",
"dimension": 4
}
}
}
}
Default values applied when parameters are not explicitly defined:
**Parameter****Default value**space\_typel2enginefaissmethod namehnswdata\_typefloatExpected output:
{ "acknowledged": true, "shards\_acknowledged": true, "index": "instaclustr\_knn\_demo" }
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{
"acknowledged": true,
"shards\_acknowledged": true,
"index": "instaclustr\_knn\_demo"
}
**Production note:** Always define parameters explicitly to match your embedding model and performance requirements. Update dimension to match your model’s output size.
### Step 2: Index a Document with a Vector Embedding
Index a document by providing the text content and its corresponding vector embedding.
POST instaclustr\_knn\_demo/\_doc { "text": "vector search example", "embedding": \[0.12, 0.45, 0.67, 0.89\] }
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POST instaclustr\_knn\_demo/\_doc
{
"text": "vector search example",
"embedding": \[0.12, 0.45, 0.67, 0.89\]
}
**Note:** The embedding values in this example are manually written placeholders for demonstration. In practice, embeddings are generated by a machine learning model—such as those available via the sentence-transformers Python library—that converts text into a fixed-size list of numbers representing its meaning.
**Critical:** Always use the same embedding model for both indexing and querying. Using different models produces meaningless similarity scores because the vectors are not comparable.
Expected output:
{ "\_index": "instaclustr\_knn\_demo", "\_id": "XEdATp0BSeQDCK7X2oA6", "\_version": 1, "result": "created", ... }
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{
"\_index": "instaclustr\_knn\_demo",
"\_id": "XEdATp0BSeQDCK7X2oA6",
"\_version": 1,
"result": "created",
...
}
### Step 3: Run a Semantic Search Query
Execute a k-NN query by passing a query vector to retrieve the most semantically
similar results. A higher similarity score indicates a closer semantic match.
POST instaclustr\_knn\_demo/\_search { "query": { "knn": { "embedding": { "vector": \[0.10, 0.40, 0.60, 0.80\], "k": 5 } } } }
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POST instaclustr\_knn\_demo/\_search
{
"query": {
"knn": {
"embedding": {
"vector": \[0.10, 0.40, 0.60, 0.80\],
"k": 5
}
}
}
}
Expected output (truncated):
{ "took": 509, "timed\_out": false, "hits": { "total": { "value": 1, "relation": "eq" }, "max\_score": 0.98434883, "hits": \[...\] } }
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{
"took": 509,
"timed\_out": false,
"hits": {
"total": { "value": 1, "relation": "eq" },
"max\_score": 0.98434883,
"hits": \[...\]
}
}
A max\_score close to 1.0 indicates a highly similar result.
Once you master the OpenSearch both the field types, try combining keyword search
with dense semantic search to build a truly hybrid AI search pipeline.
Frequently Asked Questions
--------------------------
**What is knn\_vector in OpenSearch?**
knn\_vector is a field type in OpenSearch used to store dense vector embeddings and
perform k-nearest neighbor (k-NN) similarity search. It enables semantic search—
finding results based on meaning rather than exact keyword matches.
**What is the difference between knn\_vector and sparse\_vector in OpenSearch?**
knn\_vector stores dense embeddings for semantic similarity search, while sparse\_vector stores token-weighted sparse representations for neural sparse search. They are complementary: knn\_vector excels at meaning-based retrieval, while sparse\_vector adds term-level precision.
**What engines does OpenSearch knn\_vector support?**
OpenSearch knn\_vector supports two engines: FAISS (Facebook AI Similarity Search,
the default) and Lucene. NMSLIB was deprecated in OpenSearch 2.16 and removed in
OpenSearch 3.0.
**What dimension should I use for knn\_vector?**
The dimension value must match the output size of your embedding model. Common
real-world embedding models produce vectors of 384 to 1536 dimensions.
**How do I enable k-NN search in OpenSearch?**
Set “index.knn”: true in your index settings and define a knn\_vector field in your index mapping. When provisioning on Instaclustr, enable the k-NN Plugin or the AI Search Plugin.
**Can I use knn\_vector for RAG pipelines?**
Yes. knn\_vector is a core component of RAG (Retrieval-Augmented Generation) pipelines, enabling semantic retrieval of relevant documents that are then passed to a language model for generation.
Next Steps
----------
knn\_vector provides the foundation for semantic search in OpenSearch. To continue
building:
- [\[Part 2 of this series\]](https://www.instaclustr.com/blog/understanding-opensearch-vector-field-type-part-2-sparse_vector/) — Explore `sparse_vector` and neural sparse approximate
nearest neighbor (ANN) search for improved search efficiency and precision
- Create a free OpenSearch cluster on Instaclustr and run the code snippets in
this guide to execute your first semantic search query
- For production deployments, explicitly configure `space_type`, engine, method,
and dimension parameters to match your embedding model
### About the author
**[Ramya Ravi](https://www.instaclustr.com/blog/author/ramya-ravi/)** | Developer Advocate
Ramya Ravi is a Developer Advocate at NetApp, specializing in open source and AI technologies. She connects Instaclustr supported technologies with emerging AI use cases, creating technical content, demos, and guides to help developers build and deploy AI applications. She has previously worked for Intel and TCS. In her previous role at Intel, she drove AI/ML ecosystem growth by developing tutorials and resources that supported thousands of AI developers across Reddit, the PyTorch community, and developer ecosystem forums.
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