# Vector Search and Hybrid Retrieval — Elasticsearch

Source: https://www.skillbyai.com/en/elasticsearch/s-vector

> Semantic matching with dense_vector and knn.

## Embeddings next to text

An embedding model turns text (or images) into a vector so that similar meanings are close together. Elasticsearch stores these in a `dense_vector` field with `dims` and a `similarity` (`cosine`, `dot_product`, `l2_norm`), indexed with an HNSW graph for **approximate k-nearest-neighbour** search. A search request's `knn` option takes a `query_vector`, the number of results `k` and `num_candidates` (more candidates per shard means better recall but more work), and can include a `filter`. **Hybrid search** combines a BM25 `query` with `knn` in one request, either by adding boosted scores or with reciprocal rank fusion (RRF), which merges rankings rather than raw scores. Newer versions add features such as `semantic_text` fields, retrievers and quantised vectors, and some depend on licence tier, so check the docs for your version.

## A vector field and a hybrid query

Kibana Dev Tools console syntax; send the same requests with curl or a client library.

```http
PUT /docs
{
  "mappings": {
    "properties": {
      "title":     { "type": "text" },
      "lang":      { "type": "keyword" },
      "embedding": { "type": "dense_vector", "dims": 384, "index": true, "similarity": "cosine" }
    }
  }
}

GET /docs/_search
{
  "query": {
    "match": { "title": { "query": "reset my password", "boost": 0.3 } }
  },
  "knn": {
    "field": "embedding",
    "query_vector": [ 0.012, -0.044, 0.093 ],
    "k": 10,
    "num_candidates": 100,
    "filter": { "term": { "lang": "en" } },
    "boost": 0.7
  },
  "size": 10
}

# query_vector is shortened here; it must have exactly 384 values
# and come from the same model used to embed the documents.
```

## Keep BM25 in the mix

Vectors are great at paraphrases but weak at exact identifiers, SKUs and rare names. Hybrid retrieval usually beats either method alone; evaluate on your own queries.

**Quiz:** What does increasing num_candidates in a knn search generally do?

- [x] Improves recall at the cost of more work per shard
- [ ] Changes the vector dimensions
- [ ] Disables filters
- [ ] Switches scoring to BM25

*Answer:* Improves recall at the cost of more work per shard. More candidates are explored in the HNSW graph.