# Rank by Vector Similarity

### Description
Ranks objects in `SOURCE [OBJ,VECTOR]` according to the relevance scores of each `VEC` with `QUERY [VECTOR]`.
The relevance score is computed using the Euclidean distance between vectors.

### Input
- `SOURCE [OBJ, VECTOR]`: a 2-column input with an object-vector pair. Typically obtained with the `Extract vectors` block.
- `QVECS [VECTOR]`: a list of vectors to rank `SOURCE` objects against

Note: Vectors can differ in length, model, and pooling method. For correct usage, each vector should be encoded by the same embedding model, ideally with the same pooling method.

### Output
- `RETRIEVE [OBJ]`: a list of ranked objects

### Parameters
- `Search type`: the method used for vector similarity search
  - `EXACT`: computes the exact distance between each source and query vector, only recommended for a small amount of source vectors (~100,000 or less)
  - `HNSW`: computes the approximate distance between each source and query vector, based on the [Hierarchical Navigable Small World](https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world) algorithm.
- `K value`: the amount of objects to retrieve when using an approximate `Search type`, greatly affects search time.
- `Index name`: name necessary for storing the graph-based indices used during approximate search, needs to be unique per source data

Output scores can be [normalised](docs://score_normalisation).

Note: When using `HNSW`, if the `SOURCE` vectors are changed/updated the index will not automatically update. Change `Index name` to create a new index and see the changes.