# Vector Types

> In the previous sections Indexing and Search, we encountered the vector type. This section will explore additional vector types and their applications in the re

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LLMS index: [llms.txt](/llms.txt)

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In the previous sections [Indexing](/pgvecto-rs/usage/indexing/) and [Search](/pgvecto-rs/usage/search/), we encountered the `vector` type. This section will explore additional vector types and their applications in the realm of vector search.

- [Half-Precision Vector](/pgvecto-rs/reference/vector-types/vecf16/)
- [8-Bit Integer Vector](/pgvecto-rs/reference/vector-types/veci8/) **since v0\.3\.0**
- [Sparse Vector](/pgvecto-rs/reference/vector-types/svector/) **since v0\.3\.0**
- [Binary Vector](/pgvecto-rs/reference/vector-types/bvector/) **since v0\.3\.0**

## Low-precision Indexing

**since v0\.3\.0**

To improve the speed of queries on the `vector` column, consider creating a low-precision index using `vecf16`, `veci8`, or `bvector` types instead of a `vector` type.

```sql
CREATE TABLE t (val vector(3));
INSERT INTO t (val) SELECT ARRAY[random(), random(), random()]::real[] FROM generate_series(1, 1000);

-- Create bvector index at vector column
-- Warning: Float32 vectors are truncated to 
--          integers of either 0 or 1 during index construction
CREATE INDEX lp_bin ON t USING vectors ((binarize(val)::bvector(3)) bvector_l2_ops);

-- Create vecf16 index at vector column
CREATE INDEX lp_f16 ON t USING vectors ((val::vecf16(3)) vecf16_l2_ops);

-- Create veci8 index at vector column
-- Warning: Float32 vectors are truncated to 
--          integers ranging from -128 to 127 during index construction
CREATE INDEX lp_i8 ON t USING vectors ((val::veci8(3)) veci8_l2_ops);
```

## Shortening Embedding

**since v0\.3\.0**

Certain embedding models like OpenAI `text-embedding-3-small` support [shortening embeddings](https://openai.com/blog/new-embedding-models-and-api-updates), which involves removing some numbers from the end of the sequence without losing the embedding's concept-representing properties.

In some cases, it may be necessary to change the embedding dimension after generation. The `pgvecto.rs` offers an approach to query by shortened embeddings.

```sql
CREATE TABLE t (val vector(3));
INSERT INTO t (val) SELECT ARRAY[random(), random(), random()]::real[] FROM generate_series(1, 1000);
CREATE INDEX ON t USING vectors (val vector_l2_ops);

-- Measure distance using only the first two dimensions
SELECT * from t ORDER BY (val[:2])::vector(2) <-> '[3, 2]'::vector;
```

Shortened embeddings are supported by all data types in `pgvecto.rs` since version `v0.3.0`.

<a id="Casts"></a>
## Casts

This diagram shows the conversion between different data types, where the types connected by arrows can be cast to each other:

<iframe class="quiver-embed" src="https://q.uiver.app/?q=WzAsNyxbMywyLCJcXHRleHR7dmVjdG9yfSJdLFsyLDAsIlxcdGV4dHt2ZWNmMTZ9Il0sWzQsMCwiXFx0ZXh0e3ZlY2k4fSJdLFszLDUsIlxcdGV4dHtidmVjdG9yfSJdLFswLDIsIlxcdGV4dHtBUlJBWX0iXSxbNiwyLCJcXHRleHR7c3ZlY3Rvcn0iXSxbNywwLCJcXHRleHR7c3ZlY2YxNn0iXSxbMiwwXSxbMCwyXSxbMSwwXSxbMCwxXSxbMywwXSxbMCwzXSxbNCwwLCIiLDAseyJzdHlsZSI6eyJib2R5Ijp7Im5hbWUiOiJkb3R0ZWQifX19XSxbMCw1XSxbNSwwXSxbNiw1XSxbNSw2XSxbMCw0LCIiLDAseyJzdHlsZSI6eyJib2R5Ijp7Im5hbWUiOiJkb3R0ZWQifX19XV0=&embed" width="90%" height="440" style="border-radius: 8px; border: none;"></iframe>

Among them, `ARRAY` is a native type of `postgreSQL`, others are types defined by `pgvecto.rs`.

Here are some examples of vector type casting:
```sql
-- OK: String ⇒ vector
SELECT '{1,2,3}'::vector;

-- OK: vector ⇒ ARRAY
SELECT '{1,2,3}'::vector::real[];

-- OK: ARRAY ⇒ vector ⇒ bvector
SELECT ARRAY[1.0 ,0 ,0]::real[]::vector::bvector;

-- ERROR: cannot cast type vecf16 to veci8
SELECT '[3, 2]'::vecf16::veci8;

-- OK: vecf16 ⇒ vector ⇒ veci8
SELECT '[3, 2]'::vecf16::vector::veci8;
```

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Section pages:

- [Half-Precision Vector: `vecf16`](/pgvecto-rs/reference/vector-types/vecf16/): Stored as a half-precision number format, vecf16 takes advantage of the 16-bit float, which uses half the memory and bandwidth compared to vector.
- [8-Bit Integer Vector: `veci8`](/pgvecto-rs/reference/vector-types/veci8/): Int8 is a data type used to represent signed integers. It stands for "integer 8-bit" and typically uses 8 bits of memory to store a value. This allows it to rep
- [Sparse Vector: `svector`](/pgvecto-rs/reference/vector-types/svector/): Unlike dense vectors, sparse vectors are very high-dimensional but contain few non-zero values.
- [Binary Vector: `bvector`](/pgvecto-rs/reference/vector-types/bvector/): The bvector type is a binary vector type in pgvecto.rs. It represents a binary vector, which is a vector where each component can take on two possible values, t

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Backlinks:

- [Indexing](/pgvecto-rs/usage/indexing/)