https://github.com/redis-applied-ai/redis-vector-php
Redis Vector Library (RedisVL) enables Redis as a real-time database for LLM applications, based on Predis PHP client
https://github.com/redis-applied-ai/redis-vector-php
Last synced: 19 days ago
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Redis Vector Library (RedisVL) enables Redis as a real-time database for LLM applications, based on Predis PHP client
- Host: GitHub
- URL: https://github.com/redis-applied-ai/redis-vector-php
- Owner: redis-applied-ai
- License: mit
- Created: 2024-01-31T08:37:36.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-29T15:33:39.000Z (almost 2 years ago)
- Last Synced: 2025-09-26T17:59:14.595Z (4 months ago)
- Language: PHP
- Homepage:
- Size: 69.3 KB
- Stars: 12
- Watchers: 3
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-php-ml - redis-applied-ai/redis-vector-php - oriented queries (Embeddings & Vector Search / Tokenizers & Prompt Utilities)
- awesome-php-ai - **redis-vector-php** - Redis Vector Library (RedisVL) enables Redis as a real-time database for LLM applications, based on Predis PHP client (Libraries / Data Manipulation)
README
## Introduction ##
The Redis Vector Library (RedisVL) is a PHP client for AI applications leveraging Redis.
Designed for:
- Vector similarity search
- Recommendation engine
A perfect tool for Redis-based applications, incorporating capabilities like vector-based semantic search,
full-text search, and geo-spatial search.
## Getting started ##
### Installation ###
```shell
composer install redis-ventures/redisvl
```
### Setting up Redis ####
Choose from multiple Redis deployment options:
1. [Redis Cloud](https://redis.com/try-free/): Managed cloud database (free tier available)
2. [Redis Stack](https://redis.io/docs/install/install-stack/docker/): Docker image for development
```shell
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
```
3. [Redis Enterprise](https://redis.com/redis-enterprise/advantages/): Commercial, self-hosted database
## What's included? ##
### Redis index management ###
1. Design your schema that models your dataset with one of the available Redis data structures (HASH, JSON)
and indexable fields (e.g. text, tags, numerics, geo, and vectors).
Load schema as a dictionary:
```php
$schema = [
'index' => [
'name' => 'products',
'prefix' => 'product:',
'storage_type' => 'hash',
],
'fields' => [
'id' => [
'type' => 'numeric',
],
'categories' => [
'type' => 'tag',
],
'description' => [
'type' => 'text',
],
'description_embedding' => [
'type' => 'vector',
'dims' => 3,
'datatype' => 'float32',
'algorithm' => 'flat',
'distance_metric' => 'cosine'
],
],
];
```
2. Create a SearchIndex object with an input schema and client connection to be able to interact with your Redis index
```php
use Predis\Client;
use RedisVentures\RedisVl\Index\SearchIndex;
$client = new Client();
$index = new SearchIndex($client, $schema);
// Creates index in the Redis
$index->create();
```
3. Load/fetch your data from index. If you have a hash index data should be loaded as key-value pairs
, for json type data loads as json string.
```php
$data = ['id' => '1', 'count' => 10, 'id_embeddings' => VectorHelper::toBytes([0.000001, 0.000002, 0.000003])];
// Loads given dataset associated with given key.
$index->load('key', $data);
// Fetch dataset corresponding to given key
$index->fetch('key');
```
### Realtime search ###
Define queries and perform advanced search over your indices, including combination of vectors and variety of filters.
**VectorQuery** - flexible vector-similarity semantic search with customizable filters
```php
use RedisVentures\RedisVl\Query\VectorQuery;
$query = new VectorQuery(
[0.001, 0.002, 0.03],
'description_embedding',
null,
3
);
// Run vector search against vector field specified in schema.
$results = $index->query($query);
```
Incorporate complex metadata filters on your queries:
```php
use RedisVentures\RedisVl\Query\Filter\TagFilter;
use RedisVentures\RedisVl\Enum\Condition;
$filter = new TagFilter(
'categories',
Condition::equal,
'foo'
);
$query = new VectorQuery(
[0.001, 0.002, 0.03],
'description_embedding',
null,
10,
true,
2,
$filter
);
// Results will be filtered by tag field values.
$results = $index->query($query);
```
### Filter types ###
#### Numeric ####
Numeric filters could be applied to numeric fields.
Supports variety of conditions applicable for scalar types (==, !=, <, >, <=, >=).
More information [here](https://redis.io/docs/interact/search-and-query/query/range/).
```php
use RedisVentures\RedisVl\Query\Filter\NumericFilter;
use RedisVentures\RedisVl\Enum\Condition;
$equal = new NumericFilter('numeric', Condition::equal, 10);
$notEqual = new NumericFilter('numeric', Condition::notEqual, 10);
$greaterThan = new NumericFilter('numeric', Condition::greaterThan, 10);
$greaterThanOrEqual = new NumericFilter('numeric', Condition::greaterThanOrEqual, 10);
$lowerThan = new NumericFilter('numeric', Condition::lowerThan, 10);
$lowerThanOrEqual = new NumericFilter('numeric', Condition::lowerThanOrEqual, 10);
```
#### Tag ####
Tag filters could be applied to tag fields. Single or multiple values can be provided, single values supports only
equality conditions (==, !==), for multiple tags additional conjunction (AND, OR) could be specified.
More information [here](https://redis.io/docs/interact/search-and-query/advanced-concepts/tags/)
```php
use RedisVentures\RedisVl\Query\Filter\TagFilter;
use RedisVentures\RedisVl\Enum\Condition;
use RedisVentures\RedisVl\Enum\Logical;
$singleTag = new TagFilter('tag', Condition::equal, 'value')
$multipleTags = new TagFilter('tag', Condition::notEqual, [
'conjunction' => Logical::or,
'tags' => ['value1', 'value2']
])
```
#### Text ####
Text filters could be applied to text fields. Values can be provided as a single word or multiple words with
specified condition. Empty value corresponds to all values (*).
More information [here](https://redis.io/docs/interact/search-and-query/query/full-text/)
```php
use RedisVentures\RedisVl\Query\Filter\TextFilter;
use RedisVentures\RedisVl\Enum\Condition;
$single = new TextFilter('text', Condition::equal, 'foo');
// Matching foo AND bar
$multipleAnd = new TextFilter('text', Condition::equal, 'foo bar');
// Matching foo OR bar
$multipleOr = new TextFilter('text', Condition::equal, 'foo|bar');
// Perform fuzzy search
$fuzzy = new TextFilter('text', Condition::equal, '%foobaz%');
```
#### Geo ####
Geo filters could be applied to geo fields. Supports only equality conditions,
value should be specified as specific-shape array.
More information [here](https://redis.io/docs/interact/search-and-query/query/geo-spatial/)
```php
use RedisVentures\RedisVl\Query\Filter\GeoFilter;
use RedisVentures\RedisVl\Enum\Condition;
use RedisVentures\RedisVl\Enum\Unit;
$geo = new GeoFilter('geo', Condition::equal, [
'lon' => 10.111,
'lat' => 11.111,
'radius' => 100,
'unit' => Unit::kilometers
]);
```
#### Aggregate ####
To apply multiple filters to a single query use AggregateFilter.
If there's the same logical operator that should be applied for each filter you can pass values in constructor,
if you need a specific combination use `and()` and `or()` methods to create combined filter.
```php
use RedisVentures\RedisVl\Query\Filter\AggregateFilter;
use RedisVentures\RedisVl\Query\Filter\TextFilter;
use RedisVentures\RedisVl\Query\Filter\NumericFilter;
use RedisVentures\RedisVl\Enum\Condition;
use RedisVentures\RedisVl\Enum\Logical;
$aggregate = new AggregateFilter([
new TextFilter('text', Condition::equal, 'value'),
new NumericFilter('numeric', Condition::greaterThan, 10)
], Logical::or);
$combinedAggregate = new AggregateFilter();
$combinedAggregate
->and(
new TextFilter('text', Condition::equal, 'value'),
new NumericFilter('numeric', Condition::greaterThan, 10)
)->or(
new NumericFilter('numeric', Condition::lowerThan, 100)
);
```
## Vectorizers ##
To be able to effectively create vector representations for your indexed data or queries, you have to use
[LLM's](https://en.wikipedia.org/wiki/Large_language_model). There's a variety of vectorizers that provide integration
with popular embedding models.
The only required option is your API key specified as environment variable or configuration option.
### OpenAI ###
```php
use RedisVentures\RedisVl\Vectorizer\Factory;
putenv('OPENAI_API_TOKEN=your_token');
$factory = new Factory();
$vectorizer = $factory->createVectorizer('openai');
// Creates vector representation of given text.
$embedding = $vectorizer->embed('your_text')
// Creates a single vector representation from multiple chunks.
$mergedEmbedding = $vectorizer->batchEmbed(['first_chunk', 'second_chunk']);
```
### VectorHelper ###
When you perform vector queries against Redis or load hash data into index that contains vector field data,
your vector should be represented as a blob string. VectorHelper allows you to create
blob representation from your vector represented as array of floats.
```php
use RedisVentures\RedisVl\VectorHelper;
$blobVector = VectorHelper::toBytes([0.001, 0.002, 0.003]);
```