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Package redisvl
Short Description Redis Vector Library (RedisVL) enables Redis as a real-time database for LLM applications, based on Predis PHP client
License MIT
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Informations about the package redisvl
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
Setting up Redis
Choose from multiple Redis deployment options:
- Redis Cloud: Managed cloud database (free tier available)
-
Redis Stack: Docker image for development
- Redis Enterprise: Commercial, self-hosted database
What's included?
Redis index management
- 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:
-
Create a SearchIndex object with an input schema and client connection to be able to interact with your Redis index
- 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.
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
Incorporate complex metadata filters on your queries:
Filter types
Numeric
Numeric filters could be applied to numeric fields. Supports variety of conditions applicable for scalar types (==, !=, <, >, <=, >=). More information here.
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
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
Geo
Geo filters could be applied to geo fields. Supports only equality conditions, value should be specified as specific-shape array. More information here
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.
Vectorizers
To be able to effectively create vector representations for your indexed data or queries, you have to use LLM's. 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
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.
