Guides on Docs

Getting Started

Mon, 01 Jan 0001 00:00:00 +0000

RedisVL is a Python library with an integrated CLI for building AI applications with Redis. This guide covers the core workflow:

Defining an IndexSchema
Preparing a sample dataset
Creating a SearchIndex
Using the rvl CLI
Loading data into Redis
Fetching and managing records
Executing vector searches
Updating an index

Prerequisites

Before you begin, ensure you have:

Query and Filter Data

Mon, 01 Jan 0001 00:00:00 +0000

This guide covers the filtering capabilities in RedisVL, including tag, numeric, text, geo, and timestamp filters. You'll learn how to combine filters to create complex queries that narrow down search results precisely.

Prerequisites

Before you begin, ensure you have:

Cache LLM Responses

Mon, 01 Jan 0001 00:00:00 +0000

This guide demonstrates how to use RedisVL's SemanticCache to cache LLM responses based on semantic similarity. Semantic caching reduces API costs and latency by retrieving cached responses for semantically similar prompts instead of making redundant API calls.

Prerequisites

Before you begin, ensure you have:

Create Embeddings with Vectorizers

Mon, 01 Jan 0001 00:00:00 +0000

This guide demonstrates how to create embeddings using RedisVL's built-in text vectorizers. RedisVL supports multiple embedding providers: OpenAI, HuggingFace, Vertex AI, Cohere, Mistral AI, Amazon Bedrock, VoyageAI, and custom vectorizers.

Prerequisites

Before you begin, ensure you have:

Choose a Storage Type

Mon, 01 Jan 0001 00:00:00 +0000

Redis provides a variety of data structures that can adapt to your domain-specific applications. This guide demonstrates how to use RedisVL with both Hash and JSON storage types, helping you choose the right approach for your use case.

Prerequisites

Before you begin, ensure you have:

Rerank Search Results

Mon, 01 Jan 0001 00:00:00 +0000

This guide demonstrates how to use RedisVL to rerank search results (documents, chunks, or records) based on query relevance. RedisVL supports reranking through Cross-Encoders from Hugging Face, Cohere Rerank API, and VoyageAI Rerank API.

Prerequisites

Before you begin, ensure you have:

Manage LLM Message History

Mon, 01 Jan 0001 00:00:00 +0000

Large Language Models are inherently stateless with no knowledge of previous interactions. This becomes a challenge when engaging in long conversations that rely on context. The solution is to store and retrieve conversation history with each LLM call.

This guide demonstrates how to use Redis to structure, store, and retrieve conversational message history.

Prerequisites

Before you begin, ensure you have:

Route Queries with SemanticRouter

Mon, 01 Jan 0001 00:00:00 +0000

RedisVL provides a SemanticRouter interface that uses Redis' built-in search and aggregation to perform KNN-style classification over a set of Route references to determine the best match.

This guide covers how to use Redis as a Semantic Router for your applications.

Prerequisites

Before you begin, ensure you have:

Optimize Indexes with SVS-VAMANA

Mon, 01 Jan 0001 00:00:00 +0000

This guide covers SVS-VAMANA (Scalable Vector Search with VAMANA graph algorithm), a graph-based vector search algorithm optimized for compression methods to reduce memory usage. It combines the Vamana graph algorithm with advanced compression techniques (LVQ and LeanVec) and is optimized for Intel hardware.

Prerequisites

Before you begin, ensure you have:

Cache Embeddings

Mon, 01 Jan 0001 00:00:00 +0000

RedisVL provides an EmbeddingsCache that stores and retrieves embedding vectors with their associated text and metadata. This cache is useful for applications that frequently compute the same embeddings.

Prerequisites

Before you begin, ensure you have:

Use Advanced Query Types

Mon, 01 Jan 0001 00:00:00 +0000

This guide covers advanced query types available in RedisVL:

TextQuery: Full text search with advanced scoring
AggregateHybridQuery and HybridQuery: Combines text and vector search for hybrid retrieval
MultiVectorQuery: Search over multiple vector fields simultaneously

These query types enable sophisticated search applications that go beyond simple vector similarity search.

Prerequisites

Before you begin, ensure you have:

Write SQL Queries for Redis

Mon, 01 Jan 0001 00:00:00 +0000

While Redis does not natively support SQL, RedisVL provides a SQLQuery class that translates SQL-like queries into Redis queries.

The SQLQuery class wraps the sql-redis package. This package is not installed by default, so install it with:

pip install redisvl[sql-redis]

Prerequisites

Before you begin, ensure you have:

Install RedisVL

Mon, 01 Jan 0001 00:00:00 +0000

There are a few ways to install RedisVL. The easiest way is to use pip.

Install RedisVL with Pip

Install redisvl into your Python (>=3.9) environment using pip:

The RedisVL CLI

Mon, 01 Jan 0001 00:00:00 +0000

RedisVL is a Python library with a dedicated CLI to help load and create vector search indices within Redis.

This notebook will walk through how to use the Redis Vector Library CLI (rvl).

Before running this notebook, be sure to

Have installed redisvl and have that environment active for this notebook.
Have a running Redis instance with the Search and Query capability

# First, see if the rvl tool is installed
!rvl version

Commands

Here's a table of all the rvl commands and options. We'll go into each one in detail below.