<div align="center"> <img width="300" src="https://raw.githubusercontent.com/redis/redis-vl-python/main/docs/_static/Redis_Logo_Red_RGB.svg" alt="Redis"> <h1>Redis Vector Library</h1> <p><strong>The AI-native Redis Python client</strong></p> </div> <div align="center">

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Introduction

Redis Vector Library (RedisVL) is the production-ready Python client for AI applications built on Redis. Lightning-fast vector search meets enterprise-grade reliability.

Perfect for building RAG pipelines with real-time retrieval, AI agents with memory and semantic routing, and recommendation systems with fast search and reranking.

🎯 Core Capabilities	🚀 AI Extensions	🛠️ Dev Utilities
Index Management<br/>Schema design, data loading, CRUD ops	Semantic Caching<br/>Reduce LLM costs & boost throughput	CLI<br/>Index management from terminal
Vector Search<br/>Similarity search with metadata filters	LLM Memory<br/>Agentic AI context management	Async Support<br/>Async indexing and search for improved performance
Complex Filtering<br/>Combine multiple filter types	Semantic Routing<br/>Intelligent query classification	Vectorizers<br/>8+ embedding provider integrations
Hybrid Search<br/>Combine semantic & full-text signals	Embedding Caching<br/>Cache embeddings for efficiency	Rerankers<br/>Improve search result relevancy

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💪 Getting Started

Installation

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

pip install redisvl

For more detailed instructions, visit the installation guide.

Redis

Choose from multiple Redis deployment options:

<details> <summary><b>Redis Cloud</b> - Managed cloud database (free tier available)</summary>

Redis Cloud offers a fully managed Redis service with a free tier, perfect for getting started quickly.

</details> <details> <summary><b>Docker</b> - Local development</summary>

Run Redis locally using Docker:

docker run -d --name redis -p 6379:6379 redis:latest

This runs Redis 8+ with built-in vector search capabilities.

</details> <details> <summary><b>Redis Enterprise</b> - Commercial, self-hosted database</summary>

Redis Enterprise provides enterprise-grade features for production deployments.

</details> <details> <summary><b>Redis Sentinel</b> - High availability with automatic failover</summary>

Configure Redis Sentinel for high availability:

# Connect via Sentinel
redis_url="redis+sentinel://sentinel1:26379,sentinel2:26379/mymaster"

</details> <details> <summary><b>Azure Managed Redis</b> - Fully managed Redis Enterprise on Azure</summary>

Azure Managed Redis provides fully managed Redis Enterprise on Microsoft Azure.

</details>

💡 Tip: Enhance your experience and observability with the free Redis Insight GUI.

Overview

Index Management

Design a schema for your use case that models your dataset with built-in Redis indexable fields (e.g. text, tags, numerics, geo, and vectors).

<details> <summary><b>Load schema from YAML file</b></summary>

index:
  name: user-idx
  prefix: user
  storage_type: json

fields:
  - name: user
    type: tag
  - name: credit_score
    type: tag
  - name: job_title
    type: text
    attrs:
      sortable: true
      no_index: false  # Index for search (default)
      unf: false       # Normalize case for sorting (default)
  - name: embedding
    type: vector
    attrs:
      algorithm: flat
      dims: 4
      distance_metric: cosine
      datatype: float32

from redisvl.schema import IndexSchema

schema = IndexSchema.from_yaml("schemas/schema.yaml")

</details> <details> <summary><b>Load schema from Python dictionary</b></summary>

from redisvl.schema import IndexSchema

schema = IndexSchema.from_dict({
    "index": {
        "name": "user-idx",
        "prefix": "user",
        "storage_type": "json"
    },
    "fields": [
        {"name": "user", "type": "tag"},
        {"name": "credit_score", "type": "tag"},
        {
            "name": "job_title",
            "type": "text",
            "attrs": {
                "sortable": True,
                "no_index": False,  # Index for search
                "unf": False        # Normalize case for sorting
            }
        },
        {
            "name": "embedding",
            "type": "vector",
            "attrs": {
                "algorithm": "flat",
                "datatype": "float32",
                "dims": 4,
                "distance_metric": "cosine"
            }
        }
    ]
})

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📚 Learn more about schema design and schema creation.

Create a SearchIndex class with an input schema to perform admin and search operations on your index in Redis:

from redis import Redis
from redisvl.index import SearchIndex

# Define the index
index = SearchIndex(schema, redis_url="redis://localhost:6379")

# Create the index in Redis
index.create()

An async-compatible index class also available: AsyncSearchIndex.

Load and fetch data to/from your Redis instance:

data = {"user": "john", "credit_score": "high", "embedding": [0.23, 0.49, -0.18, 0.95]}

# load list of dictionaries, specify the "id" field
index.load([data], id_field="user")

# fetch by "id"
john = index.fetch("john")

Retrieval

Define queries and perform advanced searches over your indices, including vector search, complex filtering, and hybrid search combining semantic and full-text signals.

<details> <summary><b>Quick Reference: Query Types</b></summary>

Query Type	Use Case	Description
`VectorQuery`	Semantic similarity search	Find similar vectors with optional filters
`RangeQuery`	Distance-based search	Vector search within a defined distance range
`FilterQuery`	Metadata filtering	Filter and search using metadata fields
`TextQuery`	Full-text search	BM25-based keyword search with field weighting
`HybridQuery`	Combined search	Combine semantic + full-text signals (Redis 8.4.0+)
`CountQuery`	Counting records	Count documents matching filter criteria

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Vector Search

VectorQuery - Flexible vector queries with customizable filters enabling semantic search:

from redisvl.query import VectorQuery

query = VectorQuery(
  vector=[0.16, -0.34, 0.98, 0.23],
  vector_field_name="embedding",
  num_results=3,
  # Optional: tune search performance with runtime parameters
  ef_runtime=100  # HNSW: higher for better recall
)
# run the vector search query against the embedding field
results = index.query(query)

RangeQuery - Vector search within a defined range paired with customizable filters

Complex Filtering

Build complex filtering queries by combining multiple filter types (tags, numerics, text, geo, timestamps) using logical operators:

```python
from redisvl.query import VectorQuery
from redisvl.query.filter import Tag, Num

# Combine multiple filter types
tag_filter = Tag("user") == "john"
price_filter = Num("price") >= 100

# Create complex filtering query with combined filters
query = VectorQuery(
    vector=[0.16, -0.34, 0.98, 0.23],
    vector_field_name="embedding",
    filter_expression=tag_filter & price_filter,
    num_results=10
)
results = index.query(query)
```

FilterQuery - Standard search using filters and

redisvl

Description

Introduction

💪 Getting Started

Installation

Redis

Overview

Index Management

Retrieval

Vector Search

Complex Filtering