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Redis is a fast in-memory data store that supports vector similarity search through the Redis Search module. It supports both cloud-hosted (Redis Cloud) and self-hosted deployments.
Cognee can use Redis as a vector store backend through this community-maintained adapter.

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Installation

This adapter is a separate package from core Cognee. Before installing, complete the Cognee installation and ensure your environment is configured with LLM and embedding providers. After that, install the adapter package: 
uv pip install cognee-community-vector-adapter-redis

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Configuration

Run a local Redis instance with the Search module enabled:
docker run -d --name redis -p 6379:6379 redis:8.0.2
Configure in Python:
from cognee_community_vector_adapter_redis import register
from cognee import config

config.set_vector_db_config({
    "vector_db_provider": "redis",
    "vector_db_url": "redis://localhost:6379",
})
Or via environment variables:
VECTOR_DB_PROVIDER="redis"
VECTOR_DB_URL="redis://localhost:6379"

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Important Notes

Import register from the adapter package before using Redis with Cognee. This registers the adapter with Cognee’s provider system.
  1. Connection Errors: Ensure Redis is running and accessible at the specified URL
  2. Search Module Missing: Make sure Redis has the Search module enabled
  3. Embedding Dimension Mismatch: Verify embedding engine dimensions match index configuration
  4. Collection Not Found: Always create collections before adding data points

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Resources

RedisVL Docs

RedisVL library documentation (powers this adapter)

Adapter Source

GitHub repository

Extended Example

Full usage example script

Vector Stores

Official vector providers

Community Overview

All community integrations

Setup Overview

Configuration guide