> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-agui.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Redis Vector Database
> Use Redis as a vector database for your Knowledge Base.
v2.2.7
You can use Redis as a vector database with Agno.
## Setup
For connecting to a remote Redis instance, pass your Redis connection string to the `redis_url` parameter and the index name to the `index_name` parameter of the `RedisDB` constructor.
For a local docker setup, you can use the following command:
```shell theme={null}
docker run -d --name redis \
-p 6379:6379 \
-p 8001:8001 \
redis/redis-stack:latest
docker start redis
```
## Example
```python agent_with_knowledge.py theme={null}
import os
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.redis import RedisDB
from agno.vectordb.search import SearchType
# Configure Redis connection (from environment variables if available, otherwise use local defaults)
REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379/0")
INDEX_NAME = os.getenv("REDIS_INDEX", "agno_cookbook_vectors")
# Initialize Redis Vector DB
vector_db = RedisDB(
index_name=INDEX_NAME,
redis_url=REDIS_URL,
search_type=SearchType.vector, # try SearchType.hybrid for hybrid search
)
# Build a Knowledge base backed by Redis
knowledge = Knowledge(
name="My Redis Vector Knowledge Base",
description="This knowledge base uses Redis + RedisVL as the vector store",
vector_db=vector_db,
)
# Add content (ingestion + chunking + embedding handled by Knowledge)
knowledge.insert(
name="Recipes",
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
metadata={"doc_type": "recipe_book"},
skip_if_exists=True,
)
# Query with an Agent
agent = Agent(knowledge=knowledge)
agent.print_response("List down the ingredients to make Massaman Gai", markdown=True)
```
## Redis Params