Redis Vector Database - Agno

You can use Redis as a vector database with Agno.

Setup

uv pip install -U redis redisvl pypdf openai agno

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.

The vector store class was renamed from RedisDB to RedisDb in Agno v3.0; the old spelling still works as a deprecated alias. Because the session storage adapter in agno.db.redis is also named RedisDb, the vector store is additionally exported as RedisVectorDb — use that spelling when both are imported in the same module.

For a local docker setup, you can use the following command:

docker run -d --name redis \
  -p 6379:6379 \
  -p 8001:8001 \
  redis/redis-stack:latest

Example

agent_with_knowledge.py

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

Parameter	Type	Default	Description
`index_name`	`str`	Required	Name of the Redis index to store vector data
`redis_url`	`Optional[str]`	`None`	Redis connection URL
`redis_client`	`Optional[Redis]`	`None`	Redis client instance
`embedder`	`Optional[Embedder]`	`OpenAIEmbedder()`	Embedder instance to generate embeddings
`search_type`	`SearchType`	`SearchType.vector`	Type of search to perform (vector, keyword, hybrid)
`distance`	`Distance`	`Distance.cosine`	Distance metric for vector comparisons
`vector_score_weight`	`float`	`0.7`	Weight for vector similarity in hybrid search
`reranker`	`Optional[Reranker]`	`None`	Reranker instance to rerank search results
`**redis_kwargs`	`Any`	-	Additional Redis connection parameters