SingleStore Vector Database

Use SingleStore as a vector database for your Knowledge Base.

Setup

uv pip install -U PyMySQL sqlalchemy pypdf openai agno

Run SingleStore with Docker:

docker run -d --name singlestoredb \
  --platform linux/amd64 \
  -p 3306:3306 \
  -p 8080:8080 \
  -e ROOT_PASSWORD=admin \
  -e LICENSE_KEY=accept \
  ghcr.io/singlestore-labs/singlestoredb-dev:latest

Create the database:

docker exec singlestoredb memsql -u root -padmin \
  -e "CREATE DATABASE IF NOT EXISTS AGNO CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;"

Then set the environment variables:

export SINGLESTORE_HOST="localhost"
export SINGLESTORE_PORT="3306"
export SINGLESTORE_USERNAME="root"
export SINGLESTORE_PASSWORD="admin"
export SINGLESTORE_DATABASE="AGNO"

SingleStore supports both cloud-based and local deployments. For step-by-step guidance on setting up your cloud deployment, please refer to the SingleStore Setup Guide.

Example

agent_with_knowledge.py

from os import getenv

from sqlalchemy.engine import create_engine

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.singlestore import SingleStore

USERNAME = getenv("SINGLESTORE_USERNAME")
PASSWORD = getenv("SINGLESTORE_PASSWORD")
HOST = getenv("SINGLESTORE_HOST")
PORT = getenv("SINGLESTORE_PORT")
DATABASE = getenv("SINGLESTORE_DATABASE")
SSL_CERT = getenv("SINGLESTORE_SSL_CERT", None)

db_url = f"mysql+pymysql://{USERNAME}:{PASSWORD}@{HOST}:{PORT}/{DATABASE}?charset=utf8mb4"
if SSL_CERT:
    db_url += f"&ssl_ca={SSL_CERT}&ssl_verify_cert=true"

db_engine = create_engine(db_url)

knowledge = Knowledge(
    vector_db=SingleStore(
        collection="recipes",
        db_engine=db_engine,
        schema=DATABASE,
    ),
)

agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
)

if __name__ == "__main__":
    knowledge.insert(
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
    )

    agent.print_response("How do I make pad thai?", markdown=True)

SingleStore Params

Parameter	Type	Default	Description
collection	str	-	The name of the collection to use.
schema	Optional[str]	"ai"	The database schema to use.
db_url	Optional[str]	None	The database connection URL.
db_engine	Optional[Engine]	None	SQLAlchemy engine instance.
embedder	Optional[Embedder]	OpenAIEmbedder()	The embedder to use for creating vector embeddings.
distance	Distance	Distance.cosine	The distance metric to use for similarity search.
reranker	Optional[Reranker]	None	Reranker instance to rerank search results.
name	Optional[str]	None	Name of the vector database.
description	Optional[str]	None	Description of the vector database.