​
Setup

docker run -d --name singlestoredb \
  -p 3306:3306 \
  -p 8080:8080 \
  -e ROOT_PASSWORD=admin \
  -e SINGLESTORE_DB=AGNO \
  -e SINGLESTORE_USER=root \
  -e SINGLESTORE_PASSWORD=password \
  singlestore/cluster-in-a-box

docker start singlestoredb

After running the container, 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
import typer
from typing import Optional
from os import getenv

from sqlalchemy.engine import create_engine

from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
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_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=SingleStore(
        collection="recipes",
        db_engine=db_engine,
        schema=DATABASE,
    ),
)

def pdf_assistant(user: str = "user"):
    run_id: Optional[str] = None

    agent = Agent(
        run_id=run_id,
        user_id=user,
        knowledge_base=knowledge_base,
        use_tools=True,
        show_tool_calls=True,
        # Uncomment the following line to use traditional RAG
        # add_references_to_prompt=True,
    )
    if run_id is None:
        run_id = agent.run_id
        print(f"Started Run: {run_id}\n")
    else:
        print(f"Continuing Run: {run_id}\n")

    while True:
        agent.cli_app(markdown=True)

if __name__ == "__main__":
    # Comment out after first run
    knowledge_base.load(recreate=False)

    typer.run(pdf_assistant)

​
SingleStore Params

ParameterTypeDefaultDescription
collectionstr-The name of the collection to use.
schemaOptional[str]"ai"The database schema to use.
db_urlOptional[str]NoneThe database connection URL.
db_engineOptional[Engine]NoneSQLAlchemy engine instance.
embedderEmbedderOpenAIEmbedder()The embedder to use for creating vector embeddings.
distanceDistanceDistance.cosineThe distance metric to use for similarity search.

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Developer Resources