Setup
uv pip install -U PyMySQL sqlalchemy pypdf openai agno
docker run -d --name singlestoredb \
--platform linux/amd64 \
-p 3306:3306 \
-p 8080:8080 \
-v /tmp:/var/lib/memsql \
-e ROOT_PASSWORD=admin \
-e LICENSE_KEY=accept \
ghcr.io/singlestore-labs/singlestoredb-dev:latest
docker exec singlestoredb memsql -u root -padmin \
-e "CREATE DATABASE IF NOT EXISTS AGNO CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;"
export SINGLESTORE_HOST="localhost"
export SINGLESTORE_PORT="3306"
export SINGLESTORE_USERNAME="root"
export SINGLESTORE_PASSWORD="admin"
export SINGLESTORE_DATABASE="AGNO"
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)
Async Support ⚡
SingleStore also supports asynchronous operations, enabling concurrency and leading to better performance.
async_singlestore_db.py
import asyncio
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__":
asyncio.run(
knowledge.ainsert(url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf")
)
asyncio.run(agent.aprint_response("How do I make pad thai?", markdown=True))
Use
ainsert() and aprint_response() methods with asyncio.run() for non-blocking operations in high-throughput applications.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. |