Setup
uv pip install -U surrealdb pypdf openai agno
export OPENAI_API_KEY=xxx
docker run -d \
--rm \
--pull always \
-p 8000:8000 \
surrealdb/surrealdb:latest \
start \
--user root \
--pass root
./cookbook/scripts/run_surrealdb.sh
Example
agent_with_knowledge.py
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.surrealdb import SurrealDb
from surrealdb import Surreal
# SurrealDB connection parameters
SURREALDB_URL = "ws://localhost:8000"
SURREALDB_USER = "root"
SURREALDB_PASSWORD = "root"
SURREALDB_NAMESPACE = "test"
SURREALDB_DATABASE = "test"
# Create a client
client = Surreal(url=SURREALDB_URL)
client.signin({"username": SURREALDB_USER, "password": SURREALDB_PASSWORD})
client.use(namespace=SURREALDB_NAMESPACE, database=SURREALDB_DATABASE)
surrealdb = SurrealDb(
client=client,
collection="recipes", # Collection name for storing documents
efc=150, # HNSW construction time/accuracy trade-off
m=12, # HNSW max number of connections per element
search_ef=40, # HNSW search time/accuracy trade-off
embedder=OpenAIEmbedder(),
)
def sync_demo():
"""Demonstrate synchronous usage of SurrealDb"""
knowledge_base = Knowledge(
vector_db=surrealdb,
)
# Load data synchronously
knowledge_base.insert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
# Create agent and query synchronously
agent = Agent(knowledge=knowledge_base)
agent.print_response(
"What are the 3 categories of Thai SELECT is given to restaurants overseas?",
markdown=True,
)
if __name__ == "__main__":
# Run synchronous demo
print("Running synchronous demo...")
sync_demo()
Async Support ⚡
SurrealDB also supports asynchronous operations, enabling concurrency and leading to better performance.
async_surrealdb_db.py
import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.surrealdb import SurrealDb
from surrealdb import AsyncSurreal
# SurrealDB connection parameters
SURREALDB_URL = "ws://localhost:8000"
SURREALDB_USER = "root"
SURREALDB_PASSWORD = "root"
SURREALDB_NAMESPACE = "test"
SURREALDB_DATABASE = "test"
# Create a client
client = AsyncSurreal(url=SURREALDB_URL)
surrealdb = SurrealDb(
async_client=client,
collection="recipes", # Collection name for storing documents
efc=150, # HNSW construction time/accuracy trade-off
m=12, # HNSW max number of connections per element
search_ef=40, # HNSW search time/accuracy trade-off
embedder=OpenAIEmbedder(),
)
async def async_demo():
"""Demonstrate asynchronous usage of SurrealDb"""
await client.signin({"username": SURREALDB_USER, "password": SURREALDB_PASSWORD})
await client.use(namespace=SURREALDB_NAMESPACE, database=SURREALDB_DATABASE)
knowledge_base = Knowledge(
vector_db=surrealdb,
)
await knowledge_base.ainsert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)
agent = Agent(knowledge=knowledge_base)
await agent.aprint_response(
"What are the 3 categories of Thai SELECT is given to restaurants overseas?",
markdown=True,
)
if __name__ == "__main__":
# Run asynchronous demo
print("\nRunning asynchronous demo...")
asyncio.run(async_demo())
Using
ainsert() and aprint_response() with asyncio provides non-blocking operations, making your application more responsive under load.SurrealDB Params
| Parameter | Type | Default | Description |
|---|---|---|---|
client | Optional[Union[BlockingWsSurrealConnection, BlockingHttpSurrealConnection]] | None | A blocking connection, either HTTP or WS |
async_client | Optional[Union[AsyncWsSurrealConnection, AsyncHttpSurrealConnection]] | None | An async connection, either HTTP or WS |
collection | str | "documents" | Collection name to store documents |
distance | Distance | Distance.cosine | Distance metric to use (cosine, l2, or max_inner_product) |
efc | int | 150 | HNSW construction time/accuracy trade-off |
m | int | 12 | HNSW max number of connections per element |
search_ef | int | 40 | HNSW search time/accuracy trade-off |
embedder | Optional[Embedder] | OpenAIEmbedder() | Embedder instance for creating embeddings |