CamelAI
🐫 CAMEL is an open-source community dedicated to finding the scaling laws of agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.
Setup
You can run SurrealDB locally or start with a free SurrealDB Cloud account.
For local, two options:
Install SurrealDB and run SurrealDB. Run in-memory with:
surreal start -u root -p root
Run with Docker.
docker run --rm --pull always -p 8000:8000 surrealdb/surrealdb:latest start
Example
import os
from camel.storages.vectordb_storages import (
SurrealStorage,
VectorDBQuery,
VectorRecord,
)
def main():
url = os.getenv("SURREAL_URL", "ws://localhost:8000/rpc")
table = os.getenv("SURREAL_TABLE", "tb")
vector_dim = int(os.getenv("SURREAL_VECTOR_DIM", 4))
namespace = os.getenv("SURREAL_NAMESPACE", "ns")
database = os.getenv("SURREAL_DATABASE", "db")
user = os.getenv("SURREAL_USER", "user")
password = os.getenv("SURREAL_PASSWORD")
if not password:
raise ValueError(
"Environment variable SURREAL_PASSWORD is not set. "
"Please set it before running."
)
storage = SurrealStorage(
url=url,
table=table,
namespace=namespace,
database=database,
user=user,
password=password,
vector_dim=vector_dim,
)
storage.clear()
print("[Step 1] After clear:", storage.status())
vec1 = VectorRecord(vector=[1, 2, 3, 4], payload={"name": "test_1"})
vec2 = VectorRecord(vector=[5, 6, 7, 8], payload={"name": "test_2"})
vec3 = VectorRecord(vector=[9, 10, 11, 12], payload={"name": "test_3"})
vec4 = VectorRecord(vector=[13, 14, 15, 16], payload={"name": "test_4"})
storage.add([vec1, vec2, vec3, vec4])
print("[Step 2] After add:", storage.status())
res = storage.client.query_raw(
"SELECT * FROM lyz_tb WHERE payload.name = 'test_3';"
)["result"][0]["result"][0]["id"].id
print("[Step 3] Query Result ID for 'test_3':", res)
storage.delete(ids=[res])
print("[Step 4] After delete 'test_3':", storage.status())
res = storage.query(
VectorDBQuery(query_vector=[1.1, 2.1, 3.1, 4.1], top_k=2)
)
print("[Step 5] Vector Query Result:", res)
The output should look like this:
[Step 1] After clear: vector_dim=4 vector_count=0oceanbasech
[Step 2] After add: vector_dim=4 vector_count=4
[Step 3] Query Result ID for 'test_3': lov5h16x6uog7l2xtsqp
[Step 4] After delete 'test_3': vector_dim=4 vector_count=3
[Step 5] Vector Query Result:
[VectorDBQueryResult(record=VectorRecord(vector=[],
id='9803ae3a-18da-4152-a522-48e1939a3604',
payload={'name': 'test_2'}), similarity=0.027665393972965968),
VectorDBQueryResult(record=VectorRecord(vector=[],
id='de3d085c-ed1b-4d23-9d12-d9fc64ae1e00',
payload={'name': 'test_1'}), similarity=0.00010404203326297434)]
Resources
