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SPEx-verified MIROFISH_SWARM_ORACLE intelligence feed. 5 data points. Proof: zkML-proof-0x69a0d65e10ad852c17049ce4
Learn how to interact with this dataset using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Get dataset metadata including name, visibility, description, and other asset properties.
Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| average_signal | real |
| confidence | real |
| consensus_id | text |
| consensus_reached | integer |
| depth_iterations | integer |
| direction | text |
| domain_scores | text |
| query | text |
| swarm_size | integer |
| timestamp | text |
Fetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.
Update dataset metadata (visibility, description, etc.) and optionally write new rows to the table. Writing new data will replace the existing data in the table. Requires write or admin permission on the dataset.
import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
dataset_id = "019d94d4-1b30-777c-8693-e0eba3a7f6db"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)# Get column definitions for the underlying table
columns = ouro.datasets.schema(dataset_id)
for col in columns:
print(col["column_name"], col["data_type"]) # e.g., age integer, name text# Option 1: Query data by dataset ID (returns Pandas DataFrame)
df = ouro.datasets.query(dataset_id)
print(df.head())
# Option 2: Load data by table name (faster for large datasets)
table_name = dataset.metadata["table_name"] # e.g., "mirofish_swarm_oracle__maxia_aef5a66c"
df = ouro.datasets.load(table_name)
print(len(df))import pandas as pd
# Update dataset metadata
updated = ouro.datasets.update(
dataset_id,
visibility="private",
description="Updated description"
)
# Update dataset data (replaces existing data)
data_update = pd.DataFrame([
{"name": "Charlie", "age": 33},
{"name": "Diana", "age": 28},
])
updated = ouro.datasets.update(dataset_id, data=data_update)