turbopuffer

turbopuffer Python Client

The official Python client for accessing the turbopuffer API.

Usage

• Install the turbopuffer package and set your API key.

pip install turbopuffer

Or if you're able to run C binaries for JSON encoding, use:

pip install 'turbopuffer[fast]'

• Start using the API

import turbopuffer as tpuf
tpuf.api_key = 'your-token'  # Alternatively: export=TURBOPUFFER_API_KEY=your-token

# Choose the best region for your data: https://turbopuffer.com/docs/regions
# Setting base URL, option 1 (global):
# tpuf.api_base_url = "https://gcp-us-east4.turbopuffer.com"

# Open a namespace
# Setting base URL, option 2 (per namespace. If specified, this will override the global api_base_url):
ns = tpuf.Namespace('hello_world', base_url="https://gcp-us-east4.turbopuffer.com")

# Read namespace metadata
if ns.exists():
    print(f'Namespace {ns.name} exists with {ns.dimensions()} dimensions and approximately {ns.approx_count()} rows.')

# Upsert your dataset
ns.write(
    upsert_columns={
        "id": [1, 2],
        "vector": [[0.1, 0.1], [0.2, 0.2]],
        "name": ["one", "two"]
    },
    distance_metric='euclidean_squared',
)

# Alternatively, upsert with the row-based format
ns.write(
    upsert_rows=[
        {
            "id": id,
            "vector": [id/10, id/10],
            "name": "other"
        } for id in range(3, 10)
    ],
    distance_metric='euclidean_squared',
)

# Query your dataset
results = ns.query(
    rank_by=["vector", "ANN", [0.18, 0.19]],
    top_k=10,
    filters=['And', [
        ['name', 'Glob', '*o*'],
        ['name', 'NotEq', 'other'],
    ]],
    include_attributes=['name', 'vector'],
)
print(results)
# Output:
# QueryResult(
#   rows=[
#     VectorRow(id=2, vector=[0.2, 0.2], attributes={'name': 'two'}, dist=0.00049999997),
#     VectorRow(id=1, vector=[0.1, 0.1], attributes={'name': 'one'}, dist=0.0145)
#   ]
# )

# List all namespaces
namespaces = tpuf.namespaces()
print('Total namespaces:', len(namespaces))
for namespace in namespaces:
    print('Namespace', namespace.name, 'contains approximately', namespace.approx_count(),
          'rows with', namespace.dimensions(), 'dimensions.')

# Delete rows
ns.write(deletes=[1, 2])

Endpoint Documentation

For more details on request parameters and query options, check the docs at https://turbopuffer.com/docs

Development

Run poetry install --with=test to set up the project and dependencies.

• poetry run pytest
• Bump version in turbopuffer/version.py and pyproject.toml
• git tag vX.Y.Z && git push --tags
• poetry build
• poetry publish