turbopuffer 0.1.22

Python Client for accessing the turbopuffer API

turbopuffer Python Client

The official Python client for accessing the turbopuffer API.

Usage

1. 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]

2. 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
tpuf.api_base_url = "https://gcp-us-east4.turbopuffer.com"

# Open a namespace
ns = tpuf.Namespace('hello_world')

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

# Upsert your dataset
ns.upsert(
    ids=[1, 2],
    vectors=[[0.1, 0.2], [0.3, 0.4]],
    attributes={'name': ['foo', 'foos']},
    distance_metric='cosine_distance',
)

# Alternatively, upsert using a row iterator
ns.upsert(
    {
        'id': id,
        'vector': [id/10, id/10],
        'attributes': {'name': 'food', 'num': 8}
    } for id in range(3, 10),
    distance_metric='cosine_distance',
)

# Query your dataset
vectors = ns.query(
    vector=[0.15, 0.22],
    distance_metric='cosine_distance',
    top_k=10,
    filters=['And', [
        ['name', 'Glob', 'foo*'],
        ['name', 'NotEq', 'food'],
    ]],
    include_attributes=['name'],
    include_vectors=True
)
print(vectors)
# [
#   VectorRow(id=2, vector=[0.30000001192092896, 0.4000000059604645], attributes={'name': 'foos'}, dist=0.001016080379486084),
#   VectorRow(id=1, vector=[0.10000000149011612, 0.20000000298023224], attributes={'name': 'foo'}, dist=0.009067952632904053)
# ]

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

# Delete vectors using the separate delete method
ns.delete([1, 2])

Endpoint Documentation

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

Development

1. poetry run pytest
2. Bump version in turbopuffer/version.py and pyproject.toml
3. poetry build
4. poetry publish