 turbopuffer Python Client [](https://github.com/turbopuffer/turbopuffer-python/actions/workflows/ci_test.yml)
=========================
The official Python client for accessing the turbopuffer API.
Usage
-----
1. Install the turbopuffer package and set your API key.
```sh
$ pip install turbopuffer
```
Or if you're able to run C binaries for JSON encoding, use:
```sh
$ pip install turbopuffer[fast]
```
2. Start using the API
```py
import turbopuffer as tpuf
tpuf.api_key = 'your-token' # Alternatively: export=TURBOPUFFER_API_KEY=your-token
# 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
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"description": " turbopuffer Python Client [](https://github.com/turbopuffer/turbopuffer-python/actions/workflows/ci_test.yml)\n=========================\n\nThe official Python client for accessing the turbopuffer API.\n\nUsage\n-----\n\n1. Install the turbopuffer package and set your API key.\n```sh\n$ pip install turbopuffer\n```\nOr if you're able to run C binaries for JSON encoding, use:\n```sh\n$ pip install turbopuffer[fast]\n```\n\n2. Start using the API\n```py\nimport turbopuffer as tpuf\ntpuf.api_key = 'your-token' # Alternatively: export=TURBOPUFFER_API_KEY=your-token\n\n# Open a namespace\nns = tpuf.Namespace('hello_world')\n\n# Read namespace metadata\nif ns.exists():\n print(f'Namespace {ns.name} exists with {ns.dimensions()} dimensions and approximately {ns.approx_count()} vectors.')\n\n# Upsert your dataset\nns.upsert(\n ids=[1, 2],\n vectors=[[0.1, 0.2], [0.3, 0.4]],\n attributes={'name': ['foo', 'foos']},\n distance_metric='cosine_distance',\n)\n\n# Alternatively, upsert using a row iterator\nns.upsert(\n {\n 'id': id,\n 'vector': [id/10, id/10],\n 'attributes': {'name': 'food', 'num': 8}\n } for id in range(3, 10),\n distance_metric='cosine_distance',\n)\n\n# Query your dataset\nvectors = ns.query(\n vector=[0.15, 0.22],\n distance_metric='cosine_distance',\n top_k=10,\n filters=['And', [\n ['name', 'Glob', 'foo*'],\n ['name', 'NotEq', 'food'],\n ]],\n include_attributes=['name'],\n include_vectors=True\n)\nprint(vectors)\n# [\n# VectorRow(id=2, vector=[0.30000001192092896, 0.4000000059604645], attributes={'name': 'foos'}, dist=0.001016080379486084),\n# VectorRow(id=1, vector=[0.10000000149011612, 0.20000000298023224], attributes={'name': 'foo'}, dist=0.009067952632904053)\n# ]\n\n# List all namespaces\nnamespaces = tpuf.namespaces()\nprint('Total namespaces:', len(namespaces))\nfor namespace in namespaces:\n print('Namespace', namespace.name, 'contains approximately', namespace.approx_count(),\n 'vectors with', namespace.dimensions(), 'dimensions.')\n\n# Delete vectors using the separate delete method\nns.delete([1, 2])\n```\n\nEndpoint Documentation\n----------------------\n\nFor more details on request parameters and query options, check the docs at https://turbopuffer.com/docs\n",
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