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feat: add create_index to the async python API (#1052)

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This also refactors the rust lancedb index builder API (and,
correspondingly, the nodejs API)
This commit is contained in:
Weston Pace
2024-03-12 05:17:05 -07:00
parent 90af5cf028
commit f822255683
38 changed files with 1329 additions and 766 deletions
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19
python/python/lancedb/__init__.py
View File

@@ -23,7 +23,8 @@ from ._lancedb import connect as lancedb_connect
from .common import URI, sanitize_uri
from .db import AsyncConnection, DBConnection, LanceDBConnection
from .remote.db import RemoteDBConnection
from .schema import vector # noqa: F401
from .schema import vector
from .table import AsyncTable
def connect(
@@ -187,3 +188,19 @@ async def connect_async(
read_consistency_interval_secs,
)
)
__all__ = [
"connect",
"connect_async",
"AsyncConnection",
"AsyncTable",
"URI",
"sanitize_uri",
"sentry_log",
"vector",
"DBConnection",
"LanceDBConnection",
"RemoteDBConnection",
"__version__",
]

19
python/python/lancedb/_lancedb.pyi
View File

@@ -2,6 +2,18 @@ from typing import Optional
import pyarrow as pa
class Index:
@staticmethod
def ivf_pq(
distance_type: Optional[str],
num_partitions: Optional[int],
num_sub_vectors: Optional[int],
max_iterations: Optional[int],
sample_rate: Optional[int],
) -> Index: ...
@staticmethod
def btree() -> Index: ...
class Connection(object):
async def table_names(
self, start_after: Optional[str], limit: Optional[int]
@@ -13,10 +25,15 @@ class Connection(object):
self, name: str, mode: str, schema: pa.Schema
) -> Table: ...
class Table(object):
class Table:
def name(self) -> str: ...
def __repr__(self) -> str: ...
async def schema(self) -> pa.Schema: ...
async def add(self, data: pa.RecordBatchReader, mode: str) -> None: ...
async def count_rows(self, filter: Optional[str]) -> int: ...
async def create_index(
self, column: str, config: Optional[Index], replace: Optional[bool]
): ...
async def connect(
uri: str,

157
python/python/lancedb/index.py Normal file
View File

@@ -0,0 +1,157 @@
from typing import Optional
from ._lancedb import (
Index as LanceDbIndex,
)
class BTree(object):
"""Describes a btree index configuration
A btree index is an index on scalar columns. The index stores a copy of the
column in sorted order. A header entry is created for each block of rows
(currently the block size is fixed at 4096). These header entries are stored
in a separate cacheable structure (a btree). To search for data the header is
used to determine which blocks need to be read from disk.
For example, a btree index in a table with 1Bi rows requires
sizeof(Scalar) * 256Ki bytes of memory and will generally need to read
sizeof(Scalar) * 4096 bytes to find the correct row ids.
This index is good for scalar columns with mostly distinct values and does best
when the query is highly selective.
The btree index does not currently have any parameters though parameters such as
the block size may be added in the future.
"""
def __init__(self):
self._inner = LanceDbIndex.btree()
class IvfPq(object):
"""Describes an IVF PQ Index
This index stores a compressed (quantized) copy of every vector. These vectors
are grouped into partitions of similar vectors. Each partition keeps track of
a centroid which is the average value of all vectors in the group.
During a query the centroids are compared with the query vector to find the
closest partitions. The compressed vectors in these partitions are then
searched to find the closest vectors.
The compression scheme is called product quantization. Each vector is divide
into subvectors and then each subvector is quantized into a small number of
bits. the parameters `num_bits` and `num_subvectors` control this process,
providing a tradeoff between index size (and thus search speed) and index
accuracy.
The partitioning process is called IVF and the `num_partitions` parameter
controls how many groups to create.
Note that training an IVF PQ index on a large dataset is a slow operation and
currently is also a memory intensive operation.
"""
def __init__(
self,
*,
distance_type: Optional[str] = None,
num_partitions: Optional[int] = None,
num_sub_vectors: Optional[int] = None,
max_iterations: Optional[int] = None,
sample_rate: Optional[int] = None,
):
"""
Create an IVF PQ index config
Parameters
----------
distance_type: str, default "L2"
The distance metric used to train the index
This is used when training the index to calculate the IVF partitions
(vectors are grouped in partitions with similar vectors according to this
distance type) and to calculate a subvector's code during quantization.
The distance type used to train an index MUST match the distance type used
to search the index. Failure to do so will yield inaccurate results.
The following distance types are available:
"l2" - Euclidean distance. This is a very common distance metric that
accounts for both magnitude and direction when determining the distance
between vectors. L2 distance has a range of [0, ∞).
"cosine" - Cosine distance. Cosine distance is a distance metric
calculated from the cosine similarity between two vectors. Cosine
similarity is a measure of similarity between two non-zero vectors of an
inner product space. It is defined to equal the cosine of the angle
between them. Unlike L2, the cosine distance is not affected by the
magnitude of the vectors. Cosine distance has a range of [0, 2].
Note: the cosine distance is undefined when one (or both) of the vectors
are all zeros (there is no direction). These vectors are invalid and may
never be returned from a vector search.
"dot" - Dot product. Dot distance is the dot product of two vectors. Dot
distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
L2 norm is 1), then dot distance is equivalent to the cosine distance.
num_partitions: int, default sqrt(num_rows)
The number of IVF partitions to create.
This value should generally scale with the number of rows in the dataset.
By default the number of partitions is the square root of the number of
rows.
If this value is too large then the first part of the search (picking the
right partition) will be slow. If this value is too small then the second
part of the search (searching within a partition) will be slow.
num_sub_vectors: int, default is vector dimension / 16
Number of sub-vectors of PQ.
This value controls how much the vector is compressed during the
quantization step. The more sub vectors there are the less the vector is
compressed. The default is the dimension of the vector divided by 16. If
the dimension is not evenly divisible by 16 we use the dimension divded by
8.
The above two cases are highly preferred. Having 8 or 16 values per
subvector allows us to use efficient SIMD instructions.
If the dimension is not visible by 8 then we use 1 subvector. This is not
ideal and will likely result in poor performance.
max_iterations: int, default 50
Max iteration to train kmeans.
When training an IVF PQ index we use kmeans to calculate the partitions.
This parameter controls how many iterations of kmeans to run.
Increasing this might improve the quality of the index but in most cases
these extra iterations have diminishing returns.
The default value is 50.
sample_rate: int, default 256
The rate used to calculate the number of training vectors for kmeans.
When an IVF PQ index is trained, we need to calculate partitions. These
are groups of vectors that are similar to each other. To do this we use an
algorithm called kmeans.
Running kmeans on a large dataset can be slow. To speed this up we run
kmeans on a random sample of the data. This parameter controls the size of
the sample. The total number of vectors used to train the index is
`sample_rate * num_partitions`.
Increasing this value might improve the quality of the index but in most
cases the default should be sufficient.
The default value is 256.
"""
self._inner = LanceDbIndex.ivf_pq(
distance_type=distance_type,
num_partitions=num_partitions,
num_sub_vectors=num_sub_vectors,
max_iterations=max_iterations,
sample_rate=sample_rate,
)

125
python/python/lancedb/table.py
View File

@@ -59,6 +59,7 @@ if TYPE_CHECKING:
from ._lancedb import Table as LanceDBTable
from .db import LanceDBConnection
from .index import BTree, IvfPq
pd = safe_import_pandas()
@@ -1909,112 +1910,48 @@ class AsyncTable:
raise NotImplementedError
async def create_index(
self,
metric="L2",
num_partitions=256,
num_sub_vectors=96,
vector_column_name: str = VECTOR_COLUMN_NAME,
replace: bool = True,
accelerator: Optional[str] = None,
index_cache_size: Optional[int] = None,
):
"""Create an index on the table.
Parameters
----------
metric: str, default "L2"
The distance metric to use when creating the index.
Valid values are "L2", "cosine", or "dot".
L2 is euclidean distance.
num_partitions: int, default 256
The number of IVF partitions to use when creating the index.
Default is 256.
num_sub_vectors: int, default 96
The number of PQ sub-vectors to use when creating the index.
Default is 96.
vector_column_name: str, default "vector"
The vector column name to create the index.
replace: bool, default True
- If True, replace the existing index if it exists.
- If False, raise an error if duplicate index exists.
accelerator: str, default None
If set, use the given accelerator to create the index.
Only support "cuda" for now.
index_cache_size : int, optional
The size of the index cache in number of entries. Default value is 256.
"""
raise NotImplementedError
async def create_scalar_index(
self,
column: str,
*,
replace: bool = True,
replace: Optional[bool] = None,
config: Optional[Union[IvfPq, BTree]] = None,
):
"""Create a scalar index on a column.
"""Create an index to speed up queries
Scalar indices, like vector indices, can be used to speed up scans. A scalar
index can speed up scans that contain filter expressions on the indexed column.
For example, the following scan will be faster if the column ``my_col`` has
a scalar index:
.. code-block:: python
import lancedb
db = lancedb.connect("/data/lance")
img_table = db.open_table("images")
my_df = img_table.search().where("my_col = 7", prefilter=True).to_pandas()
Scalar indices can also speed up scans containing a vector search and a
prefilter:
.. code-block::python
import lancedb
db = lancedb.connect("/data/lance")
img_table = db.open_table("images")
img_table.search([1, 2, 3, 4], vector_column_name="vector")
.where("my_col != 7", prefilter=True)
.to_pandas()
Scalar indices can only speed up scans for basic filters using
equality, comparison, range (e.g. ``my_col BETWEEN 0 AND 100``), and set
membership (e.g. `my_col IN (0, 1, 2)`)
Scalar indices can be used if the filter contains multiple indexed columns and
the filter criteria are AND'd or OR'd together
(e.g. ``my_col < 0 AND other_col> 100``)
Scalar indices may be used if the filter contains non-indexed columns but,
depending on the structure of the filter, they may not be usable. For example,
if the column ``not_indexed`` does not have a scalar index then the filter
``my_col = 0 OR not_indexed = 1`` will not be able to use any scalar index on
``my_col``.
**Experimental API**
Indices can be created on vector columns or scalar columns.
Indices on vector columns will speed up vector searches.
Indices on scalar columns will speed up filtering (in both
vector and non-vector searches)
Parameters
----------
column : str
The column to be indexed. Must be a boolean, integer, float,
or string column.
replace : bool, default True
Replace the existing index if it exists.
index: Index
The index to create.
Examples
--------
LanceDb supports multiple types of indices. See the static methods on
the Index class for more details.
column: str, default None
The column to index.
.. code-block:: python
When building a scalar index this must be set.
import lance
When building a vector index, this is optional. The default will look
for any columns of type fixed-size-list with floating point values. If
there is only one column of this type then it will be used. Otherwise
an error will be returned.
replace: bool, default True
Whether to replace the existing index
dataset = lance.dataset("./images.lance")
dataset.create_scalar_index("category")
If this is false, and another index already exists on the same columns
and the same name, then an error will be returned. This is true even if
that index is out of date.
The default is True
"""
raise NotImplementedError
index = None
if config is not None:
index = config._inner
await self._inner.create_index(column, index=index, replace=replace)
async def add(
self,
@@ -2058,6 +1995,8 @@ class AsyncTable:
on_bad_vectors=on_bad_vectors,
fill_value=fill_value,
)
if isinstance(data, pa.Table):
data = pa.RecordBatchReader.from_batches(data.schema, data.to_batches())
await self._inner.add(data, mode)
register_event("add")

61
python/python/tests/test_index.py Normal file
View File

@@ -0,0 +1,61 @@
from datetime import timedelta
import pyarrow as pa
import pytest
import pytest_asyncio
from lancedb import AsyncConnection, AsyncTable, connect_async
from lancedb.index import BTree, IvfPq
@pytest_asyncio.fixture
async def db_async(tmp_path) -> AsyncConnection:
return await connect_async(tmp_path, read_consistency_interval=timedelta(seconds=0))
def sample_fixed_size_list_array(nrows, dim):
vector_data = pa.array([float(i) for i in range(dim * nrows)], pa.float32())
return pa.FixedSizeListArray.from_arrays(vector_data, dim)
DIM = 8
NROWS = 256
@pytest_asyncio.fixture
async def some_table(db_async):
data = pa.Table.from_pydict(
{
"id": list(range(256)),
"vector": sample_fixed_size_list_array(NROWS, DIM),
}
)
return await db_async.create_table(
"some_table",
data,
)
@pytest.mark.asyncio
async def test_create_scalar_index(some_table: AsyncTable):
# Can create
await some_table.create_index("id")
# Can recreate if replace=True
await some_table.create_index("id", replace=True)
# Can't recreate if replace=False
with pytest.raises(RuntimeError, match="already exists"):
await some_table.create_index("id", replace=False)
# can also specify index type
await some_table.create_index("id", config=BTree())
@pytest.mark.asyncio
async def test_create_vector_index(some_table: AsyncTable):
# Can create
await some_table.create_index("vector")
# Can recreate if replace=True
await some_table.create_index("vector", replace=True)
# Can't recreate if replace=False
with pytest.raises(RuntimeError, match="already exists"):
await some_table.create_index("vector", replace=False)
# Can also specify index type
await some_table.create_index("vector", config=IvfPq(num_partitions=100))