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lancedb/python/python/lancedb/table.py
josca42 0fe844034d feat: enable stemming (#1356) 
Added the ability to specify tokenizer_name, when creating a full text
search index using tantivy. This enables the use of language specific
stemming.

Also updated the [guide on full text
search](https://lancedb.github.io/lancedb/fts/) with a short section on
choosing tokenizer.

Fixes #1315
2024-06-20 14:23:55 -07:00

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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import inspect
import time
from abc import ABC, abstractmethod
from dataclasses import dataclass
from datetime import timedelta
from functools import cached_property
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
Literal,
Optional,
Tuple,
Union,
)
import lance
import numpy as np
import pyarrow as pa
import pyarrow.compute as pc
import pyarrow.fs as pa_fs
from lance import LanceDataset
from lance.dependencies import _check_for_hugging_face
from lance.vector import vec_to_table
from .common import DATA, VEC, VECTOR_COLUMN_NAME
from .embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
from .merge import LanceMergeInsertBuilder
from .pydantic import LanceModel, model_to_dict
from .query import AsyncQuery, AsyncVectorQuery, LanceQueryBuilder, Query
from .util import (
fs_from_uri,
inf_vector_column_query,
join_uri,
safe_import_pandas,
safe_import_polars,
value_to_sql,
)
if TYPE_CHECKING:
import PIL
from lance.dataset import CleanupStats, ReaderLike
from ._lancedb import Table as LanceDBTable, OptimizeStats
from .db import LanceDBConnection
from .index import BTree, IndexConfig, IvfPq
pd = safe_import_pandas()
pl = safe_import_polars()
def _sanitize_data(
data,
schema: Optional[pa.Schema],
metadata: Optional[dict],
on_bad_vectors: str,
fill_value: Any,
):
if _check_for_hugging_face(data):
# Huggingface datasets
from lance.dependencies import datasets
if isinstance(data, datasets.dataset_dict.DatasetDict):
if schema is None:
schema = _schema_from_hf(data, schema)
data = _to_record_batch_generator(
_to_batches_with_split(data),
schema,
metadata,
on_bad_vectors,
fill_value,
)
elif isinstance(data, datasets.Dataset):
if schema is None:
schema = data.features.arrow_schema
data = _to_record_batch_generator(
data.data.to_batches(), schema, metadata, on_bad_vectors, fill_value
)
if isinstance(data, LanceModel):
raise ValueError("Cannot add a single LanceModel to a table. Use a list.")
if isinstance(data, list):
# convert to list of dict if data is a bunch of LanceModels
if isinstance(data[0], LanceModel):
schema = data[0].__class__.to_arrow_schema()
data = [model_to_dict(d) for d in data]
data = pa.Table.from_pylist(data, schema=schema)
else:
data = pa.Table.from_pylist(data)
elif isinstance(data, dict):
data = vec_to_table(data)
elif pd is not None and isinstance(data, pd.DataFrame):
data = pa.Table.from_pandas(data, preserve_index=False)
# Do not serialize Pandas metadata
meta = data.schema.metadata if data.schema.metadata is not None else {}
meta = {k: v for k, v in meta.items() if k != b"pandas"}
data = data.replace_schema_metadata(meta)
elif pl is not None and isinstance(data, pl.DataFrame):
data = data.to_arrow()
if isinstance(data, pa.Table):
if metadata:
data = _append_vector_col(data, metadata, schema)
metadata.update(data.schema.metadata or {})
data = data.replace_schema_metadata(metadata)
data = _sanitize_schema(
data, schema=schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
)
elif isinstance(data, Iterable):
data = _to_record_batch_generator(
data, schema, metadata, on_bad_vectors, fill_value
)
else:
raise TypeError(f"Unsupported data type: {type(data)}")
return data
def _schema_from_hf(data, schema):
"""
Extract pyarrow schema from HuggingFace DatasetDict
and validate that they're all the same schema between
splits
"""
for dataset in data.values():
if schema is None:
schema = dataset.features.arrow_schema
elif schema != dataset.features.arrow_schema:
msg = "All datasets in a HuggingFace DatasetDict must have the same schema"
raise TypeError(msg)
return schema
def _to_batches_with_split(data):
"""
Return a generator of RecordBatches from a HuggingFace DatasetDict
with an extra `split` column
"""
for key, dataset in data.items():
for batch in dataset.data.to_batches():
table = pa.Table.from_batches([batch])
if "split" not in table.column_names:
table = table.append_column(
"split", pa.array([key] * batch.num_rows, pa.string())
)
for b in table.to_batches():
yield b
def _append_vector_col(data: pa.Table, metadata: dict, schema: Optional[pa.Schema]):
"""
Use the embedding function to automatically embed the source column and add the
vector column to the table.
"""
functions = EmbeddingFunctionRegistry.get_instance().parse_functions(metadata)
for vector_column, conf in functions.items():
func = conf.function
no_vector_column = vector_column not in data.column_names
if no_vector_column or pc.all(pc.is_null(data[vector_column])).as_py():
col_data = func.compute_source_embeddings_with_retry(
data[conf.source_column]
)
if schema is not None:
dtype = schema.field(vector_column).type
else:
dtype = pa.list_(pa.float32(), len(col_data[0]))
if no_vector_column:
data = data.append_column(
pa.field(vector_column, type=dtype), pa.array(col_data, type=dtype)
)
else:
data = data.set_column(
data.column_names.index(vector_column),
pa.field(vector_column, type=dtype),
pa.array(col_data, type=dtype),
)
return data
def _to_record_batch_generator(
data: Iterable, schema, metadata, on_bad_vectors, fill_value
):
for batch in data:
# always convert to table because we need to sanitize the data
# and do things like add the vector column etc
if isinstance(batch, pa.RecordBatch):
batch = pa.Table.from_batches([batch])
batch = _sanitize_data(batch, schema, metadata, on_bad_vectors, fill_value)
for b in batch.to_batches():
yield b
class Table(ABC):
"""
A Table is a collection of Records in a LanceDB Database.
Examples
--------
Create using [DBConnection.create_table][lancedb.DBConnection.create_table]
(more examples in that method's documentation).
>>> import lancedb
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", data=[{"vector": [1.1, 1.2], "b": 2}])
>>> table.head()
pyarrow.Table
vector: fixed_size_list<item: float>[2]
child 0, item: float
b: int64
----
vector: [[[1.1,1.2]]]
b: [[2]]
Can append new data with [Table.add()][lancedb.table.Table.add].
>>> table.add([{"vector": [0.5, 1.3], "b": 4}])
Can query the table with [Table.search][lancedb.table.Table.search].
>>> table.search([0.4, 0.4]).select(["b", "vector"]).to_pandas()
b vector _distance
0 4 [0.5, 1.3] 0.82
1 2 [1.1, 1.2] 1.13
Search queries are much faster when an index is created. See
[Table.create_index][lancedb.table.Table.create_index].
"""
@property
@abstractmethod
def schema(self) -> pa.Schema:
"""The [Arrow Schema](https://arrow.apache.org/docs/python/api/datatypes.html#)
of this Table
"""
raise NotImplementedError
@abstractmethod
def count_rows(self, filter: Optional[str] = None) -> int:
"""
Count the number of rows in the table.
Parameters
----------
filter: str, optional
A SQL where clause to filter the rows to count.
"""
raise NotImplementedError
def to_pandas(self) -> "pd.DataFrame":
"""Return the table as a pandas DataFrame.
Returns
-------
pd.DataFrame
"""
return self.to_arrow().to_pandas()
@abstractmethod
def to_arrow(self) -> pa.Table:
"""Return the table as a pyarrow Table.
Returns
-------
pa.Table
"""
raise NotImplementedError
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
@abstractmethod
def create_scalar_index(
self,
column: str,
*,
replace: bool = True,
):
"""Create a scalar index on a column.
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:
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:
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**
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.
Examples
--------
import lance
dataset = lance.dataset("./images.lance")
dataset.create_scalar_index("category")
"""
raise NotImplementedError
@abstractmethod
def add(
self,
data: DATA,
mode: str = "append",
on_bad_vectors: str = "error",
fill_value: float = 0.0,
):
"""Add more data to the [Table](Table).
Parameters
----------
data: DATA
The data to insert into the table. Acceptable types are:
- dict or list-of-dict
- pandas.DataFrame
- pyarrow.Table or pyarrow.RecordBatch
mode: str
The mode to use when writing the data. Valid values are
"append" and "overwrite".
on_bad_vectors: str, default "error"
What to do if any of the vectors are not the same size or contains NaNs.
One of "error", "drop", "fill".
fill_value: float, default 0.
The value to use when filling vectors. Only used if on_bad_vectors="fill".
"""
raise NotImplementedError
def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
"""
Returns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder]
that can be used to create a "merge insert" operation
This operation can add rows, update rows, and remove rows all in a single
transaction. It is a very generic tool that can be used to create
behaviors like "insert if not exists", "update or insert (i.e. upsert)",
or even replace a portion of existing data with new data (e.g. replace
all data where month="january")
The merge insert operation works by combining new data from a
**source table** with existing data in a **target table** by using a
join. There are three categories of records.
"Matched" records are records that exist in both the source table and
the target table. "Not matched" records exist only in the source table
(e.g. these are new data) "Not matched by source" records exist only
in the target table (this is old data)
The builder returned by this method can be used to customize what
should happen for each category of data.
Please note that the data may appear to be reordered as part of this
operation. This is because updated rows will be deleted from the
dataset and then reinserted at the end with the new values.
Parameters
----------
on: Union[str, Iterable[str]]
A column (or columns) to join on. This is how records from the
source table and target table are matched. Typically this is some
kind of key or id column.
Examples
--------
>>> import lancedb
>>> data = pa.table({"a": [2, 1, 3], "b": ["a", "b", "c"]})
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", data)
>>> new_data = pa.table({"a": [2, 3, 4], "b": ["x", "y", "z"]})
>>> # Perform a "upsert" operation
>>> table.merge_insert("a") \\
... .when_matched_update_all() \\
... .when_not_matched_insert_all() \\
... .execute(new_data)
>>> # The order of new rows is non-deterministic since we use
>>> # a hash-join as part of this operation and so we sort here
>>> table.to_arrow().sort_by("a").to_pandas()
a b
0 1 b
1 2 x
2 3 y
3 4 z
"""
on = [on] if isinstance(on, str) else list(on.iter())
return LanceMergeInsertBuilder(self, on)
@abstractmethod
def search(
self,
query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
vector_column_name: Optional[str] = None,
query_type: str = "auto",
) -> LanceQueryBuilder:
"""Create a search query to find the nearest neighbors
of the given query vector. We currently support [vector search][search]
and [full-text search][experimental-full-text-search].
All query options are defined in [Query][lancedb.query.Query].
Examples
--------
>>> import lancedb
>>> db = lancedb.connect("./.lancedb")
>>> data = [
... {"original_width": 100, "caption": "bar", "vector": [0.1, 2.3, 4.5]},
... {"original_width": 2000, "caption": "foo", "vector": [0.5, 3.4, 1.3]},
... {"original_width": 3000, "caption": "test", "vector": [0.3, 6.2, 2.6]}
... ]
>>> table = db.create_table("my_table", data)
>>> query = [0.4, 1.4, 2.4]
>>> (table.search(query)
... .where("original_width > 1000", prefilter=True)
... .select(["caption", "original_width", "vector"])
... .limit(2)
... .to_pandas())
caption original_width vector _distance
0 foo 2000 [0.5, 3.4, 1.3] 5.220000
1 test 3000 [0.3, 6.2, 2.6] 23.089996
Parameters
----------
query: list/np.ndarray/str/PIL.Image.Image, default None
The targetted vector to search for.
- *default None*.
Acceptable types are: list, np.ndarray, PIL.Image.Image
- If None then the select/where/limit clauses are applied to filter
the table
vector_column_name: str, optional
The name of the vector column to search.
The vector column needs to be a pyarrow fixed size list type
- If not specified then the vector column is inferred from
the table schema
- If the table has multiple vector columns then the *vector_column_name*
needs to be specified. Otherwise, an error is raised.
query_type: str
*default "auto"*.
Acceptable types are: "vector", "fts", "hybrid", or "auto"
- If "auto" then the query type is inferred from the query;
- If `query` is a list/np.ndarray then the query type is
"vector";
- If `query` is a PIL.Image.Image then either do vector search,
or raise an error if no corresponding embedding function is found.
- If `query` is a string, then the query type is "vector" if the
table has embedding functions else the query type is "fts"
Returns
-------
LanceQueryBuilder
A query builder object representing the query.
Once executed, the query returns
- selected columns
- the vector
- and also the "_distance" column which is the distance between the query
vector and the returned vector.
"""
raise NotImplementedError
@abstractmethod
def _execute_query(
self, query: Query, batch_size: Optional[int] = None
) -> pa.RecordBatchReader:
pass
@abstractmethod
def _do_merge(
self,
merge: LanceMergeInsertBuilder,
new_data: DATA,
on_bad_vectors: str,
fill_value: float,
):
pass
@abstractmethod
def delete(self, where: str):
"""Delete rows from the table.
This can be used to delete a single row, many rows, all rows, or
sometimes no rows (if your predicate matches nothing).
Parameters
----------
where: str
The SQL where clause to use when deleting rows.
- For example, 'x = 2' or 'x IN (1, 2, 3)'.
The filter must not be empty, or it will error.
Examples
--------
>>> import lancedb
>>> data = [
... {"x": 1, "vector": [1, 2]},
... {"x": 2, "vector": [3, 4]},
... {"x": 3, "vector": [5, 6]}
... ]
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", data)
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 2 [3.0, 4.0]
2 3 [5.0, 6.0]
>>> table.delete("x = 2")
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 3 [5.0, 6.0]
If you have a list of values to delete, you can combine them into a
stringified list and use the `IN` operator:
>>> to_remove = [1, 5]
>>> to_remove = ", ".join([str(v) for v in to_remove])
>>> to_remove
'1, 5'
>>> table.delete(f"x IN ({to_remove})")
>>> table.to_pandas()
x vector
0 3 [5.0, 6.0]
"""
raise NotImplementedError
@abstractmethod
def update(
self,
where: Optional[str] = None,
values: Optional[dict] = None,
*,
values_sql: Optional[Dict[str, str]] = None,
):
"""
This can be used to update zero to all rows depending on how many
rows match the where clause. If no where clause is provided, then
all rows will be updated.
Either `values` or `values_sql` must be provided. You cannot provide
both.
Parameters
----------
where: str, optional
The SQL where clause to use when updating rows. For example, 'x = 2'
or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
values: dict, optional
The values to update. The keys are the column names and the values
are the values to set.
values_sql: dict, optional
The values to update, expressed as SQL expression strings. These can
reference existing columns. For example, {"x": "x + 1"} will increment
the x column by 1.
Examples
--------
>>> import lancedb
>>> import pandas as pd
>>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", data)
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 2 [3.0, 4.0]
2 3 [5.0, 6.0]
>>> table.update(where="x = 2", values={"vector": [10, 10]})
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 3 [5.0, 6.0]
2 2 [10.0, 10.0]
>>> table.update(values_sql={"x": "x + 1"})
>>> table.to_pandas()
x vector
0 2 [1.0, 2.0]
1 4 [5.0, 6.0]
2 3 [10.0, 10.0]
"""
raise NotImplementedError
@abstractmethod
def cleanup_old_versions(
self,
older_than: Optional[timedelta] = None,
*,
delete_unverified: bool = False,
) -> CleanupStats:
"""
Clean up old versions of the table, freeing disk space.
Note: This function is not available in LanceDb Cloud (since LanceDb
Cloud manages cleanup for you automatically)
Parameters
----------
older_than: timedelta, default None
The minimum age of the version to delete. If None, then this defaults
to two weeks.
delete_unverified: bool, default False
Because they may be part of an in-progress transaction, files newer
than 7 days old are not deleted by default. If you are sure that
there are no in-progress transactions, then you can set this to True
to delete all files older than `older_than`.
Returns
-------
CleanupStats
The stats of the cleanup operation, including how many bytes were
freed.
"""
@abstractmethod
def compact_files(self, *args, **kwargs):
"""
Run the compaction process on the table.
Note: This function is not available in LanceDb Cloud (since LanceDb
Cloud manages compaction for you automatically)
This can be run after making several small appends to optimize the table
for faster reads.
Arguments are passed onto :meth:`lance.dataset.DatasetOptimizer.compact_files`.
For most cases, the default should be fine.
"""
@abstractmethod
def add_columns(self, transforms: Dict[str, str]):
"""
Add new columns with defined values.
This is not yet available in LanceDB Cloud.
Parameters
----------
transforms: Dict[str, str]
A map of column name to a SQL expression to use to calculate the
value of the new column. These expressions will be evaluated for
each row in the table, and can reference existing columns.
"""
@abstractmethod
def alter_columns(self, alterations: Iterable[Dict[str, str]]):
"""
Alter column names and nullability.
This is not yet available in LanceDB Cloud.
alterations : Iterable[Dict[str, Any]]
A sequence of dictionaries, each with the following keys:
- "path": str
The column path to alter. For a top-level column, this is the name.
For a nested column, this is the dot-separated path, e.g. "a.b.c".
- "name": str, optional
The new name of the column. If not specified, the column name is
not changed.
- "nullable": bool, optional
Whether the column should be nullable. If not specified, the column
nullability is not changed. Only non-nullable columns can be changed
to nullable. Currently, you cannot change a nullable column to
non-nullable.
"""
@abstractmethod
def drop_columns(self, columns: Iterable[str]):
"""
Drop columns from the table.
This is not yet available in LanceDB Cloud.
Parameters
----------
columns : Iterable[str]
The names of the columns to drop.
"""
class _LanceDatasetRef(ABC):
@property
@abstractmethod
def dataset(self) -> LanceDataset:
pass
@property
@abstractmethod
def dataset_mut(self) -> LanceDataset:
pass
@dataclass
class _LanceLatestDatasetRef(_LanceDatasetRef):
"""Reference to the latest version of a LanceDataset."""
uri: str
index_cache_size: Optional[int] = None
read_consistency_interval: Optional[timedelta] = None
last_consistency_check: Optional[float] = None
_dataset: Optional[LanceDataset] = None
@property
def dataset(self) -> LanceDataset:
if not self._dataset:
self._dataset = lance.dataset(
self.uri, index_cache_size=self.index_cache_size
)
self.last_consistency_check = time.monotonic()
elif self.read_consistency_interval is not None:
now = time.monotonic()
diff = timedelta(seconds=now - self.last_consistency_check)
if (
self.last_consistency_check is None
or diff > self.read_consistency_interval
):
self._dataset = self._dataset.checkout_version(
self._dataset.latest_version
)
self.last_consistency_check = time.monotonic()
return self._dataset
@dataset.setter
def dataset(self, value: LanceDataset):
self._dataset = value
self.last_consistency_check = time.monotonic()
@property
def dataset_mut(self) -> LanceDataset:
return self.dataset
@dataclass
class _LanceTimeTravelRef(_LanceDatasetRef):
uri: str
version: int
index_cache_size: Optional[int] = None
_dataset: Optional[LanceDataset] = None
@property
def dataset(self) -> LanceDataset:
if not self._dataset:
self._dataset = lance.dataset(
self.uri, version=self.version, index_cache_size=self.index_cache_size
)
return self._dataset
@dataset.setter
def dataset(self, value: LanceDataset):
self._dataset = value
self.version = value.version
@property
def dataset_mut(self) -> LanceDataset:
raise ValueError(
"Cannot mutate table reference fixed at version "
f"{self.version}. Call checkout_latest() to get a mutable "
"table reference."
)
class LanceTable(Table):
"""
A table in a LanceDB database.
This can be opened in two modes: standard and time-travel.
Standard mode is the default. In this mode, the table is mutable and tracks
the latest version of the table. The level of read consistency is controlled
by the `read_consistency_interval` parameter on the connection.
Time-travel mode is activated by specifying a version number. In this mode,
the table is immutable and fixed to a specific version. This is useful for
querying historical versions of the table.
"""
def __init__(
self,
connection: "LanceDBConnection",
name: str,
version: Optional[int] = None,
*,
index_cache_size: Optional[int] = None,
):
self._conn = connection
self.name = name
if version is not None:
self._ref = _LanceTimeTravelRef(
uri=self._dataset_uri,
version=version,
index_cache_size=index_cache_size,
)
else:
self._ref = _LanceLatestDatasetRef(
uri=self._dataset_uri,
read_consistency_interval=connection.read_consistency_interval,
index_cache_size=index_cache_size,
)
@classmethod
def open(cls, db, name, **kwargs):
tbl = cls(db, name, **kwargs)
fs, path = fs_from_uri(tbl._dataset_uri)
file_info = fs.get_file_info(path)
if file_info.type != pa.fs.FileType.Directory:
raise FileNotFoundError(
f"Table {name} does not exist."
f"Please first call db.create_table({name}, data)"
)
return tbl
@property
def _dataset_uri(self) -> str:
return join_uri(self._conn.uri, f"{self.name}.lance")
@property
def _dataset(self) -> LanceDataset:
return self._ref.dataset
@property
def _dataset_mut(self) -> LanceDataset:
return self._ref.dataset_mut
def to_lance(self) -> LanceDataset:
"""Return the LanceDataset backing this table."""
return self._dataset
@property
def schema(self) -> pa.Schema:
"""Return the schema of the table.
Returns
-------
pa.Schema
A PyArrow schema object."""
return self._dataset.schema
def list_versions(self):
"""List all versions of the table"""
return self._dataset.versions()
@property
def version(self) -> int:
"""Get the current version of the table"""
return self._dataset.version
def checkout(self, version: int):
"""Checkout a version of the table. This is an in-place operation.
This allows viewing previous versions of the table. If you wish to
keep writing to the dataset starting from an old version, then use
the `restore` function.
Calling this method will set the table into time-travel mode. If you
wish to return to standard mode, call `checkout_latest`.
Parameters
----------
version : int
The version to checkout.
Examples
--------
>>> import lancedb
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table",
... [{"vector": [1.1, 0.9], "type": "vector"}])
>>> table.version
2
>>> table.to_pandas()
vector type
0 [1.1, 0.9] vector
>>> table.add([{"vector": [0.5, 0.2], "type": "vector"}])
>>> table.version
3
>>> table.checkout(2)
>>> table.to_pandas()
vector type
0 [1.1, 0.9] vector
"""
max_ver = self._dataset.latest_version
if version < 1 or version > max_ver:
raise ValueError(f"Invalid version {version}")
try:
ds = self._dataset.checkout_version(version)
except IOError as e:
if "not found" in str(e):
raise ValueError(
f"Version {version} no longer exists. Was it cleaned up?"
)
else:
raise e
self._ref = _LanceTimeTravelRef(
uri=self._dataset_uri,
version=version,
)
# We've already loaded the version so we can populate it directly.
self._ref.dataset = ds
def checkout_latest(self):
"""Checkout the latest version of the table. This is an in-place operation.
The table will be set back into standard mode, and will track the latest
version of the table.
"""
self.checkout(self._dataset.latest_version)
ds = self._ref.dataset
self._ref = _LanceLatestDatasetRef(
uri=self._dataset_uri,
read_consistency_interval=self._conn.read_consistency_interval,
)
self._ref.dataset = ds
def restore(self, version: int = None):
"""Restore a version of the table. This is an in-place operation.
This creates a new version where the data is equivalent to the
specified previous version. Data is not copied (as of python-v0.2.1).
Parameters
----------
version : int, default None
The version to restore. If unspecified then restores the currently
checked out version. If the currently checked out version is the
latest version then this is a no-op.
Examples
--------
>>> import lancedb
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", [
... {"vector": [1.1, 0.9], "type": "vector"}])
>>> table.version
2
>>> table.to_pandas()
vector type
0 [1.1, 0.9] vector
>>> table.add([{"vector": [0.5, 0.2], "type": "vector"}])
>>> table.version
3
>>> table.restore(2)
>>> table.to_pandas()
vector type
0 [1.1, 0.9] vector
>>> len(table.list_versions())
4
"""
max_ver = self._dataset.latest_version
if version is None:
version = self.version
elif version < 1 or version > max_ver:
raise ValueError(f"Invalid version {version}")
else:
self.checkout(version)
ds = self._dataset
# no-op if restoring the latest version
if version != max_ver:
ds.restore()
self._ref = _LanceLatestDatasetRef(
uri=self._dataset_uri,
read_consistency_interval=self._conn.read_consistency_interval,
)
self._ref.dataset = ds
def count_rows(self, filter: Optional[str] = None) -> int:
return self._dataset.count_rows(filter)
def __len__(self):
return self.count_rows()
def __repr__(self) -> str:
val = f'{self.__class__.__name__}(connection={self._conn!r}, name="{self.name}"'
if isinstance(self._ref, _LanceTimeTravelRef):
val += f", version={self._ref.version}"
val += ")"
return val
def __str__(self) -> str:
return self.__repr__()
def head(self, n=5) -> pa.Table:
"""Return the first n rows of the table."""
return self._dataset.head(n)
def to_pandas(self) -> "pd.DataFrame":
"""Return the table as a pandas DataFrame.
Returns
-------
pd.DataFrame
"""
return self.to_arrow().to_pandas()
def to_arrow(self) -> pa.Table:
"""Return the table as a pyarrow Table.
Returns
-------
pa.Table"""
return self._dataset.to_table()
def to_polars(self, batch_size=None) -> "pl.LazyFrame":
"""Return the table as a polars LazyFrame.
Parameters
----------
batch_size: int, optional
Passed to polars. This is the maximum row count for
scanned pyarrow record batches
Note
----
1. This requires polars to be installed separately
2. Currently we've disabled push-down of the filters from polars
because polars pushdown into pyarrow uses pyarrow compute
expressions rather than SQl strings (which LanceDB supports)
Returns
-------
pl.LazyFrame
"""
return pl.scan_pyarrow_dataset(
self.to_lance(), allow_pyarrow_filter=False, batch_size=batch_size
)
def create_index(
self,
metric="L2",
num_partitions=256,
num_sub_vectors=96,
vector_column_name=VECTOR_COLUMN_NAME,
replace: bool = True,
accelerator: Optional[str] = None,
index_cache_size: Optional[int] = None,
):
"""Create an index on the table."""
self._dataset_mut.create_index(
column=vector_column_name,
index_type="IVF_PQ",
metric=metric,
num_partitions=num_partitions,
num_sub_vectors=num_sub_vectors,
replace=replace,
accelerator=accelerator,
index_cache_size=index_cache_size,
)
def create_scalar_index(self, column: str, *, replace: bool = True):
self._dataset_mut.create_scalar_index(
column, index_type="BTREE", replace=replace
)
def create_fts_index(
self,
field_names: Union[str, List[str]],
ordering_field_names: Union[str, List[str]] = None,
*,
replace: bool = False,
writer_heap_size: Optional[int] = 1024 * 1024 * 1024,
tokenizer_name: str = "default",
):
"""Create a full-text search index on the table.
Warning - this API is highly experimental and is highly likely to change
in the future.
Parameters
----------
field_names: str or list of str
The name(s) of the field to index.
replace: bool, default False
If True, replace the existing index if it exists. Note that this is
not yet an atomic operation; the index will be temporarily
unavailable while the new index is being created.
writer_heap_size: int, default 1GB
ordering_field_names:
A list of unsigned type fields to index to optionally order
results on at search time
tokenizer_name: str, default "default"
The tokenizer to use for the index. Can be "raw", "default" or the 2 letter
language code followed by "_stem". So for english it would be "en_stem".
For available languages see: https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html
"""
from .fts import create_index, populate_index
if isinstance(field_names, str):
field_names = [field_names]
if isinstance(ordering_field_names, str):
ordering_field_names = [ordering_field_names]
fs, path = fs_from_uri(self._get_fts_index_path())
index_exists = fs.get_file_info(path).type != pa_fs.FileType.NotFound
if index_exists:
if not replace:
raise ValueError("Index already exists. Use replace=True to overwrite.")
fs.delete_dir(path)
if not isinstance(fs, pa_fs.LocalFileSystem):
raise NotImplementedError(
"Full-text search is only supported on the local filesystem"
)
index = create_index(
self._get_fts_index_path(),
field_names,
ordering_fields=ordering_field_names,
tokenizer_name=tokenizer_name,
)
populate_index(
index,
self,
field_names,
ordering_fields=ordering_field_names,
writer_heap_size=writer_heap_size,
)
def _get_fts_index_path(self):
return join_uri(self._dataset_uri, "_indices", "tantivy")
def add(
self,
data: DATA,
mode: str = "append",
on_bad_vectors: str = "error",
fill_value: float = 0.0,
):
"""Add data to the table.
If vector columns are missing and the table
has embedding functions, then the vector columns
are automatically computed and added.
Parameters
----------
data: list-of-dict, dict, pd.DataFrame
The data to insert into the table.
mode: str
The mode to use when writing the data. Valid values are
"append" and "overwrite".
on_bad_vectors: str, default "error"
What to do if any of the vectors are not the same size or contains NaNs.
One of "error", "drop", "fill".
fill_value: float, default 0.
The value to use when filling vectors. Only used if on_bad_vectors="fill".
Returns
-------
int
The number of vectors in the table.
"""
# TODO: manage table listing and metadata separately
data = _sanitize_data(
data,
self.schema,
metadata=self.schema.metadata,
on_bad_vectors=on_bad_vectors,
fill_value=fill_value,
)
# Access the dataset_mut property to ensure that the dataset is mutable.
self._ref.dataset_mut
self._ref.dataset = lance.write_dataset(
data, self._dataset_uri, schema=self.schema, mode=mode
)
def merge(
self,
other_table: Union[LanceTable, ReaderLike],
left_on: str,
right_on: Optional[str] = None,
schema: Optional[Union[pa.Schema, LanceModel]] = None,
):
"""Merge another table into this table.
Performs a left join, where the dataset is the left side and other_table
is the right side. Rows existing in the dataset but not on the left will
be filled with null values, unless Lance doesn't support null values for
some types, in which case an error will be raised. The only overlapping
column allowed is the join column. If other overlapping columns exist,
an error will be raised.
Parameters
----------
other_table: LanceTable or Reader-like
The data to be merged. Acceptable types are:
- Pandas DataFrame, Pyarrow Table, Dataset, Scanner,
Iterator[RecordBatch], or RecordBatchReader
- LanceTable
left_on: str
The name of the column in the dataset to join on.
right_on: str or None
The name of the column in other_table to join on. If None, defaults to
left_on.
schema: pa.Schema or LanceModel, optional
The schema of the other_table.
If not provided, the schema is inferred from the data.
Examples
--------
>>> import lancedb
>>> import pyarrow as pa
>>> df = pa.table({'x': [1, 2, 3], 'y': ['a', 'b', 'c']})
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("dataset", df)
>>> table.to_pandas()
x y
0 1 a
1 2 b
2 3 c
>>> new_df = pa.table({'x': [1, 2, 3], 'z': ['d', 'e', 'f']})
>>> table.merge(new_df, 'x')
>>> table.to_pandas()
x y z
0 1 a d
1 2 b e
2 3 c f
"""
if isinstance(schema, LanceModel):
schema = schema.to_arrow_schema()
if isinstance(other_table, LanceTable):
other_table = other_table.to_lance()
if isinstance(other_table, LanceDataset):
other_table = other_table.to_table()
self._ref.dataset = self._dataset_mut.merge(
other_table, left_on=left_on, right_on=right_on, schema=schema
)
@cached_property
def embedding_functions(self) -> dict:
"""
Get the embedding functions for the table
Returns
-------
funcs: dict
A mapping of the vector column to the embedding function
or empty dict if not configured.
"""
return EmbeddingFunctionRegistry.get_instance().parse_functions(
self.schema.metadata
)
def search(
self,
query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
vector_column_name: Optional[str] = None,
query_type: str = "auto",
ordering_field_name: Optional[str] = None,
) -> LanceQueryBuilder:
"""Create a search query to find the nearest neighbors
of the given query vector. We currently support [vector search][search]
and [full-text search][search].
Examples
--------
>>> import lancedb
>>> db = lancedb.connect("./.lancedb")
>>> data = [
... {"original_width": 100, "caption": "bar", "vector": [0.1, 2.3, 4.5]},
... {"original_width": 2000, "caption": "foo", "vector": [0.5, 3.4, 1.3]},
... {"original_width": 3000, "caption": "test", "vector": [0.3, 6.2, 2.6]}
... ]
>>> table = db.create_table("my_table", data)
>>> query = [0.4, 1.4, 2.4]
>>> (table.search(query)
... .where("original_width > 1000", prefilter=True)
... .select(["caption", "original_width", "vector"])
... .limit(2)
... .to_pandas())
caption original_width vector _distance
0 foo 2000 [0.5, 3.4, 1.3] 5.220000
1 test 3000 [0.3, 6.2, 2.6] 23.089996
Parameters
----------
query: list/np.ndarray/str/PIL.Image.Image, default None
The targetted vector to search for.
- *default None*.
Acceptable types are: list, np.ndarray, PIL.Image.Image
- If None then the select/[where][sql]/limit clauses are applied
to filter the table
vector_column_name: str, optional
The name of the vector column to search.
The vector column needs to be a pyarrow fixed size list type
*default "vector"*
- If not specified then the vector column is inferred from
the table schema
- If the table has multiple vector columns then the *vector_column_name*
needs to be specified. Otherwise, an error is raised.
query_type: str, default "auto"
"vector", "fts", or "auto"
If "auto" then the query type is inferred from the query;
If `query` is a list/np.ndarray then the query type is "vector";
If `query` is a PIL.Image.Image then either do vector search
or raise an error if no corresponding embedding function is found.
If the `query` is a string, then the query type is "vector" if the
table has embedding functions, else the query type is "fts"
Returns
-------
LanceQueryBuilder
A query builder object representing the query.
Once executed, the query returns selected columns, the vector,
and also the "_distance" column which is the distance between the query
vector and the returned vector.
"""
if vector_column_name is None and query is not None:
try:
vector_column_name = inf_vector_column_query(self.schema)
except Exception as e:
if query_type == "fts":
vector_column_name = ""
else:
raise e
return LanceQueryBuilder.create(
self,
query,
query_type,
vector_column_name=vector_column_name,
ordering_field_name=ordering_field_name,
)
@classmethod
def create(
cls,
db,
name,
data=None,
schema=None,
mode="create",
exist_ok=False,
on_bad_vectors: str = "error",
fill_value: float = 0.0,
embedding_functions: List[EmbeddingFunctionConfig] = None,
):
"""
Create a new table.
Examples
--------
>>> import lancedb
>>> data = [
... {"x": 1, "vector": [1, 2]},
... {"x": 2, "vector": [3, 4]},
... {"x": 3, "vector": [5, 6]}
... ]
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", data)
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 2 [3.0, 4.0]
2 3 [5.0, 6.0]
Parameters
----------
db: LanceDB
The LanceDB instance to create the table in.
name: str
The name of the table to create.
data: list-of-dict, dict, pd.DataFrame, default None
The data to insert into the table.
At least one of `data` or `schema` must be provided.
schema: pa.Schema or LanceModel, optional
The schema of the table. If not provided,
the schema is inferred from the data.
At least one of `data` or `schema` must be provided.
mode: str, default "create"
The mode to use when writing the data. Valid values are
"create", "overwrite", and "append".
exist_ok: bool, default False
If the table already exists then raise an error if False,
otherwise just open the table, it will not add the provided
data but will validate against any schema that's specified.
on_bad_vectors: str, default "error"
What to do if any of the vectors are not the same size or contains NaNs.
One of "error", "drop", "fill".
fill_value: float, default 0.
The value to use when filling vectors. Only used if on_bad_vectors="fill".
embedding_functions: list of EmbeddingFunctionModel, default None
The embedding functions to use when creating the table.
"""
tbl = LanceTable(db, name)
if inspect.isclass(schema) and issubclass(schema, LanceModel):
# convert LanceModel to pyarrow schema
# note that it's possible this contains
# embedding function metadata already
schema = schema.to_arrow_schema()
metadata = None
if embedding_functions is not None:
# If we passed in embedding functions explicitly
# then we'll override any schema metadata that
# may was implicitly specified by the LanceModel schema
registry = EmbeddingFunctionRegistry.get_instance()
metadata = registry.get_table_metadata(embedding_functions)
if data is not None:
data = _sanitize_data(
data,
schema,
metadata=metadata,
on_bad_vectors=on_bad_vectors,
fill_value=fill_value,
)
if schema is None:
if data is None:
raise ValueError("Either data or schema must be provided")
elif hasattr(data, "schema"):
schema = data.schema
elif isinstance(data, Iterable):
if metadata:
raise TypeError(
(
"Persistent embedding functions not yet "
"supported for generator data input"
)
)
if metadata:
schema = schema.with_metadata(metadata)
empty = pa.Table.from_pylist([], schema=schema)
try:
lance.write_dataset(empty, tbl._dataset_uri, schema=schema, mode=mode)
except OSError as err:
if "Dataset already exists" in str(err) and exist_ok:
if tbl.schema != schema:
raise ValueError(
f"Table {name} already exists with a different schema"
)
return tbl
raise
new_table = LanceTable(db, name)
if data is not None:
new_table.add(data)
return new_table
def delete(self, where: str):
self._dataset_mut.delete(where)
def update(
self,
where: Optional[str] = None,
values: Optional[dict] = None,
*,
values_sql: Optional[Dict[str, str]] = None,
):
"""
This can be used to update zero to all rows depending on how many
rows match the where clause.
Parameters
----------
where: str, optional
The SQL where clause to use when updating rows. For example, 'x = 2'
or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
values: dict, optional
The values to update. The keys are the column names and the values
are the values to set.
values_sql: dict, optional
The values to update, expressed as SQL expression strings. These can
reference existing columns. For example, {"x": "x + 1"} will increment
the x column by 1.
Examples
--------
>>> import lancedb
>>> import pandas as pd
>>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", data)
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 2 [3.0, 4.0]
2 3 [5.0, 6.0]
>>> table.update(where="x = 2", values={"vector": [10, 10]})
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 3 [5.0, 6.0]
2 2 [10.0, 10.0]
"""
if values is not None and values_sql is not None:
raise ValueError("Only one of values or values_sql can be provided")
if values is None and values_sql is None:
raise ValueError("Either values or values_sql must be provided")
if values is not None:
values_sql = {k: value_to_sql(v) for k, v in values.items()}
self._dataset_mut.update(values_sql, where)
def _execute_query(
self, query: Query, batch_size: Optional[int] = None
) -> pa.RecordBatchReader:
ds = self.to_lance()
return ds.scanner(
columns=query.columns,
filter=query.filter,
prefilter=query.prefilter,
nearest={
"column": query.vector_column,
"q": query.vector,
"k": query.k,
"metric": query.metric,
"nprobes": query.nprobes,
"refine_factor": query.refine_factor,
},
with_row_id=query.with_row_id,
batch_size=batch_size,
).to_reader()
def _do_merge(
self,
merge: LanceMergeInsertBuilder,
new_data: DATA,
on_bad_vectors: str,
fill_value: float,
):
new_data = _sanitize_data(
new_data,
self.schema,
metadata=self.schema.metadata,
on_bad_vectors=on_bad_vectors,
fill_value=fill_value,
)
ds = self.to_lance()
builder = ds.merge_insert(merge._on)
if merge._when_matched_update_all:
builder.when_matched_update_all(merge._when_matched_update_all_condition)
if merge._when_not_matched_insert_all:
builder.when_not_matched_insert_all()
if merge._when_not_matched_by_source_delete:
cond = merge._when_not_matched_by_source_condition
builder.when_not_matched_by_source_delete(cond)
builder.execute(new_data)
def cleanup_old_versions(
self,
older_than: Optional[timedelta] = None,
*,
delete_unverified: bool = False,
) -> CleanupStats:
"""
Clean up old versions of the table, freeing disk space.
Parameters
----------
older_than: timedelta, default None
The minimum age of the version to delete. If None, then this defaults
to two weeks.
delete_unverified: bool, default False
Because they may be part of an in-progress transaction, files newer
than 7 days old are not deleted by default. If you are sure that
there are no in-progress transactions, then you can set this to True
to delete all files older than `older_than`.
Returns
-------
CleanupStats
The stats of the cleanup operation, including how many bytes were
freed.
"""
return self.to_lance().cleanup_old_versions(
older_than, delete_unverified=delete_unverified
)
def compact_files(self, *args, **kwargs):
"""
Run the compaction process on the table.
This can be run after making several small appends to optimize the table
for faster reads.
Arguments are passed onto `lance.dataset.DatasetOptimizer.compact_files`.
(see Lance documentation for more details) For most cases, the default
should be fine.
"""
return self.to_lance().optimize.compact_files(*args, **kwargs)
def add_columns(self, transforms: Dict[str, str]):
self._dataset_mut.add_columns(transforms)
def alter_columns(self, *alterations: Iterable[Dict[str, str]]):
modified = []
# I called this name in pylance, but I think I regret that now. So we
# allow both name and rename.
for alter in alterations:
if "rename" in alter:
alter["name"] = alter.pop("rename")
modified.append(alter)
self._dataset_mut.alter_columns(*modified)
def drop_columns(self, columns: Iterable[str]):
self._dataset_mut.drop_columns(columns)
def _sanitize_schema(
data: pa.Table,
schema: pa.Schema = None,
on_bad_vectors: str = "error",
fill_value: float = 0.0,
) -> pa.Table:
"""Ensure that the table has the expected schema.
Parameters
----------
data: pa.Table
The table to sanitize.
schema: pa.Schema; optional
The expected schema. If not provided, this just converts the
vector column to fixed_size_list(float32) if necessary.
on_bad_vectors: str, default "error"
What to do if any of the vectors are not the same size or contains NaNs.
One of "error", "drop", "fill".
fill_value: float, default 0.
The value to use when filling vectors. Only used if on_bad_vectors="fill".
"""
if schema is not None:
if data.schema == schema:
return data
# cast the columns to the expected types
data = data.combine_chunks()
for field in schema:
# TODO: we're making an assumption that fixed size list of 10 or more
# is a vector column. This is definitely a bit hacky.
likely_vector_col = (
pa.types.is_fixed_size_list(field.type)
and pa.types.is_float32(field.type.value_type)
and field.type.list_size >= 10
)
is_default_vector_col = field.name == VECTOR_COLUMN_NAME
if field.name in data.column_names and (
likely_vector_col or is_default_vector_col
):
data = _sanitize_vector_column(
data,
vector_column_name=field.name,
on_bad_vectors=on_bad_vectors,
fill_value=fill_value,
)
return pa.Table.from_arrays(
[data[name] for name in schema.names], schema=schema
)
# just check the vector column
if VECTOR_COLUMN_NAME in data.column_names:
return _sanitize_vector_column(
data,
vector_column_name=VECTOR_COLUMN_NAME,
on_bad_vectors=on_bad_vectors,
fill_value=fill_value,
)
return data
def _sanitize_vector_column(
data: pa.Table,
vector_column_name: str,
on_bad_vectors: str = "error",
fill_value: float = 0.0,
) -> pa.Table:
"""
Ensure that the vector column exists and has type fixed_size_list(float32)
Parameters
----------
data: pa.Table
The table to sanitize.
vector_column_name: str
The name of the vector column.
on_bad_vectors: str, default "error"
What to do if any of the vectors are not the same size or contains NaNs.
One of "error", "drop", "fill".
fill_value: float, default 0.0
The value to use when filling vectors. Only used if on_bad_vectors="fill".
"""
# ChunkedArray is annoying to work with, so we combine chunks here
vec_arr = data[vector_column_name].combine_chunks()
typ = data[vector_column_name].type
if pa.types.is_list(typ) or pa.types.is_large_list(typ):
# if it's a variable size list array,
# we make sure the dimensions are all the same
has_jagged_ndims = len(vec_arr.values) % len(data) != 0
if has_jagged_ndims:
data = _sanitize_jagged(
data, fill_value, on_bad_vectors, vec_arr, vector_column_name
)
vec_arr = data[vector_column_name].combine_chunks()
elif not pa.types.is_fixed_size_list(vec_arr.type):
raise TypeError(f"Unsupported vector column type: {vec_arr.type}")
vec_arr = ensure_fixed_size_list(vec_arr)
data = data.set_column(
data.column_names.index(vector_column_name), vector_column_name, vec_arr
)
# Use numpy to check for NaNs, because as pyarrow 14.0.2 does not have `is_nan`
# kernel over f16 types.
values_np = vec_arr.values.to_numpy(zero_copy_only=False)
if np.isnan(values_np).any():
data = _sanitize_nans(
data, fill_value, on_bad_vectors, vec_arr, vector_column_name
)
return data
def ensure_fixed_size_list(vec_arr) -> pa.FixedSizeListArray:
values = vec_arr.values
if not (pa.types.is_float16(values.type) or pa.types.is_float32(values.type)):
values = values.cast(pa.float32())
if pa.types.is_fixed_size_list(vec_arr.type):
list_size = vec_arr.type.list_size
else:
list_size = len(values) / len(vec_arr)
vec_arr = pa.FixedSizeListArray.from_arrays(values, list_size)
return vec_arr
def _sanitize_jagged(data, fill_value, on_bad_vectors, vec_arr, vector_column_name):
"""Sanitize jagged vectors."""
if on_bad_vectors == "error":
raise ValueError(
f"Vector column {vector_column_name} has variable length vectors "
"Set on_bad_vectors='drop' to remove them, or "
"set on_bad_vectors='fill' and fill_value=<value> to replace them."
)
lst_lengths = pc.list_value_length(vec_arr)
ndims = pc.max(lst_lengths).as_py()
correct_ndims = pc.equal(lst_lengths, ndims)
if on_bad_vectors == "fill":
if fill_value is None:
raise ValueError(
"`fill_value` must not be None if `on_bad_vectors` is 'fill'"
)
fill_arr = pa.scalar([float(fill_value)] * ndims)
vec_arr = pc.if_else(correct_ndims, vec_arr, fill_arr)
data = data.set_column(
data.column_names.index(vector_column_name), vector_column_name, vec_arr
)
elif on_bad_vectors == "drop":
data = data.filter(correct_ndims)
return data
def _sanitize_nans(data, fill_value, on_bad_vectors, vec_arr, vector_column_name):
"""Sanitize NaNs in vectors"""
if on_bad_vectors == "error":
raise ValueError(
f"Vector column {vector_column_name} has NaNs. "
"Set on_bad_vectors='drop' to remove them, or "
"set on_bad_vectors='fill' and fill_value=<value> to replace them."
)
elif on_bad_vectors == "fill":
if fill_value is None:
raise ValueError(
"`fill_value` must not be None if `on_bad_vectors` is 'fill'"
)
fill_value = float(fill_value)
values = pc.if_else(pc.is_nan(vec_arr.values), fill_value, vec_arr.values)
ndims = len(vec_arr[0])
vec_arr = pa.FixedSizeListArray.from_arrays(values, ndims)
data = data.set_column(
data.column_names.index(vector_column_name), vector_column_name, vec_arr
)
elif on_bad_vectors == "drop":
is_value_nan = pc.is_nan(vec_arr.values).to_numpy(zero_copy_only=False)
is_full = np.any(~is_value_nan.reshape(-1, vec_arr.type.list_size), axis=1)
data = data.filter(is_full)
return data
class AsyncTable:
"""
An AsyncTable is a collection of Records in a LanceDB Database.
An AsyncTable can be obtained from the
[AsyncConnection.create_table][lancedb.AsyncConnection.create_table] and
[AsyncConnection.open_table][lancedb.AsyncConnection.open_table] methods.
An AsyncTable object is expected to be long lived and reused for multiple
operations. AsyncTable objects will cache a certain amount of index data in memory.
This cache will be freed when the Table is garbage collected. To eagerly free the
cache you can call the [close][lancedb.AsyncTable.close] method. Once the
AsyncTable is closed, it cannot be used for any further operations.
An AsyncTable can also be used as a context manager, and will automatically close
when the context is exited. Closing a table is optional. If you do not close the
table, it will be closed when the AsyncTable object is garbage collected.
Examples
--------
Create using [AsyncConnection.create_table][lancedb.AsyncConnection.create_table]
(more examples in that method's documentation).
>>> import lancedb
>>> async def create_a_table():
... db = await lancedb.connect_async("./.lancedb")
... data = [{"vector": [1.1, 1.2], "b": 2}]
... table = await db.create_table("my_table", data=data)
... print(await table.query().limit(5).to_arrow())
>>> import asyncio
>>> asyncio.run(create_a_table())
pyarrow.Table
vector: fixed_size_list<item: float>[2]
child 0, item: float
b: int64
----
vector: [[[1.1,1.2]]]
b: [[2]]
Can append new data with [AsyncTable.add()][lancedb.table.AsyncTable.add].
>>> async def add_to_table():
... db = await lancedb.connect_async("./.lancedb")
... table = await db.open_table("my_table")
... await table.add([{"vector": [0.5, 1.3], "b": 4}])
>>> asyncio.run(add_to_table())
Can query the table with
[AsyncTable.vector_search][lancedb.table.AsyncTable.vector_search].
>>> async def search_table_for_vector():
... db = await lancedb.connect_async("./.lancedb")
... table = await db.open_table("my_table")
... results = (
... await table.vector_search([0.4, 0.4]).select(["b", "vector"]).to_pandas()
... )
... print(results)
>>> asyncio.run(search_table_for_vector())
b vector _distance
0 4 [0.5, 1.3] 0.82
1 2 [1.1, 1.2] 1.13
Search queries are much faster when an index is created. See
[AsyncTable.create_index][lancedb.table.AsyncTable.create_index].
"""
def __init__(self, table: LanceDBTable):
"""Create a new AsyncTable object.
You should not create AsyncTable objects directly.
Use [AsyncConnection.create_table][lancedb.AsyncConnection.create_table] and
[AsyncConnection.open_table][lancedb.AsyncConnection.open_table] to obtain
Table objects."""
self._inner = table
def __repr__(self):
return self._inner.__repr__()
def __enter__(self):
return self
def __exit__(self, *_):
self.close()
def is_open(self) -> bool:
"""Return True if the table is closed."""
return self._inner.is_open()
def close(self):
"""Close the table and free any resources associated with it.
It is safe to call this method multiple times.
Any attempt to use the table after it has been closed will raise an error."""
return self._inner.close()
@property
def name(self) -> str:
"""The name of the table."""
return self._inner.name()
async def schema(self) -> pa.Schema:
"""The [Arrow Schema](https://arrow.apache.org/docs/python/api/datatypes.html#)
of this Table
"""
return await self._inner.schema()
async def count_rows(self, filter: Optional[str] = None) -> int:
"""
Count the number of rows in the table.
Parameters
----------
filter: str, optional
A SQL where clause to filter the rows to count.
"""
return await self._inner.count_rows(filter)
def query(self) -> AsyncQuery:
"""
Returns an [AsyncQuery][lancedb.query.AsyncQuery] that can be used
to search the table.
Use methods on the returned query to control query behavior. The query
can be executed with methods like [to_arrow][lancedb.query.AsyncQuery.to_arrow],
[to_pandas][lancedb.query.AsyncQuery.to_pandas] and more.
"""
return AsyncQuery(self._inner.query())
async def to_pandas(self) -> "pd.DataFrame":
"""Return the table as a pandas DataFrame.
Returns
-------
pd.DataFrame
"""
return (await self.to_arrow()).to_pandas()
async def to_arrow(self) -> pa.Table:
"""Return the table as a pyarrow Table.
Returns
-------
pa.Table
"""
return await self.query().to_arrow()
async def create_index(
self,
column: str,
*,
replace: Optional[bool] = None,
config: Optional[Union[IvfPq, BTree]] = None,
):
"""Create an index to speed up queries
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 index.
replace: bool, default True
Whether to replace the existing index
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
config: Union[IvfPq, BTree], default None
For advanced configuration you can specify the type of index you would
like to create. You can also specify index-specific parameters when
creating an index object.
"""
index = None
if config is not None:
index = config._inner
await self._inner.create_index(column, index=index, replace=replace)
async def add(
self,
data: DATA,
*,
mode: Optional[Literal["append", "overwrite"]] = "append",
on_bad_vectors: Optional[str] = None,
fill_value: Optional[float] = None,
):
"""Add more data to the [Table](Table).
Parameters
----------
data: DATA
The data to insert into the table. Acceptable types are:
- dict or list-of-dict
- pandas.DataFrame
- pyarrow.Table or pyarrow.RecordBatch
mode: str
The mode to use when writing the data. Valid values are
"append" and "overwrite".
on_bad_vectors: str, default "error"
What to do if any of the vectors are not the same size or contains NaNs.
One of "error", "drop", "fill".
fill_value: float, default 0.
The value to use when filling vectors. Only used if on_bad_vectors="fill".
"""
schema = await self.schema()
if on_bad_vectors is None:
on_bad_vectors = "error"
if fill_value is None:
fill_value = 0.0
data = _sanitize_data(
data,
schema,
metadata=schema.metadata,
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)
def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
"""
Returns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder]
that can be used to create a "merge insert" operation
This operation can add rows, update rows, and remove rows all in a single
transaction. It is a very generic tool that can be used to create
behaviors like "insert if not exists", "update or insert (i.e. upsert)",
or even replace a portion of existing data with new data (e.g. replace
all data where month="january")
The merge insert operation works by combining new data from a
**source table** with existing data in a **target table** by using a
join. There are three categories of records.
"Matched" records are records that exist in both the source table and
the target table. "Not matched" records exist only in the source table
(e.g. these are new data) "Not matched by source" records exist only
in the target table (this is old data)
The builder returned by this method can be used to customize what
should happen for each category of data.
Please note that the data may appear to be reordered as part of this
operation. This is because updated rows will be deleted from the
dataset and then reinserted at the end with the new values.
Parameters
----------
on: Union[str, Iterable[str]]
A column (or columns) to join on. This is how records from the
source table and target table are matched. Typically this is some
kind of key or id column.
Examples
--------
>>> import lancedb
>>> data = pa.table({"a": [2, 1, 3], "b": ["a", "b", "c"]})
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", data)
>>> new_data = pa.table({"a": [2, 3, 4], "b": ["x", "y", "z"]})
>>> # Perform a "upsert" operation
>>> table.merge_insert("a") \\
... .when_matched_update_all() \\
... .when_not_matched_insert_all() \\
... .execute(new_data)
>>> # The order of new rows is non-deterministic since we use
>>> # a hash-join as part of this operation and so we sort here
>>> table.to_arrow().sort_by("a").to_pandas()
a b
0 1 b
1 2 x
2 3 y
3 4 z
"""
on = [on] if isinstance(on, str) else list(on.iter())
return LanceMergeInsertBuilder(self, on)
def vector_search(
self,
query_vector: Optional[Union[VEC, Tuple]] = None,
) -> AsyncVectorQuery:
"""
Search the table with a given query vector.
This is a convenience method for preparing a vector query and
is the same thing as calling `nearestTo` on the builder returned
by `query`. Seer [nearest_to][lancedb.query.AsyncQuery.nearest_to] for more
details.
"""
return self.query().nearest_to(query_vector)
async def _execute_query(
self, query: Query, batch_size: Optional[int] = None
) -> pa.RecordBatchReader:
pass
async def _do_merge(
self,
merge: LanceMergeInsertBuilder,
new_data: DATA,
on_bad_vectors: str,
fill_value: float,
):
pass
async def delete(self, where: str):
"""Delete rows from the table.
This can be used to delete a single row, many rows, all rows, or
sometimes no rows (if your predicate matches nothing).
Parameters
----------
where: str
The SQL where clause to use when deleting rows.
- For example, 'x = 2' or 'x IN (1, 2, 3)'.
The filter must not be empty, or it will error.
Examples
--------
>>> import lancedb
>>> data = [
... {"x": 1, "vector": [1, 2]},
... {"x": 2, "vector": [3, 4]},
... {"x": 3, "vector": [5, 6]}
... ]
>>> db = lancedb.connect("./.lancedb")
>>> table = db.create_table("my_table", data)
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 2 [3.0, 4.0]
2 3 [5.0, 6.0]
>>> table.delete("x = 2")
>>> table.to_pandas()
x vector
0 1 [1.0, 2.0]
1 3 [5.0, 6.0]
If you have a list of values to delete, you can combine them into a
stringified list and use the `IN` operator:
>>> to_remove = [1, 5]
>>> to_remove = ", ".join([str(v) for v in to_remove])
>>> to_remove
'1, 5'
>>> table.delete(f"x IN ({to_remove})")
>>> table.to_pandas()
x vector
0 3 [5.0, 6.0]
"""
return await self._inner.delete(where)
async def update(
self,
updates: Optional[Dict[str, Any]] = None,
*,
where: Optional[str] = None,
updates_sql: Optional[Dict[str, str]] = None,
):
"""
This can be used to update zero to all rows in the table.
If a filter is provided with `where` then only rows matching the
filter will be updated. Otherwise all rows will be updated.
Parameters
----------
updates: dict, optional
The updates to apply. The keys should be the name of the column to
update. The values should be the new values to assign. This is
required unless updates_sql is supplied.
where: str, optional
An SQL filter that controls which rows are updated. For example, 'x = 2'
or 'x IN (1, 2, 3)'. Only rows that satisfy this filter will be udpated.
updates_sql: dict, optional
The updates to apply, expressed as SQL expression strings. The keys should
be column names. The values should be SQL expressions. These can be SQL
literals (e.g. "7" or "'foo'") or they can be expressions based on the
previous value of the row (e.g. "x + 1" to increment the x column by 1)
Examples
--------
>>> import asyncio
>>> import lancedb
>>> import pandas as pd
>>> async def demo_update():
... data = pd.DataFrame({"x": [1, 2], "vector": [[1, 2], [3, 4]]})
... db = await lancedb.connect_async("./.lancedb")
... table = await db.create_table("my_table", data)
... # x is [1, 2], vector is [[1, 2], [3, 4]]
... await table.update({"vector": [10, 10]}, where="x = 2")
... # x is [1, 2], vector is [[1, 2], [10, 10]]
... await table.update(updates_sql={"x": "x + 1"})
... # x is [2, 3], vector is [[1, 2], [10, 10]]
>>> asyncio.run(demo_update())
"""
if updates is not None and updates_sql is not None:
raise ValueError("Only one of updates or updates_sql can be provided")
if updates is None and updates_sql is None:
raise ValueError("Either updates or updates_sql must be provided")
if updates is not None:
updates_sql = {k: value_to_sql(v) for k, v in updates.items()}
return await self._inner.update(updates_sql, where)
async def version(self) -> int:
"""
Retrieve the version of the table
LanceDb supports versioning. Every operation that modifies the table increases
version. As long as a version hasn't been deleted you can `[Self::checkout]`
that version to view the data at that point. In addition, you can
`[Self::restore]` the version to replace the current table with a previous
version.
"""
return await self._inner.version()
async def checkout(self, version):
"""
Checks out a specific version of the Table
Any read operation on the table will now access the data at the checked out
version. As a consequence, calling this method will disable any read consistency
interval that was previously set.
This is a read-only operation that turns the table into a sort of "view"
or "detached head". Other table instances will not be affected. To make the
change permanent you can use the `[Self::restore]` method.
Any operation that modifies the table will fail while the table is in a checked
out state.
To return the table to a normal state use `[Self::checkout_latest]`
"""
await self._inner.checkout(version)
async def checkout_latest(self):
"""
Ensures the table is pointing at the latest version
This can be used to manually update a table when the read_consistency_interval
is None
It can also be used to undo a `[Self::checkout]` operation
"""
await self._inner.checkout_latest()
async def restore(self):
"""
Restore the table to the currently checked out version
This operation will fail if checkout has not been called previously
This operation will overwrite the latest version of the table with a
previous version. Any changes made since the checked out version will
no longer be visible.
Once the operation concludes the table will no longer be in a checked
out state and the read_consistency_interval, if any, will apply.
"""
await self._inner.restore()
async def optimize(
self, *, cleanup_older_than: Optional[timedelta] = None
) -> OptimizeStats:
"""
Optimize the on-disk data and indices for better performance.
Modeled after ``VACUUM`` in PostgreSQL.
Optimization covers three operations:
* Compaction: Merges small files into larger ones
* Prune: Removes old versions of the dataset
* Index: Optimizes the indices, adding new data to existing indices
Parameters
----------
cleanup_older_than: timedelta, optional default 7 days
All files belonging to versions older than this will be removed. Set
to 0 days to remove all versions except the latest. The latest version
is never removed.
Experimental API
----------------
The optimization process is undergoing active development and may change.
Our goal with these changes is to improve the performance of optimization and
reduce the complexity.
That being said, it is essential today to run optimize if you want the best
performance. It should be stable and safe to use in production, but it our
hope that the API may be simplified (or not even need to be called) in the
future.
The frequency an application shoudl call optimize is based on the frequency of
data modifications. If data is frequently added, deleted, or updated then
optimize should be run frequently. A good rule of thumb is to run optimize if
you have added or modified 100,000 or more records or run more than 20 data
modification operations.
"""
if cleanup_older_than is not None:
cleanup_older_than = round(cleanup_older_than.total_seconds() * 1000)
return await self._inner.optimize(cleanup_older_than)
async def list_indices(self) -> IndexConfig:
"""
List all indices that have been created with Self::create_index
"""
return await self._inner.list_indices()
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