rust/lancedb
1
0
Fork 0
mirror of https://github.com/lancedb/lancedb.git synced 2025-12-25 22:29:58 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
ce2242e06d53d2dbcf8349b2de7a2f7685534f35
lancedb/python/lancedb/table.py

1564 lines
53 KiB
Python
Raw Blame History

# 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
from abc import ABC, abstractmethod
from functools import cached_property
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, 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.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 LanceQueryBuilder, Query
from .util import (
fs_from_uri,
join_uri,
safe_import,
value_to_sql,
)
from .utils.events import register_event
if TYPE_CHECKING:
from datetime import timedelta
import PIL
from lance.dataset import CleanupStats, ReaderLike
from .db import LanceDBConnection
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 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 _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
if vector_column not in data.column_names:
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]))
data = data.append_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:
if not isinstance(batch, pa.RecordBatch):
table = _sanitize_data(batch, schema, metadata, on_bad_vectors, fill_value)
for batch in table.to_batches():
yield batch
else:
yield batch
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"]).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
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:
.. 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**
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
--------
.. code-block:: python
import lance
dataset = lance.dataset("/tmp/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: str = VECTOR_COLUMN_NAME,
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, vector_column_name="vector")
... .where("original_width > 1000", prefilter=True)
... .select(["caption", "original_width"])
... .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
The name of the vector column to search.
The vector column needs to be a pyarrow fixed size list type
*default "vector"*
query_type: str
*default "auto"*.
Acceptable types are: "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 `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) -> pa.Table:
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.
"""
class LanceTable(Table):
"""
A table in a LanceDB database.
"""
def __init__(self, connection: "LanceDBConnection", name: str, version: int = None):
self._conn = connection
self.name = name
self._version = version
def _reset_dataset(self, version=None):
try:
if "_dataset" in self.__dict__:
del self.__dict__["_dataset"]
self._version = version
except AttributeError:
pass
@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.
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 = max([v["version"] for v in self._dataset.versions()])
if version < 1 or version > max_ver:
raise ValueError(f"Invalid version {version}")
self._reset_dataset(version=version)
try:
# Accessing the property updates the cached value
_ = self._dataset
except Exception as e:
if "not found" in str(e):
raise ValueError(
f"Version {version} no longer exists. Was it cleaned up?"
)
else:
raise e
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 = max([v["version"] for v in self._dataset.versions()])
if version is None:
version = self.version
elif version < 1 or version > max_ver:
raise ValueError(f"Invalid version {version}")
else:
self.checkout(version)
if version == max_ver:
# no-op if restoring the latest version
return
self._dataset.restore()
self._reset_dataset()
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 self._dataset.count_rows(filter)
def __len__(self):
return self.count_rows()
def __repr__(self) -> str:
return f"LanceTable({self.name})"
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
)
@property
def _dataset_uri(self) -> str:
return join_uri(self._conn.uri, f"{self.name}.lance")
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.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,
)
self._reset_dataset()
register_event("create_index")
def create_scalar_index(self, column: str, *, replace: bool = True):
self._dataset.create_scalar_index(column, index_type="BTREE", replace=replace)
def create_fts_index(
self,
field_names: Union[str, List[str]],
*,
replace: bool = False,
writer_heap_size: Optional[int] = 1024 * 1024 * 1024,
):
"""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
"""
from .fts import create_index, populate_index
if isinstance(field_names, str):
field_names = [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)
index = create_index(self._get_fts_index_path(), field_names)
populate_index(index, self, field_names, writer_heap_size=writer_heap_size)
register_event("create_fts_index")
def _get_fts_index_path(self):
return join_uri(self._dataset_uri, "_indices", "tantivy")
@cached_property
def _dataset(self) -> LanceDataset:
return lance.dataset(self._dataset_uri, version=self._version)
def to_lance(self) -> LanceDataset:
"""Return the LanceDataset backing this table."""
return self._dataset
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,
)
lance.write_dataset(data, self._dataset_uri, schema=self.schema, mode=mode)
self._reset_dataset()
register_event("add")
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._dataset.merge(
other_table, left_on=left_on, right_on=right_on, schema=schema
)
self._reset_dataset()
register_event("merge")
@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: str = VECTOR_COLUMN_NAME,
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][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, vector_column_name="vector")
... .where("original_width > 1000", prefilter=True)
... .select(["caption", "original_width"])
... .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, default "vector"
The name of the vector column to search.
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.
"""
register_event("search_table")
return LanceQueryBuilder.create(
self, query, query_type, vector_column_name=vector_column_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)
register_event("create_table")
return new_table
@classmethod
def open(cls, db, name):
tbl = cls(db, name)
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)"
)
register_event("open_table")
return tbl
def delete(self, where: str):
self._dataset.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.to_lance().update(values_sql, where)
self._reset_dataset()
register_event("update")
def _execute_query(self, query: Query) -> pa.Table:
ds = self.to_lance()
return ds.to_table(
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,
)
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()
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 _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
Reference in New Issue View Git Blame Copy Permalink