mirror of
https://github.com/lancedb/lancedb.git
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cbb56e25ab
* to_df() is now async, added `to_df_blocking` to convenience * add remote lancedb client to public lancedb * make lancedb connection class understand url scheme `lancedb+<connection_type>://<host>:<port>`.
346 lines
11 KiB
Python
346 lines
11 KiB
Python
# Copyright 2023 LanceDB Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import os
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from functools import cached_property
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from typing import List, Union
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import lance
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import numpy as np
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import pandas as pd
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import pyarrow as pa
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from lance import LanceDataset
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from lance.vector import vec_to_table
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from .common import DATA, VEC, VECTOR_COLUMN_NAME
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from .query import LanceFtsQueryBuilder, LanceQueryBuilder
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def _sanitize_data(data, schema):
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if isinstance(data, list):
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data = pa.Table.from_pylist(data)
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data = _sanitize_schema(data, schema=schema)
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if isinstance(data, dict):
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data = vec_to_table(data)
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if isinstance(data, pd.DataFrame):
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data = pa.Table.from_pandas(data)
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data = _sanitize_schema(data, schema=schema)
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if not isinstance(data, pa.Table):
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raise TypeError(f"Unsupported data type: {type(data)}")
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return data
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class LanceTable:
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"""
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A table in a LanceDB database.
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Examples
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--------
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Create using [LanceDBConnection.create_table][lancedb.LanceDBConnection.create_table]
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(more examples in that method's documentation).
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>>> import lancedb
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>>> db = lancedb.connect("./.lancedb")
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>>> table = db.create_table("my_table", data=[{"vector": [1.1, 1.2], "b": 2}])
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>>> table.head()
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pyarrow.Table
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vector: fixed_size_list<item: float>[2]
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child 0, item: float
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b: int64
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----
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vector: [[[1.1,1.2]]]
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b: [[2]]
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Can append new data with [LanceTable.add][lancedb.table.LanceTable.add].
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>>> table.add([{"vector": [0.5, 1.3], "b": 4}])
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2
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Can query the table with [LanceTable.search][lancedb.table.LanceTable.search].
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>>> table.search([0.4, 0.4]).select(["b"]).to_df()
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b vector score
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0 4 [0.5, 1.3] 0.82
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1 2 [1.1, 1.2] 1.13
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Search queries are much faster when an index is created. See
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[LanceTable.create_index][lancedb.table.LanceTable.create_index].
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"""
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def __init__(
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self, connection: "lancedb.db.LanceDBConnection", name: str, version: int = None
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):
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self._conn = connection
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self.name = name
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self._version = version
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def _reset_dataset(self):
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try:
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del self.__dict__["_dataset"]
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except AttributeError:
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pass
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@property
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def schema(self) -> pa.Schema:
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"""Return the schema of the table.
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Returns
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-------
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pa.Schema
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A PyArrow schema object."""
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return self._dataset.schema
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def list_versions(self):
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"""List all versions of the table"""
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return self._dataset.versions()
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@property
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def version(self) -> int:
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"""Get the current version of the table"""
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return self._dataset.version
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def checkout(self, version: int):
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"""Checkout a version of the table. This is an in-place operation.
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This allows viewing previous versions of the table.
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Parameters
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----------
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version : int
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The version to checkout.
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Examples
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--------
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>>> import lancedb
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>>> db = lancedb.connect("./.lancedb")
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>>> table = db.create_table("my_table", [{"vector": [1.1, 0.9], "type": "vector"}])
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>>> table.version
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1
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>>> table.to_pandas()
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vector type
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0 [1.1, 0.9] vector
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>>> table.add([{"vector": [0.5, 0.2], "type": "vector"}])
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2
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>>> table.version
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2
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>>> table.checkout(1)
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>>> table.to_pandas()
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vector type
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0 [1.1, 0.9] vector
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"""
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max_ver = max([v["version"] for v in self._dataset.versions()])
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if version < 1 or version > max_ver:
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raise ValueError(f"Invalid version {version}")
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self._version = version
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self._reset_dataset()
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def __len__(self):
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return self._dataset.count_rows()
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def __repr__(self) -> str:
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return f"LanceTable({self.name})"
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def __str__(self) -> str:
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return self.__repr__()
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def head(self, n=5) -> pa.Table:
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"""Return the first n rows of the table."""
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return self._dataset.head(n)
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def to_pandas(self) -> pd.DataFrame:
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"""Return the table as a pandas DataFrame.
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Returns
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-------
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pd.DataFrame
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"""
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return self.to_arrow().to_pandas()
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def to_arrow(self) -> pa.Table:
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"""Return the table as a pyarrow Table.
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Returns
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-------
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pa.Table"""
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return self._dataset.to_table()
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@property
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def _dataset_uri(self) -> str:
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return os.path.join(self._conn.uri, f"{self.name}.lance")
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def create_index(self, metric="L2", num_partitions=256, num_sub_vectors=96):
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"""Create an index on the table.
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Parameters
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----------
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metric: str, default "L2"
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The distance metric to use when creating the index. Valid values are "L2" or "cosine".
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L2 is euclidean distance.
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num_partitions: int
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The number of IVF partitions to use when creating the index.
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Default is 256.
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num_sub_vectors: int
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The number of PQ sub-vectors to use when creating the index.
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Default is 96.
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"""
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self._dataset.create_index(
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column=VECTOR_COLUMN_NAME,
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index_type="IVF_PQ",
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metric=metric,
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num_partitions=num_partitions,
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num_sub_vectors=num_sub_vectors,
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)
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self._reset_dataset()
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def create_fts_index(self, field_names: Union[str, List[str]]):
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"""Create a full-text search index on the table.
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Warning - this API is highly experimental and is highly likely to change
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in the future.
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Parameters
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----------
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field_names: str or list of str
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The name(s) of the field to index.
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"""
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from .fts import create_index, populate_index
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if isinstance(field_names, str):
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field_names = [field_names]
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index = create_index(self._get_fts_index_path(), field_names)
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populate_index(index, self, field_names)
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def _get_fts_index_path(self):
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return os.path.join(self._dataset_uri, "_indices", "tantivy")
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@cached_property
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def _dataset(self) -> LanceDataset:
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return lance.dataset(self._dataset_uri, version=self._version)
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def to_lance(self) -> LanceDataset:
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"""Return the LanceDataset backing this table."""
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return self._dataset
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def add(self, data: DATA, mode: str = "append") -> int:
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"""Add data to the table.
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Parameters
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----------
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data: list-of-dict, dict, pd.DataFrame
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The data to insert into the table.
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mode: str
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The mode to use when writing the data. Valid values are
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"append" and "overwrite".
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Returns
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-------
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int
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The number of vectors in the table.
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"""
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data = _sanitize_data(data, self.schema)
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lance.write_dataset(data, self._dataset_uri, mode=mode)
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self._reset_dataset()
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return len(self)
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def search(self, query: Union[VEC, str]) -> LanceQueryBuilder:
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"""Create a search query to find the nearest neighbors
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of the given query vector.
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Parameters
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----------
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query: list, np.ndarray
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The query vector.
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Returns
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-------
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LanceQueryBuilder
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A query builder object representing the query.
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Once executed, the query returns selected columns, the vector,
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and also the "score" column which is the distance between the query
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vector and the returned vector.
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"""
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if isinstance(query, str):
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# fts
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return LanceFtsQueryBuilder(self, query)
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if isinstance(query, list):
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query = np.array(query)
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if isinstance(query, np.ndarray):
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query = query.astype(np.float32)
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else:
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raise TypeError(f"Unsupported query type: {type(query)}")
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return LanceQueryBuilder(self, query)
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@classmethod
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def create(cls, db, name, data, schema=None, mode="create"):
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tbl = LanceTable(db, name)
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data = _sanitize_data(data, schema)
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lance.write_dataset(data, tbl._dataset_uri, mode=mode)
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return tbl
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def _sanitize_schema(data: pa.Table, schema: pa.Schema = None) -> pa.Table:
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"""Ensure that the table has the expected schema.
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Parameters
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----------
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data: pa.Table
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The table to sanitize.
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schema: pa.Schema; optional
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The expected schema. If not provided, this just converts the
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vector column to fixed_size_list(float32) if necessary.
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"""
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if schema is not None:
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if data.schema == schema:
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return data
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# cast the columns to the expected types
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data = data.combine_chunks()
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data = _sanitize_vector_column(data, vector_column_name=VECTOR_COLUMN_NAME)
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return pa.Table.from_arrays(
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[data[name] for name in schema.names], schema=schema
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)
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# just check the vector column
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return _sanitize_vector_column(data, vector_column_name=VECTOR_COLUMN_NAME)
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def _sanitize_vector_column(data: pa.Table, vector_column_name: str) -> pa.Table:
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"""
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Ensure that the vector column exists and has type fixed_size_list(float32)
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Parameters
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----------
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data: pa.Table
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The table to sanitize.
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vector_column_name: str
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The name of the vector column.
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"""
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if vector_column_name not in data.column_names:
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raise ValueError(f"Missing vector column: {vector_column_name}")
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vec_arr = data[vector_column_name].combine_chunks()
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if pa.types.is_fixed_size_list(vec_arr.type):
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return data
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if not pa.types.is_list(vec_arr.type):
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raise TypeError(f"Unsupported vector column type: {vec_arr.type}")
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values = vec_arr.values
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if not pa.types.is_float32(values.type):
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values = values.cast(pa.float32())
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list_size = len(values) / len(data)
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vec_arr = pa.FixedSizeListArray.from_arrays(values, list_size)
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return data.set_column(
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data.column_names.index(vector_column_name), vector_column_name, vec_arr
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)
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