Working With Tables in LanceDB
In LanceDB, tables store records with a defined schema that specifies column names and types. You can create LanceDB tables from these data formats:
- Pandas DataFrames
- Polars DataFrames
- Apache Arrow Tables
The Python SDK additionally supports:
- PyArrow schemas for explicit schema control
LanceModelfor Pydantic-based validation
Create a LanceDB Table
Initialize a LanceDB connection and create a table
LanceDB allows ingesting data from various sources - dict, list[dict], pd.DataFrame, pa.Table or a Iterator[pa.RecordBatch]. Let's take a look at some of the these.
From list of tuples or dictionaries
Note
If the table already exists, LanceDB will raise an error by default.
create_table supports an optional exist_ok parameter. When set to True
and the table exists, then it simply opens the existing table. The data you
passed in will NOT be appended to the table in that case.
Sometimes you want to make sure that you start fresh. If you want to overwrite the table, you can pass in mode="overwrite" to the createTable function.
You can create a LanceDB table in JavaScript using an array of records as follows.
const _tbl = await db.createTable(
"myTable",
[
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
],
{ mode: "overwrite" },
);
This will infer the schema from the provided data. If you want to explicitly provide a schema, you can use apache-arrow to declare a schema
const schema = new arrow.Schema([
new arrow.Field(
"vector",
new arrow.FixedSizeList(
2,
new arrow.Field("item", new arrow.Float32(), true),
),
),
new arrow.Field("item", new arrow.Utf8(), true),
new arrow.Field("price", new arrow.Float32(), true),
]);
const data = [
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
];
const tbl = await db.createTable("myTable", data, {
schema,
});
Note
createTable supports an optional existsOk parameter. When set to true
and the table exists, then it simply opens the existing table. The data you
passed in will NOT be appended to the table in that case.
Sometimes you want to make sure that you start fresh. If you want to overwrite the table, you can pass in mode: "overwrite" to the createTable function.
const tbl = await db.createTable(
"myTable",
[
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
],
{ writeMode: lancedb.WriteMode.Overwrite },
);
This will infer the schema from the provided data. If you want to explicitly provide a schema, you can use apache-arrow to declare a schema
const schema = new arrow.Schema([
new arrow.Field(
"vector",
new arrow.FixedSizeList(
2,
new arrow.Field("item", new arrow.Float32(), true),
),
),
new arrow.Field("item", new arrow.Utf8(), true),
new arrow.Field("price", new arrow.Float32(), true),
]);
const data = [
{ vector: [3.1, 4.1], item: "foo", price: 10.0 },
{ vector: [5.9, 26.5], item: "bar", price: 20.0 },
];
const tbl = await db.createTable({
name: "myTableWithSchema",
data,
schema,
});
Warning
existsOk is not available in vectordb
If the table already exists, vectordb will raise an error by default.
You can use writeMode: WriteMode.Overwrite to overwrite the table.
But this will delete the existing table and create a new one with the same name.
Sometimes you want to make sure that you start fresh.
If you want to overwrite the table, you can pass in writeMode: lancedb.WriteMode.Overwrite to the createTable function.
From a Pandas DataFrame
Note
Data is converted to Arrow before being written to disk. For maximum control over how data is saved, either provide the PyArrow schema to convert to or else provide a PyArrow Table directly.
The vector column needs to be a Vector (defined as pyarrow.FixedSizeList) type.
From a Polars DataFrame
LanceDB supports Polars, a modern, fast DataFrame library written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow under the hood. A deeper integration between LanceDB Tables and Polars DataFrames is on the way.
From an Arrow Table
You can also create LanceDB tables directly from Arrow tables. LanceDB supports float16 data type!
import pyarrow as pa
import numpy as np
dim = 16
total = 2
schema = pa.schema(
[pa.field("vector", pa.list_(pa.float16(), dim)), pa.field("text", pa.string())]
)
data = pa.Table.from_arrays(
[
pa.array(
[np.random.randn(dim).astype(np.float16) for _ in range(total)],
pa.list_(pa.float16(), dim),
),
pa.array(["foo", "bar"]),
],
["vector", "text"],
)
tbl = db.create_table("f16_tbl", data, schema=schema)
import polars as pl
import numpy as np
dim = 16
total = 2
schema = pa.schema(
[pa.field("vector", pa.list_(pa.float16(), dim)), pa.field("text", pa.string())]
)
data = pa.Table.from_arrays(
[
pa.array(
[np.random.randn(dim).astype(np.float16) for _ in range(total)],
pa.list_(pa.float16(), dim),
),
pa.array(["foo", "bar"]),
],
["vector", "text"],
)
async_tbl = await async_db.create_table("f16_tbl_async", data, schema=schema)
const db = await lancedb.connect(databaseDir);
const dim = 16;
const total = 10;
const f16Schema = new Schema([
new Field("id", new Int32()),
new Field(
"vector",
new FixedSizeList(dim, new Field("item", new Float16(), true)),
false,
),
]);
const data = lancedb.makeArrowTable(
Array.from(Array(total), (_, i) => ({
id: i,
vector: Array.from(Array(dim), Math.random),
})),
{ schema: f16Schema },
);
const _table = await db.createTable("f16_tbl", data);
const dim = 16;
const total = 10;
const schema = new Schema([
new Field("id", new Int32()),
new Field(
"vector",
new FixedSizeList(dim, new Field("item", new Float16(), true)),
false,
),
]);
const data = lancedb.makeArrowTable(
Array.from(Array(total), (_, i) => ({
id: i,
vector: Array.from(Array(dim), Math.random),
})),
{ schema },
);
const table = await db.createTable("f16_tbl", data);
From Pydantic Models
When you create an empty table without data, you must specify the table schema.
LanceDB supports creating tables by specifying a PyArrow schema or a specialized
Pydantic model called LanceModel.
For example, the following Content model specifies a table with 5 columns:
movie_id, vector, genres, title, and imdb_id. When you create a table, you can
pass the class as the value of the schema parameter to create_table.
The vector column is a Vector type, which is a specialized Pydantic type that
can be configured with the vector dimensions. It is also important to note that
LanceDB only understands subclasses of lancedb.pydantic.LanceModel
(which itself derives from pydantic.BaseModel).
from lancedb.pydantic import Vector, LanceModel
import pyarrow as pa
class Content(LanceModel):
movie_id: int
vector: Vector(128)
genres: str
title: str
imdb_id: int
@property
def imdb_url(self) -> str:
return f"https://www.imdb.com/title/tt{self.imdb_id}"
tbl = db.create_table("movielens_small", schema=Content)
from lancedb.pydantic import Vector, LanceModel
import pyarrow as pa
class Content(LanceModel):
movie_id: int
vector: Vector(128)
genres: str
title: str
imdb_id: int
@property
def imdb_url(self) -> str:
return f"https://www.imdb.com/title/tt{self.imdb_id}"
async_tbl = await async_db.create_table("movielens_small_async", schema=Content)
Nested schemas
Sometimes your data model may contain nested objects. For example, you may want to store the document string and the document source name as a nested Document object:
This can be used as the type of a LanceDB table column:
This creates a struct column called "document" that has two subfields called "content" and "source":
In [28]: tbl.schema
Out[28]:
id: string not null
vector: fixed_size_list<item: float>[1536] not null
child 0, item: float
document: struct<content: string not null, source: string not null> not null
child 0, content: string not null
child 1, source: string not null
Validators
Note that neither Pydantic nor PyArrow automatically validates that input data is of the correct timezone, but this is easy to add as a custom field validator:
from datetime import datetime
from zoneinfo import ZoneInfo
from lancedb.pydantic import LanceModel
from pydantic import Field, field_validator, ValidationError, ValidationInfo
tzname = "America/New_York"
tz = ZoneInfo(tzname)
class TestModel(LanceModel):
dt_with_tz: datetime = Field(json_schema_extra={"tz": tzname})
@field_validator('dt_with_tz')
@classmethod
def tz_must_match(cls, dt: datetime) -> datetime:
assert dt.tzinfo == tz
return dt
ok = TestModel(dt_with_tz=datetime.now(tz))
try:
TestModel(dt_with_tz=datetime.now(ZoneInfo("Asia/Shanghai")))
assert 0 == 1, "this should raise ValidationError"
except ValidationError:
print("A ValidationError was raised.")
pass
When you run this code it should print "A ValidationError was raised."
Pydantic custom types
LanceDB does NOT yet support converting pydantic custom types. If this is something you need, please file a feature request on the LanceDB Github repo.
Using Iterators / Writing Large Datasets
It is recommended to use iterators to add large datasets in batches when creating your table in one go. This does not create multiple versions of your dataset unlike manually adding batches using table.add()
LanceDB additionally supports PyArrow's RecordBatch Iterators or other generators producing supported data types.
Here's an example using using RecordBatch iterator for creating tables.
import pyarrow as pa
def make_batches():
for i in range(5):
yield pa.RecordBatch.from_arrays(
[
pa.array(
[[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
pa.list_(pa.float32(), 4),
),
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
["vector", "item", "price"],
)
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), 4)),
pa.field("item", pa.utf8()),
pa.field("price", pa.float32()),
]
)
db.create_table("batched_tale", make_batches(), schema=schema)
import pyarrow as pa
def make_batches():
for i in range(5):
yield pa.RecordBatch.from_arrays(
[
pa.array(
[[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],
pa.list_(pa.float32(), 4),
),
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
["vector", "item", "price"],
)
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), 4)),
pa.field("item", pa.utf8()),
pa.field("price", pa.float32()),
]
)
await async_db.create_table("batched_table", make_batches(), schema=schema)
You can also use iterators of other types like Pandas DataFrame or Pylists directly in the above example.
Open existing tables
If you forget the name of your table, you can always get a listing of all table names.
Then, you can open any existing tables.
Creating empty table
You can create an empty table for scenarios where you want to add data to the table later. An example would be when you want to collect data from a stream/external file and then add it to a table in batches.
An empty table can be initialized via a PyArrow schema.
Alternatively, you can also use Pydantic to specify the schema for the empty table. Note that we do not
directly import pydantic but instead use lancedb.pydantic which is a subclass of pydantic.BaseModel
that has been extended to support LanceDB specific types like Vector.
Once the empty table has been created, you can add data to it via the various methods listed in the Adding to a table section.
Drop a table
Use the drop_table() method on the database to remove a table.
This permanently removes the table and is not recoverable, unlike deleting rows.
By default, if the table does not exist an exception is raised. To suppress this,
you can pass in ignore_missing=True.