---
title: Quickstart
sidebarTitle: "Quickstart"
description: "Install LanceDB, create a table, and run your first vector search."
icon: rocket
---
import { PyConnect, TsConnect, RsConnect, PyConnectObjectStorage, TsConnectObjectStorage, RsConnectObjectStorage, PyConnectEnterprisePlaceholders, TsConnectEnterprisePlaceholders, RsConnectEnterprisePlaceholders } from '/snippets/connection.mdx';
import { PyQuickstartData, PyQuickstartCreateTable, PyQuickstartAddData, PyQuickstartCurateSample, PyQuickstartCurateDedupeSetup, PyQuickstartCurateDedupe, PyQuickstartCurateDedupeQuery, PyQuickstartFlattenStats, PyQuickstartAddFeature, PyQuickstartAddFeatureEnterprise, PyQuickstartMultimodalBytes, PyQuickstartTraining, TsQuickstartData, TsQuickstartCreateTable, TsQuickstartAddData, TsQuickstartVectorSearch1, TsQuickstartMultimodalBytes, RsQuickstartDefineStruct, RsQuickstartData, RsQuickstartCreateTable, RsQuickstartAddData, RsQuickstartVectorSearch1, RsQuickstartMultimodalBytes } from '/snippets/quickstart.mdx';

This quickstart uses LanceDB Embedded with a local database and works without an account or API key.
LanceDB Embedded runs in your application's process; your data can live on local disk or in object storage.

## 1\. Install LanceDB {#1-install-lancedb}

Choose your language. Python uses Pandas to display the search results. TypeScript needs Node.js 22.18 or later to run the `.mts` example directly.

<CodeGroup>
```bash Python (pip) icon="python"
pip install lancedb pandas
```

```bash Python (uv) icon="python"
uv venv
uv pip install lancedb pandas
```

```bash TypeScript icon="square-js"
npm install @lancedb/lancedb
```

```bash Rust icon="rust"
cargo init --name lancedb_quickstart
cargo add lancedb@0.31.0 --features polars,remote
cargo add tokio --features macros,rt-multi-thread
```
</CodeGroup>

Run the Rust commands in a new, empty directory. The `polars` feature provides DataFrame output; `remote` also lets this example connect to Enterprise.

<Accordion title="Python pre-release builds and older CPUs">
### Python pre-release builds {#python-pre-release-builds}

To pick up the latest features and bug fixes
before the next stable release, install a pre-release from LanceDB's Fury index.

<CodeGroup>
```bash Python (pip) icon="terminal"
pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
```

```bash Python (uv) icon="terminal"
uv venv
uv pip install --prerelease allow --index https://pypi.fury.io/lancedb/ lancedb

# To add to pyproject.toml, use:
uv add --prerelease allow --index https://pypi.fury.io/lancedb/ lancedb
```
</CodeGroup>

Most applications only need `lancedb`. Install the optional `pylance` extra and
include the Lance preview index only when building query engines or connectors,
calling format-level `lance` APIs directly, or converting plain-scan lazy blob
handles with `to_pandas()`. Legacy plain-scan byte conversions use LanceDB's
native path and do not require `pylance`:

<CodeGroup>
```bash Python (pip) icon="terminal"
pip install --pre \
  --extra-index-url https://pypi.fury.io/lancedb/ \
  --extra-index-url https://pypi.fury.io/lance-format/ \
  "lancedb[pylance]"
```

```bash Python (uv) icon="terminal"
uv pip install --prerelease allow \
  --index https://pypi.fury.io/lancedb/ \
  --index https://pypi.fury.io/lance-format/ \
  --index-strategy unsafe-best-match \
  "lancedb[pylance]"
```
</CodeGroup>

The LanceDB preview index contains old `pylance` releases; current Lance previews
are published to the `lance-format` index. By default, uv only considers versions
from the first index containing a package, which can select an old `pylance` or
backtrack to an older LanceDB release. `unsafe-best-match` searches all configured
indexes, like pip; use it only with indexes you trust. Install the extra rather
than an unconstrained `pylance` so LanceDB's minimum supported version is enforced.

<Note>
Pre-release builds receive the same level of testing as stable releases, but their availability is not guaranteed
for more than 6 months after release. For real-world workloads, we recommend you use the latest stable release
as far as possible.
</Note>

### Older x86_64 CPUs {#older-x86-cpus}
The default `lancedb` wheel targets `x86-64-haswell` (AVX2 + FMA + F16C). On older x86_64 CPUs without AVX2
(Intel Sandy Bridge, Ivy Bridge, Westmere; AMD Bulldozer, Piledriver, Steamroller), `import lancedb` crashes
with `Illegal instruction`.

Install `lancedb-compat` instead. It exposes the same API (`import lancedb` still works), is built at the
`x86-64-v2` baseline, and uses runtime SIMD dispatch to still use AVX2, FMA, or AVX-512 when available:

<CodeGroup>
```bash Python (pip) icon="terminal"
pip install lancedb-compat
```
</CodeGroup>

The two packages share the same `lancedb/` namespace and conflict at install time, so pick one. To switch,
uninstall the other first (`pip uninstall lancedb && pip install lancedb-compat`).


</Accordion>

## 2\. Connect to a database {#2-connect-to-a-lancedb-database}

### Local directory {#connect-via-local-directory-path}

LanceDB creates an `ex_lancedb` directory to store your tables. No server is needed.

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyConnect}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {"import * as lancedb from \"@lancedb/lancedb\";\n\n"}
    {TsConnect}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {"use lancedb::connect;\n\n"}
    {RsConnect}
    </CodeBlock>
</CodeGroup>

For Python's async API, see [Connecting to a database](/basics/catalog).

### Object storage {#connect-via-object-storage-uris}

Use an `s3://`, `gs://`, or `az://` URI in place of the local path. See [Object storage](/basics/catalog#object-storage) for credentials and provider-specific options.

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyConnectObjectStorage}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {"import * as lancedb from \"@lancedb/lancedb\";\n\n"}
    {TsConnectObjectStorage}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {"use lancedb::connect;\n\n"}
    {RsConnectObjectStorage}
    </CodeBlock>
</CodeGroup>

### Enterprise {#connect-to-lancedb-enterprise}

Use the database URI, API key, and endpoint provided by your administrator or the LanceDB team. Replace the placeholders below with those details. Use the deployment's region if provided; otherwise keep `us-east-1`.

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyConnectEnterprisePlaceholders}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {"import * as lancedb from \"@lancedb/lancedb\";\n\n"}
    {TsConnectEnterprisePlaceholders}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {"use lancedb::connect;\n\n"}
    {RsConnectEnterprisePlaceholders}
    </CodeBlock>
</CodeGroup>

<Note>
In application code, load credentials from environment variables or a secrets manager. See [connection settings](/basics/catalog#enterprise) for an example.
</Note>

Need a deployment? [Contact the LanceDB team](https://www.lancedb.com/contact). For differences between local and server execution, see [LanceDB Enterprise](/index#architecture).

## 3\. Create a table and add data {#3-create-a-new-table}

Each row describes a frame from a robot video clip and stores an embedding alongside its video, caption, and quality stats.

<Info>
These embeddings are made up for this example. In your application, use an embedding model to generate them from your data.
</Info>

<Accordion title="Frame records">
<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartData}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {TsQuickstartData}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {RsQuickstartDefineStruct}
    {RsQuickstartData}
    </CodeBlock>
</CodeGroup>
</Accordion>

Create a `frames` table from those records:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartCreateTable}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {TsQuickstartCreateTable}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {RsQuickstartCreateTable}
    </CodeBlock>
</CodeGroup>

This example uses overwrite mode so you can run it again. It replaces any existing `frames` table in the database you connect to.

Add one more frame:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartAddData}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {TsQuickstartAddData}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {RsQuickstartAddData}
    </CodeBlock>
</CodeGroup>

## 4\. Search the table {#4-semantic-search}

Find the two embeddings closest to the query embedding. This example uses a fixed embedding; it does not call an embedding model.

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {"# Search for frames similar to \"a gripper closing on an object\"\nquery_vector = [0.2, 0.8, 0.4, 0.9]\n\n# Ensure you run `pip install pandas` beforehand\nresult = (\n    table.search(query_vector)\n    .select([\"frame_id\", \"video_id\", \"caption\", \"_distance\"])\n    .limit(2)\n    .to_pandas()\n)\nprint(result)\n"}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {TsQuickstartVectorSearch1}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {RsQuickstartVectorSearch1}
    </CodeBlock>
</CodeGroup>

Frames 2 and 4 come back first -- the gripper closing on the cup, then lowering it. Python displays a Pandas DataFrame, Rust displays a Polars DataFrame, and TypeScript displays an array of results. The `_distance` column measures how far each result is from your query embedding, with smaller values indicating a closer match.

## 5\. Curate data with SQL {#5-curate-with-sql}

<Info>
Step 5 needs LanceDB Enterprise: SQL is an Enterprise feature. If you're
using LanceDB Embedded, skip ahead to
[Flatten nested fields](#flatten-nested-fields) below.
</Info>

LanceDB Enterprise accepts SQL over the same connection, for sampling, joins, and
curation tasks that go beyond a single table scan. Draw a quick sample to look
at the data:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartCurateSample}
    </CodeBlock>
</CodeGroup>

See [SQL](/basics/sql) for the Flight SQL client and CLI alternatives to `execute_query()`.

Now add a near-duplicate frame -- frame 5 is one video frame after frame 2, with
an almost identical embedding -- and build the vector index that `dedupe` reads:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartCurateDedupeSetup}
    </CodeBlock>
</CodeGroup>

Collapse near-duplicates into a materialized view that stays current as new frames arrive, then read it back:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartCurateDedupe}
    </CodeBlock>
</CodeGroup>

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartCurateDedupeQuery}
    </CodeBlock>
</CodeGroup>

Frames 2 and 5 collapse into one representative -- five rows become four.
Which of the two ids survives is not guaranteed to stay the same across
appends, so don't treat it as a stable identifier for the cluster. See
[Deduplicate near-duplicate records](/curation/dedupe) for `duplicate_pairs`,
resolution strategies, and tuning `distance_threshold`, and
[Statistical Sampling](/curation/statistical-sampling) for percentage samples,
repeatable draws, and sampling across a join.

## 6\. Add a derived feature {#6-add-a-derived-feature}

### Flatten nested fields {#flatten-nested-fields}

A UDF binds to top-level columns, so flatten the fields inside `stats` first.
This is a plain eager `add_columns` call, so it works the same on LanceDB
Embedded and LanceDB Enterprise -- [step 8](#8-train) needs these columns
either way:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartFlattenStats}
    </CodeBlock>
</CodeGroup>

<Info>
The rest of this step needs LanceDB Enterprise -- Functions are an
Enterprise feature. If you're using LanceDB Embedded, skip ahead to
[step 7](#7-store-multimodal-data).
</Info>

Register a UDF as a Function, then call it to fill a column computed from
those flattened fields. This fills every existing row once, immediately:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartAddFeature}
    </CodeBlock>
</CodeGroup>

Read it back like any other column:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {"features = table.search().select([\"frame_id\", \"video_id\", \"quality_score\"]).limit(10).to_pandas()\nprint(features)\n"}
    </CodeBlock>
</CodeGroup>

`refresh_column` fills only rows added since the last refresh and recomputes rows whose inputs changed, leaving unchanged rows alone. Run the same refresh as a background job instead, so it doesn't block:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartAddFeatureEnterprise}
    </CodeBlock>
</CodeGroup>

See [Create a Function](/curation/create-function) for UDFs that call external
models or services, and [Computed columns](/feature-engineering/add-computed-column)
for the SQL-expression form that doesn't need a registered Function.

## 7\. Store multimodal data {#7-store-multimodal-data}

Store images, audio, or other binary data alongside vectors and metadata, in a column of raw bytes. This creates a new table from one image file -- replace the path with one of your own:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartMultimodalBytes}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {TsQuickstartMultimodalBytes}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {RsQuickstartMultimodalBytes}
    </CodeBlock>
</CodeGroup>

See [Multimodal data types](/management/table#multimodal) for larger payloads and the Blob v2 column type, which avoids loading binary data on every scan.

## 8\. Train on your data {#8-train}

Read table data into PyTorch, projecting and shuffling only the columns a training step needs:

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartTraining}
    </CodeBlock>
</CodeGroup>

`StreamingDataset` wraps a table for PyTorch training reads; `columns` projects before reading, so a training step touches only the columns it selects, not unused ones such as `caption` or `embedding`. It works the same way against LanceDB Embedded and LanceDB Enterprise. See [PyTorch Data Loader](/training/data-loading) for shuffling, transforms, resuming, and multi-worker loading, and [Permutation](/training/permutation) for the lower-level row-ordering primitive it's built on.

## Where to go next {#next-steps}

<Columns cols={2}>
<Card title="Basic table operations" icon="table" href="/management/table">
Create, update, and manage your tables.
</Card>
<Card title="Build a RAG app" icon="search" href="/tutorials/agents/contextual-rag">
Use retrieved context in a language model application.
</Card>
<Card title="Indexing" icon="layer-group" href="/indexing/">
Speed up queries on larger datasets.
</Card>
<Card title="Embedding models" icon="vector-square" href="/embedding/">
Generate vectors from your own data.
</Card>
</Columns>