---
title: Quickstart
sidebarTitle: "Quickstart"
description: "Install LanceDB, create a table, and run your first vector search."
icon: rocket
---
import { PyConnect, TsConnect, RsConnect, PyConnectEnterprisePlaceholders, TsConnectEnterprisePlaceholders, RsConnectEnterprisePlaceholders } from '/snippets/connection.mdx';
import { PyQuickstartData, PyQuickstartCreateTable, PyQuickstartAddData, PyQuickstartVectorSearch1, TsQuickstartData, TsQuickstartCreateTable, TsQuickstartAddData, TsQuickstartVectorSearch1, RsQuickstartDefineStruct, RsQuickstartData, RsQuickstartCreateTable, RsQuickstartAddData, RsQuickstartVectorSearch1 } from '/snippets/quickstart.mdx';

This quickstart uses a local database and works without an account or API key.

Follow the steps below, or jump to the [complete examples](#code) to copy a runnable program.

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

Choose your language. Python uses Polars 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 polars
```

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

```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>

<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:

```bash Python (pip) icon="terminal"
pip install lancedb-compat
```

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}

### Use a 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>

The [complete examples](#code) include the imports and entry points for each language. For Python's async API, see [Connecting to a database](/basics/connect).

<Accordion title="Connect to LanceDB Enterprise instead">
### Connect to LanceDB 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/connect#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 [Moving to LanceDB Enterprise](/basics/sdk#moving-to-enterprise).
</Accordion>

<Accordion title="Connect directly to object storage instead">
### Connect via object storage URIs {#connect-via-object-storage-uris}

Use an `s3://`, `gs://`, or `az://` URI in place of the local path. See [Configuring storage](/deployment/storage) for credentials and provider-specific options.
</Accordion>

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

Each row describes a character and stores a vector alongside their name, description, and stats.

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

<Accordion title="Character 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 `characters` 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 `characters` table in the database you connect to.

Add one more character:

<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 vectors closest to the query vector. This example uses a fixed vector; it does not call an embedding model.

<CodeGroup>
    <CodeBlock filename="Python" language="Python" icon="python">
    {PyQuickstartVectorSearch1}
    </CodeBlock>
    <CodeBlock filename="TypeScript" language="TypeScript" icon="square-js">
    {TsQuickstartVectorSearch1}
    </CodeBlock>
    <CodeBlock filename="Rust" language="Rust" icon="rust">
    {RsQuickstartVectorSearch1}
    </CodeBlock>
</CodeGroup>

Merlin and Morgana come back first. Python and Rust display a Polars DataFrame; TypeScript displays an array of results. The `_distance` column measures how far each result is from your query vector, with smaller values indicating a closer match.

## Complete examples {#code}

Each example below creates the local database, adds four characters, and prints the two closest matches. Use the dependencies from step 1.

<Accordion title="Python: quickstart.py">
Save this as `quickstart.py` and run `python quickstart.py`. If you installed with uv, run `uv run quickstart.py`.

<CodeGroup>
    <CodeBlock filename="quickstart.py" language="Python" icon="python">
    {PyConnect}
    {PyQuickstartData}
    {PyQuickstartCreateTable}
    {PyQuickstartAddData}
    {PyQuickstartVectorSearch1}
    </CodeBlock>
</CodeGroup>
</Accordion>

<Accordion title="TypeScript: quickstart.mts">
Save this as `quickstart.mts` and run `node quickstart.mts` from the directory where you installed LanceDB.

<CodeGroup>
    <CodeBlock filename="quickstart.mts" language="TypeScript" icon="square-js">
    {"import * as lancedb from \"@lancedb/lancedb\";\n\n"}
    {TsConnect}
    {TsQuickstartData}
    {TsQuickstartCreateTable}
    {TsQuickstartAddData}
    {TsQuickstartVectorSearch1}
    </CodeBlock>
</CodeGroup>
</Accordion>

<Accordion title="Rust: src/main.rs">
Replace `src/main.rs` with this program, then run `cargo run` from your project directory. The schema and Arrow conversion helpers are included.

<CodeGroup>
    <CodeBlock filename="src/main.rs" language="Rust" icon="rust">
    {"use lancedb::connect;\n\n"}
    {RsQuickstartDefineStruct}
    {"\n#[tokio::main]\nasync fn main() -> Result<(), Box<dyn std::error::Error>> {\n"}
    {RsConnect}
    {RsQuickstartData}
    {RsQuickstartCreateTable}
    {RsQuickstartAddData}
    {RsQuickstartVectorSearch1}
    {"\nOk(())\n}\n"}
    </CodeBlock>
</CodeGroup>
</Accordion>

## Next steps {#next-steps}

You've created a table, added rows, and searched it. Continue with the guides below.

### Filter results {#5-curation}

Combine vector search with metadata filters, such as `stats.magic >= 4`. See [SQL filtering](/search/filtering).

### Extend your table {#extensions}

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

Add columns calculated from existing fields. See [Adding columns](/management/table#schema-add-new-columns).

#### Computed columns {#computed-columns}

Declare a column and choose when to fill or refresh it. See [Computed columns](/feature-engineering/add-computed-column).

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

Store images, audio, and other binary data alongside vectors and metadata. See [Multimodal data types](/management/table#multimodal).

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

Read table data into PyTorch for training. See [Data loading](/training/data-loading).

<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>