lancedb/docs/src/basic.md

Quick start

!!! info "LanceDB can be run in a number of ways:"

* Embedded within an existing backend (like your Django, Flask, Node.js or FastAPI application)
* Connected to directly from a client application like a Jupyter notebook for analytical workloads
* Deployed as a remote serverless database

Installation

=== "Python"

  ```shell
  pip install lancedb
  ```

=== "Typescript"

  ```shell
  npm install vectordb
  ```

=== "Rust"

!!! warning "Rust SDK is experimental, might introduce breaking changes in the near future"

```shell
cargo add vectordb
```

!!! info "To use the vectordb create, you first need to install protobuf."

=== "macOS"

    ```shell
    brew install protobuf
    ```

=== "Ubuntu/Debian"

    ```shell
    sudo apt install -y protobuf-compiler libssl-dev
    ```

!!! info "Please also make sure you're using the same version of Arrow as in the [vectordb crate](https://github.com/lancedb/lancedb/blob/main/Cargo.toml)"

How to connect to a database

=== "Python"

  ```python
  import lancedb
  uri = "data/sample-lancedb"
  db = lancedb.connect(uri)
  ```

=== "Typescript"

```typescript
--8<-- "docs/src/basic_legacy.ts:import"

--8<-- "docs/src/basic_legacy.ts:open_db"
```

=== "Rust"

```rust
#[tokio::main]
async fn main() -> Result<()> {
    --8<-- "rust/vectordb/examples/simple.rs:connect"
}
```

!!! info "See [examples/simple.rs](https://github.com/lancedb/lancedb/tree/main/rust/vectordb/examples/simple.rs) for a full working example."

LanceDB will create the directory if it doesn't exist (including parent directories).

If you need a reminder of the uri, you can call db.uri().

How to create a table

=== "Python"

```python
tbl = db.create_table("my_table",
                data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
                      {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
```

If the table already exists, LanceDB will raise an error by default.
If you want to overwrite the table, you can pass in `mode="overwrite"`
to the `create_table` method.

You can also pass in a pandas DataFrame directly:

```python
import pandas as pd
df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
                   {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
tbl = db.create_table("table_from_df", data=df)
```

=== "Typescript"

```typescript
--8<-- "docs/src/basic_legacy.ts:create_table"
```

If the table already exists, LanceDB will raise an error by default.
If you want to overwrite the table, you can pass in `mode="overwrite"`
to the `createTable` function.

=== "Rust"

```rust
use arrow_schema::{DataType, Schema, Field};
use arrow_array::{RecordBatch, RecordBatchIterator};

--8<-- "rust/vectordb/examples/simple.rs:create_table"
```

If the table already exists, LanceDB will raise an error by default.

!!! info "Under the hood, LanceDB is converting the input data into an Apache Arrow table and persisting it to disk in Lance format."

Creating an empty table

Sometimes you may not have the data to insert into the table at creation time. In this case, you can create an empty table and specify the schema.

=== "Python"

  ```python
  import pyarrow as pa
  schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
  tbl = db.create_table("empty_table", schema=schema)
  ```

=== "Typescript"

```typescript
--8<-- "docs/src/basic_legacy.ts:create_empty_table"
```

=== "Rust"

```rust
--8<-- "rust/vectordb/examples/simple.rs:create_empty_table"
```

How to open an existing table

Once created, you can open a table using the following code:

=== "Python"

```python
tbl = db.open_table("my_table")
```

=== "Typescript"

```typescript
const tbl = await db.openTable("myTable");
```

=== "Rust"

```rust
--8<-- "rust/vectordb/examples/simple.rs:open_with_existing_file"
```

If you forget the name of your table, you can always get a listing of all table names:

=== "Python"

```python
print(db.table_names())
```

=== "Javascript"

```javascript
console.log(await db.tableNames());
```

=== "Rust"

```rust
--8<-- "rust/vectordb/examples/simple.rs:list_names"
```

How to add data to a table

After a table has been created, you can always add more data to it using

=== "Python"

```python

# Option 1: Add a list of dicts to a table
data = [{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
        {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}]
tbl.add(data)

# Option 2: Add a pandas DataFrame to a table
df = pd.DataFrame(data)
tbl.add(data)
```

=== "Typescript"

```typescript
--8<-- "docs/src/basic_legacy.ts:add"
```

=== "Rust"

```rust
--8<-- "rust/vectordb/examples/simple.rs:add"
```

How to search for (approximate) nearest neighbors

Once you've embedded the query, you can find its nearest neighbors using the following code:

=== "Python"

```python
tbl.search([100, 100]).limit(2).to_pandas()
```

This returns a pandas DataFrame with the results.

=== "Typescript"

```typescript
--8<-- "docs/src/basic_legacy.ts:search"
```

=== "Rust"

```rust
use futures::TryStreamExt;

--8<-- "rust/vectordb/examples/simple.rs:search"
```

By default, LanceDB runs a brute-force scan over dataset to find the K nearest neighbours (KNN). For tables with more than 50K vectors, creating an ANN index is recommended to speed up search performance.

=== "Python"

```py
tbl.create_index()
```

=== "Typescript"

```{.typescript .ignore}
--8<-- "docs/src/basic_legacy.ts:create_index"
```

=== "Rust"

```rust
 --8<-- "rust/vectordb/examples/simple.rs:create_index"
```

Check Approximate Nearest Neighbor (ANN) Indexes section for more details.

How to delete rows from a table

Use the delete() method on tables to delete rows from a table. To choose which rows to delete, provide a filter that matches on the metadata columns. This can delete any number of rows that match the filter.

=== "Python"

```python
tbl.delete('item = "fizz"')
```

=== "Typescript"

```typescript
--8<-- "docs/src/basic_legacy.ts:delete"
```

=== "Rust"

```rust
--8<-- "rust/vectordb/examples/simple.rs:delete"
```

The deletion predicate is a SQL expression that supports the same expressions as the where() clause on a search. They can be as simple or complex as needed. To see what expressions are supported, see the SQL filters section.

=== "Python"

  Read more: [lancedb.table.Table.delete][]

=== "Javascript"

  Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)

How to remove a table

Use the drop_table() method on the database to remove a table.

=== "Python"

  ```python
  db.drop_table("my_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`.

=== "Typescript"

  ```typescript
  --8<-- "docs/src/basic_legacy.ts:drop_table"
  ```

  This permanently removes the table and is not recoverable, unlike deleting rows.
  If the table does not exist an exception is raised.

=== "Rust"

```rust
--8<-- "rust/vectordb/examples/simple.rs:drop_table"
```

!!! note "Bundling vectordb apps with Webpack"

If you're using the `vectordb` module in JavaScript, since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying a LanceDB app on Vercel.

```javascript
/** @type {import('next').NextConfig} */
module.exports = ({
webpack(config) {
    config.externals.push({ vectordb: 'vectordb' })
    return config;
}
})
```

What's next

This section covered the very basics of using LanceDB. If you're learning about vector databases for the first time, you may want to read the page on indexing to get familiar with the concepts.

If you've already worked with other vector databases, you may want to read the guides to learn how to work with LanceDB in more detail.