> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chonkie.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LanceDB Handshake

> Export Chonkie's Chunks into a LanceDB table.

The `LanceDBHandshake` class provides seamless integration between Chonkie's chunking system and LanceDB, a serverless vector database built on Apache Arrow.

Embed and store your Chonkie chunks in LanceDB — locally or in the cloud — without ever leaving the Chonkie SDK.

## Installation

Before using the LanceDB handshake, make sure to install the required dependencies:

```bash theme={"system"}
pip install chonkie[lancedb]
```

## Basic Usage

### Initialization

<CodeGroup>
  ```python in-memory (ephemeral) theme={"system"}
  from chonkie import LanceDBHandshake

  # Default: in-memory LanceDB, auto-generated table name
  handshake = LanceDBHandshake()
  ```

  ```python local persistent storage theme={"system"}
  from chonkie import LanceDBHandshake

  # Persist to a local directory
  handshake = LanceDBHandshake(uri="./my_lancedb", table_name="my_chunks")
  ```

  ```python existing connection theme={"system"}
  import lancedb
  from chonkie import LanceDBHandshake

  conn = lancedb.connect("./my_lancedb")
  handshake = LanceDBHandshake(connection=conn, table_name="my_chunks")
  ```

  ```python LanceDB Cloud theme={"system"}
  from chonkie import LanceDBHandshake

  handshake = LanceDBHandshake(
      uri="db://my-project",
      table_name="my_chunks",
  )
  ```
</CodeGroup>

### Writing Chunks to LanceDB

```python theme={"system"}
from chonkie import LanceDBHandshake, SemanticChunker

# Initialize the handshake
handshake = LanceDBHandshake(uri="./my_lancedb", table_name="my_documents")

# Create some chunks
chunker = SemanticChunker()
chunks = chunker("Chonkie loves to chonk your texts!")

# Write chunks to LanceDB
handshake.write(chunks)
```

### Searching Chunks in LanceDB

You can retrieve the most similar chunks from your LanceDB table using the `search` method:

<CodeGroup>
  ```python search using a query theme={"system"}
  from chonkie import LanceDBHandshake

  handshake = LanceDBHandshake(uri="./my_lancedb", table_name="my_documents")

  results = handshake.search(query="chonk your texts", limit=5)
  for result in results:
      print(result["score"], result["text"])
  ```

  ```python search using an embedding theme={"system"}
  from chonkie import LanceDBHandshake

  handshake = LanceDBHandshake(uri="./my_lancedb", table_name="my_documents")

  embedding = handshake.embedding_model.embed("chonk your texts").tolist()
  results = handshake.search(embedding=embedding, limit=5)
  for result in results:
      print(result["score"], result["text"])
  ```
</CodeGroup>

## Parameters

<ParamField path="connection" type="Optional[lancedb.DBConnection]" default="None">
  An existing LanceDB connection. If not provided, a new connection is created using `uri`.
</ParamField>

<ParamField path="uri" type="Union[str, os.PathLike]" default="memory://">
  URI of the LanceDB database. Use `"memory://"` for an ephemeral in-memory database, a local directory path for persistent storage, or a `db://` URI for LanceDB Cloud.
</ParamField>

<ParamField path="table_name" type="Union[str, Literal['random']]" default="random">
  Name of the table to write chunks to. If `"random"`, a unique name is auto-generated.
</ParamField>

<ParamField path="embedding_model" type="Union[str, BaseEmbeddings]" default="minishlab/potion-retrieval-32M">
  Embedding model to use. Can be a model name string or a `BaseEmbeddings` instance.
</ParamField>