> ## 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.

# Chroma Handshake

> Export Chonkie's Chunks into a Chroma collection.

The `ChromaHandshake` class provides seamless integration between Chonkie's chunking system and ChromaDB, a popular vector database.

Embed and store your Chonkie chunks in ChromaDB without ever leaving the Chonkie SDK.

## Installation

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

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

## Basic Usage

### Initialization

```python theme={"system"}
from chonkie import ChromaHandshake

# Initialize with default settings (in-memory ChromaDB)
handshake = ChromaHandshake()

# Or specify a persistent storage path
handshake = ChromaHandshake(path="./chroma_db")

# Or use an existing Chroma client
import chromadb
client = chromadb.Client()
handshake = ChromaHandshake(client=client, collection_name="my_collection")
```

### Writing Chunks to ChromaDB

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

handshake = ChromaHandshake() # Initializes a new Chroma client

chunker = SemanticChunker()
chunks = chunker("Chonkie is the best chonker ever!")

handshake.write(chunks)
```

### Searching Chunks in ChromaDB

You can retrieve the most similar chunks from your ChromaDB collection using the `search` method:

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

  # Initialize the handshake
  handshake = ChromaHandshake(collection_name="my_documents")
  results = handshake.search(query="best chonker", limit=2)
  for result in results:
      print(result["score"], result["text"])
  ```

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

  # Initialize the handshake
  handshake = ChromaHandshake(collection_name="my_documents")

  # Generate an embedding using the handshake's configured embedding function
  embedding = handshake.embedding_function("best chonker").tolist()

  results = handshake.search(embedding=embedding, limit=2)
  for result in results:
      print(result["score"], result["text"])
  ```

  ```python search using chonkie chunks theme={"system"}
  from chonkie import ChromaHandshake, SemanticChunker

  # Initialize the handshake
  handshake = ChromaHandshake(collection_name="my_documents")

  # Create some chunks
  chunker = SemanticChunker()
  chunks = chunker.chunk("Chonkie is the best chonker ever!")

  # The chunker creates chunks but doesn't store their embeddings on the object.
  # We can generate an embedding from a chunk's text to use for the search.
  if chunks:
      search_embedding = handshake.embedding_function(chunks.text).tolist()

      # Search the handshake using the generated embedding
      results = handshake.search(
          embedding=search_embedding,
          limit=2,
      )
      for result in results:
          print(result["score"], result["text"])
  ```
</CodeGroup>

## Parameters

<ParamField path="client" type="Optional[chromadb.Client]" default="None">
  Chroma client instance. If not provided, a new client will be created.
</ParamField>

<ParamField path="collection_name" type="Union[str, Literal['random']]" default="random">
  Name of the collection to use. If "random", a random name will be generated.
</ParamField>

<ParamField path="embedding_model" type="Union[str, BaseEmbeddings]" default="minishlab/potion-retrieval-32M">
  Embedding model to use.
</ParamField>

<ParamField path="path" type="Optional[str]" default="None">
  If provided, creates a persistent Chroma client at the specified path.
</ParamField>