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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.

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Installation

Before using the Chroma handshake, make sure to install the required dependencies: 
pip install chonkie[chroma]

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Basic Usage

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Initialization

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")

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Writing Chunks to ChromaDB

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)

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Searching Chunks in ChromaDB

You can retrieve the most similar chunks from your ChromaDB collection using the search method: 
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"])

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"])

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"])

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Parameters

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client
Optional[chromadb.Client]
default:"None"
Chroma client instance. If not provided, a new client will be created.
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collection_name
Union[str, Literal['random']]
default:"random"
Name of the collection to use. If “random”, a random name will be generated.
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embedding_model
Union[str, BaseEmbeddings]
default:"minishlab/potion-retrieval-32M"
Embedding model to use.
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path
Optional[str]
default:"None"
If provided, creates a persistent Chroma client at the specified path.