Qdrant Handshake

Export Chonkie’s Chunks into a Qdrant collection.

The QdrantHandshake class provides seamless integration between Chonkie’s chunking system and Qdrant, a high-performance vector database.

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

Installation

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

pip install chonkie[qdrant]

Basic Usage

Initialization

Initialize using chonkie

from chonkie import QdrantHandshake
handshake = QdrantHandshake(url="http://localhost:6333")

from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
handshake = QdrantHandshake(client=client, collection_name="my_collection")

from qdrant_client import QdrantClient
handshake = QdrantHandshake(
    url="YOUR_CLOUD_URL",
    api_key="YOUR_API_KEY",
)

Parameters

client · Optional[qdrant_client.QdrantClient] · default: "None"

Qdrant client instance. If not provided, a new client will be created based on other parameters.

collection_name · Union[str, Literal['random']] · default: "random"

Name of the collection to use. If “random”, a unique name will be generated.

embedding_model · Union[str, BaseEmbeddings] · default: "minishlab/potion-retrieval-32M"

Embedding model to use. Can be a model name or a BaseEmbeddings instance.

url · Optional[str] · default: "None"

URL of the Qdrant server. If provided, will connect to this server.

path · Optional[str] · default: "None"

If provided, creates a persistent Qdrant client at the specified path.

api_key · Optional[str] · default: "None"

API key for Qdrant Cloud authentication.

Writing Chunks to Qdrant

from chonkie import QdrantHandshake, SemanticChunker    

# Initialize the handshake
handshake = QdrantHandshake(
    url="YOUR_CLOUD_URL",
    api_key="YOUR_API_KEY",
    collection_name="my_documents",
)

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

# Write chunks to Qdrant
handshake.write(chunks)

Searching Chunks in Qdrant

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

search using a query

from chonkie import QdrantHandshake

# Initialize the handshake
handshake = QdrantHandshake(
    url="YOUR_CLOUD_URL",
    api_key="YOUR_API_KEY",
    collection_name="my_documents",
)
results = handshake.search(query="chonk your texts", limit=2)
for result in results:
    print(result["score"], result["text"])

from chonkie import QdrantHandshake

# Initialize the handshake
handshake = QdrantHandshake(
    url="YOUR_CLOUD_URL",
    api_key="YOUR_API_KEY",
    collection_name="my_documents",
)
embedding = handshake.embedding_model.embed("chonk your texts").tolist()
results = handshake.search(embedding=embedding, limit=2)
for result in results:
    print(result["score"], result["text"])

from chonkie import QdrantHandshake, SemanticChunker

# Initialize the handshake
handshake = QdrantHandshake(
    url="YOUR_CLOUD_URL",
    api_key="YOUR_API_KEY",
    collection_name="my_documents",
)

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

# Search the handshake
results = handshake.search(
    embedding=chunks[0].sentences[0].embedding, 
    limit=2,
)
for result in results:
    print(result["score"], result["text"])