Weaviate Handshake

Export Chonkie’s Chunks into a Weaviate collection.

The WeaviateHandshake class provides seamless integration between Chonkie’s chunking system and Weaviate, a powerful vector database.

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

Installation

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

pip install chonkie[weaviate]

Basic Usage

Initialization

Initialize using chonkie

from chonkie import WeaviateHandshake

# Initialize with default settings (local Weaviate)
handshake = WeaviateHandshake()

# Or connect to a Weaviate server
handshake = WeaviateHandshake(url="http://localhost:8080", api_key= "YOUR_API_KEY")

import weaviate
from chonkie import WeaviateHandshake

client = weaviate.connect_to_local()
handshake = WeaviateHandshake(client=client, collection_name="my_collection")

from chonkie import WeaviateHandshake

handshake = WeaviateHandshake(
    url="YOUR_CLOUD_URL",
    api_key="YOUR_API_KEY"
)

Parameters

client · Optional[weaviate.Client] · default: "None"

Weaviate 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 Weaviate server. If provided, will connect to this server.

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

API key for Weaviate Cloud authentication.

auth_config · Optional[dict[str, Any]] · default: "None"

OAuth configuration for authentication (optional).

batch_size · int · default: "100"

Batch size for batch operations.

batch_dynamic · bool · default: "True"

Whether to use dynamic batching.

batch_timeout_retries · int · default: "3"

Number of retries for batch timeouts.

additional_headers · Optional[dict[str, str]] · default: "None"

Additional headers for the Weaviate client.

Writing Chunks to Weaviate

from chonkie import WeaviateHandshake, SemanticChunker    

# Initialize the handshake
handshake = WeaviateHandshake(
    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 Weaviate
handshake.write(chunks)

Searching Chunks in Weaviate

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

search using a query

from chonkie import WeaviateHandshake

# Initialize the handshake
handshake = WeaviateHandshake(
    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 WeaviateHandshake

# Initialize the handshake
handshake = WeaviateHandshake(
    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 WeaviateHandshake, SemanticChunker

# Initialize the handshake
handshake = WeaviateHandshake(
    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"])