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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.

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Installation

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

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Basic Usage

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Initialization

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"
)

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Parameters

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client
Optional[weaviate.Client]
default:"None"
Weaviate client instance. If not provided, a new client will be created based on other parameters.
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collection_name
Union[str, Literal['random']]
default:"random"
Name of the collection to use. If “random”, a unique name will be generated.
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embedding_model
Union[str, BaseEmbeddings]
default:"minishlab/potion-retrieval-32M"
Embedding model to use. Can be a model name or a BaseEmbeddings instance.
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url
Optional[str]
default:"None"
URL of the Weaviate server. If provided, will connect to this server.
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api_key
Optional[str]
default:"None"
API key for Weaviate Cloud authentication.
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auth_config
Optional[dict[str, Any]]
default:"None"
OAuth configuration for authentication (optional).
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batch_size
int
default:"100"
Batch size for batch operations.
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batch_dynamic
bool
default:"True"
Whether to use dynamic batching.
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batch_timeout_retries
int
default:"3"
Number of retries for batch timeouts.
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additional_headers
Optional[dict[str, str]]
default:"None"
Additional headers for the Weaviate client.

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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)

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Searching Chunks in Weaviate

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