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The MilvusHandshake class provides seamless integration between Chonkie’s chunking system and Milvus, a powerful, open-source vector database. Embed and store your Chonkie chunks in a Milvus collection, with automatic schema and index creation, without ever leaving the Chonkie SDK.

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Installation

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

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Basic Usage

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Initialization

from chonkie import MilvusHandshake

# Connects to Milvus at http://localhost:19530 by default
handshake = MilvusHandshake()

from chonkie import MilvusHandshake

# Recommended for connecting to remote or secured instances
handshake = MilvusHandshake(
    uri=os.getenv("MILVUS_URI"),
    user=os.getenv("MILVUS_USER"),
    api_key=os.getenv("MILVUS_API_KEY"),
    collection_name="test_collection",
)

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Parameters

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collection_name
Union[str, Literal['random']]
default:"random"
The name of the Milvus collection to use. If “random”, a unique name is generated.
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embedding_model
Union[str, BaseEmbeddings]
default:"minishlab/potion-retrieval-32M"
The embedding model to use for creating vectors.
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uri
Optional[str]
default:"None"
The full URI to connect to Milvus. This is the preferred method for specifying connection details.
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host
str
default:"localhost"
The host of the Milvus instance. Used if uri is not provided.
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port
str
default:"19530"
The port of the Milvus instance. Used if uri is not provided.
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alias
str
default:"default"
The connection alias to use for this Milvus connection.

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Writing Chunks to Milvus

from chonkie import MilvusHandshake, SentenceChunker

# Initialize the handshake for your deployment
handshake = MilvusHandshake(
    uri="http://localhost:19530",
    collection_name="my_documents",
)

# Create some chunks
chunker = SentenceChunker()
chunks = chunker.chunk("Milvus stores data in collections. Chonkie makes ingestion easy!")

# Write chunks to the Milvus collection
handshake.write(chunks)

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Searching Chunks in Milvus

You can retrieve the most similar chunks from your Milvus collection using the search method. 
from chonkie import MilvusHandshake

# Initialize the handshake to connect to your collection
handshake = MilvusHandshake(
    uri="http://localhost:19530",
    collection_name="my_documents",
)

results = handshake.search(query="easy data ingestion", limit=2)

from chonkie import MilvusHandshake

# Initialize the handshake
handshake = MilvusHandshake(
    uri="http://localhost:19530",
    collection_name="my_documents",
)

# Generate an embedding vector for your query
embedding = handshake.embedding_model.embed("easy data ingestion")

results = handshake.search(embedding=embedding, limit=2)