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The PineconeHandshake class provides seamless integration between Chonkie’s chunking system and Pinecone, a managed vector database. Embed and store your Chonkie chunks in Pinecone directly from the Chonkie SDK.

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Installation

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

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Initialization

from chonkie import PineconeHandshake
handshake = PineconeHandshake(api_key="YOUR_API_KEY")

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Parameters

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client
Optional[pinecone.Pinecone]
default:"None"
Pinecone client instance. If not provided, a new client will be created based on other parameters.
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api_key
Optional[str]
default:"None"
Pinecone API key for authentication.
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index_name
Union[str, Literal['random']]
default:"random"
Name of the index 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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dimension
Optional[int]
default:"None"
Dimension of the embeddings. If not provided, will be inferred from the embedding model.
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**kwargs
Dict[str, Any]
default:"{}"
Additional keyword arguments to pass to the Pinecone client or index creation.

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Writing Chunks to Pinecone

from chonkie import PineconeHandshake, SemanticChunker    

# Initialize the handshake
handshake = PineconeHandshake(api_key="YOUR_API_KEY", index_name="my_documents")

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

# Write chunks to Pinecone
handshake.write(chunks)

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Searching Chunks in Pinecone

You can retrieve the most similar chunks from your Pinecone index using the search method: 
from chonkie import PineconeHandshake

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