Chonkie Documentation

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.

Installation

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

pip install chonkie[pinecone]

Initialization

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

import pinecone
client = pinecone.Pinecone(api_key="YOUR_API_KEY")
handshake = PineconeHandshake(client=client, index_name="my_index")

handshake = PineconeHandshake(
    api_key="YOUR_API_KEY",
    index_name="my_index",
    embedding_model="minishlab/potion-retrieval-32M",
)

Parameters

Optional[pinecone.Pinecone]

default:"None"

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

Optional[str]

default:"None"

Pinecone API key for authentication.

Union[str, Literal['random']]

default:"random"

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

Union[str, BaseEmbeddings]

default:"minishlab/potion-retrieval-32M"

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

Optional[int]

default:"None"

Dimension of the embeddings. If not provided, will be inferred from the embedding model.

dict[str, Any]

default:"{}"

Additional keyword arguments to pass to the Pinecone client or index creation.

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)

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"])

from chonkie import PineconeHandshake

# Initialize the handshake
handshake = PineconeHandshake(api_key="YOUR_API_KEY", index_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 PineconeHandshake, SemanticChunker

# Initialize the handshake
handshake = PineconeHandshake(api_key="YOUR_API_KEY", index_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"])

Getting Started

API Server

Chefs

Fetchers

Chunkers

Embeddings

Refinery

Handshakes

Porters

Utils

Experimental

Changelog

Pinecone Handshake

Installation

Initialization

Parameters

Writing Chunks to Pinecone

Searching Chunks in Pinecone

​Installation

​Initialization

​Parameters

​Writing Chunks to Pinecone

​Searching Chunks in Pinecone

Installation

Initialization

Parameters

Writing Chunks to Pinecone

Searching Chunks in Pinecone