> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chonkie.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Pinecone Handshake

> Export Chonkie's Chunks into a Pinecone index.

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:

```bash theme={"system"}
pip install chonkie[pinecone]
```

## Initialization

<CodeGroup>
  ```python Initialize using chonkie theme={"system"}
  from chonkie import PineconeHandshake
  handshake = PineconeHandshake(api_key="YOUR_API_KEY")
  ```

  ```python initialize using the client theme={"system"}
  import pinecone
  client = pinecone.Pinecone(api_key="YOUR_API_KEY")
  handshake = PineconeHandshake(client=client, index_name="my_index")
  ```

  ```python specify embedding model theme={"system"}
  handshake = PineconeHandshake(
      api_key="YOUR_API_KEY",
      index_name="my_index",
      embedding_model="minishlab/potion-retrieval-32M",
  )
  ```
</CodeGroup>

### Parameters

<ParamField path="client" type="Optional[pinecone.Pinecone]" default="None">
  Pinecone client instance. If not provided, a new client will be created based on other parameters.
</ParamField>

<ParamField path="api_key" type="Optional[str]" default="None">
  Pinecone API key for authentication.
</ParamField>

<ParamField path="index_name" type="Union[str, Literal['random']]" default="random">
  Name of the index to use. If "random", a unique name will be generated.
</ParamField>

<ParamField path="embedding_model" type="Union[str, BaseEmbeddings]" default="minishlab/potion-retrieval-32M">
  Embedding model to use. Can be a model name or a BaseEmbeddings instance.
</ParamField>

<ParamField path="dimension" type="Optional[int]" default="None">
  Dimension of the embeddings. If not provided, will be inferred from the embedding model.
</ParamField>

<ParamField path="**kwargs" type="dict[str, Any]" default="{}">
  Additional keyword arguments to pass to the Pinecone client or index creation.
</ParamField>

## Writing Chunks to Pinecone

```python theme={"system"}
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:

<CodeGroup>
  ```python search using a query theme={"system"}
  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"])
  ```

  ```python search using embedding theme={"system"}
  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"])
  ```

  ```python search using chonkie chunks theme={"system"}
  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"])
  ```
</CodeGroup>