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* Update document embedding values in example * Update document embeddings of Qdrant example to 768 dimensions * Remove spaces in embedding initialization * Update URL in agent output example to remove the 403 error
67 lines
2.8 KiB
Plaintext
67 lines
2.8 KiB
Plaintext
---
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title: "PineconeDocumentStore"
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id: pinecone-document-store
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slug: "/pinecone-document-store"
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description: "Use a Pinecone vector database with Haystack."
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---
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# PineconeDocumentStore
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Use a Pinecone vector database with Haystack.
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<div className="key-value-table">
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| | |
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| --- | --- |
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| API reference | [Pinecone](/reference/integrations-pinecone) |
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| GitHub link | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pinecone |
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</div>
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[Pinecone](https://www.pinecone.io/) is a cloud-based vector database. It is fast and easy to use.
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Unlike other solutions (such as Qdrant and Weaviate), it can’t run locally on the user's machine but provides a generous free tier.
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### Installation
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You can simply install the Pinecone Haystack integration with:
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```shell
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pip install pinecone-haystack
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```
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### Initialization
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- To use Pinecone as a Document Store in Haystack, sign up for a free Pinecone [account](https://app.pinecone.io/) and get your API key.
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The Pinecone API key can be explicitly provided or automatically read from the environment variable `PINECONE_API_KEY` (recommended).
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- In Haystack, each `PineconeDocumentStore` operates in a specific namespace of an index. If not provided, both index and namespace are `default`.
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If the index already exists, the Document Store connects to it. Otherwise, it creates a new index.
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- When creating a new index, you can provide a `spec` in the form of a dictionary. This allows choosing between serverless and pod deployment options and setting additional parameters. Refer to the [Pinecone documentation](https://docs.pinecone.io/reference/api/control-plane/create_index) for more details. If not provided, a default spec with serverless deployment in the `us-east-1` region will be used (compatible with the free tier).
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- You can provide `dimension` and `metric`, but they are only taken into account if the Pinecone index does not already exist.
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Then, you can use the Document Store like this:
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```python
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from haystack import Document
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from haystack_integrations.document_stores.pinecone import PineconeDocumentStore
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## Make sure you have the PINECONE_API_KEY environment variable set
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document_store = PineconeDocumentStore(
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index="default",
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namespace="default",
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dimension=5,
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metric="cosine",
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spec={"serverless": {"region": "us-east-1", "cloud": "aws"}}
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)
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document_store.write_documents([
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Document(content="This is first", embedding=[0.1]*5),
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Document(content="This is second", embedding=[0.1, 0.2, 0.3, 0.4, 0.5])
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])
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print(document_store.count_documents())
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```
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### Supported Retrievers
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[`PineconeEmbeddingRetriever`](../pipeline-components/retrievers/pineconedenseretriever.mdx): Retrieves documents from the `PineconeDocumentStore` based on their dense embeddings (vectors).
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