This guide shows you how to set up and use Pinecone in minutes.

To get started in your browser, use the “Pinecone quickstart” colab notebook.

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1. Install an SDK

Pinecone SDKs provide convenient programmatic access to the Pinecone APIs.

Install the SDK for your preferred language:

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2. Get an API key

You need an API key to make calls to your Pinecone project.

Create a new API key in the Pinecone console, or use the connect widget below to generate a key. If you don’t have a Pinecone account, the widget will sign you up for the free Starter plan.

Copy your generated key:

PINECONE_API_KEY="{{YOUR_API_KEY}}"

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3. Create an index

An index defines the dimension of vectors to be stored and the similarity metric to be used when querying them. Normally, you choose a dimension and similarity metric based on the embedding model used to create your vectors. For this quickstart, however, you’ll use a configuration that makes it easy to verify your query results.

Use your API key to initialize a client, and then create a serverless index named example-index that stores vectors of 2 dimensions and performs nearest-neighbor search using the cosine similarity metric:

In this Quickstart, you are creating a serverless index in the us-east-1 region of AWS (the default). Pinecone supports multiple cloud providers and regions.

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4. Load data

Within an index, vectors are stored in namespaces, and all upserts, queries, and other data operations always target one namespace:

Namespaces are essential for implementing multitenancy when you need to isolate the data of each customer/user.

Now that you’ve created your index, target the example-index index and use the upsert operation to write six 2-dimensional vectors into 2 distinct namespaces:

When you need to load large numbers of records, it is recommended to import from object storage or upsert in large batches.

Pinecone is eventually consistent, so there can be a delay before your upserted vectors are available to query. Use the describe_index_stats operation to check if the current vector count matches the number of vectors you upserted:

Query each namespace in your index for the 3 vectors that are most similar to an example 2-dimensional query vector:

As you put more demands on Pinecone, you’ll see it returning low-latency, accurate results at huge scales, with indexes of up to billions of vectors.

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6. Clean up

When you no longer need the example-index index, use the delete_index operation to delete it:

For production indexes, consider enabling deletion protection.

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Next steps

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