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

# Pinecone

> Pinecone is a vector database that enables fast and accurate vector search for building AI applications. It provides the infrastructure for the long-term memory and retrieval needed to develop state-of-the-art AI systems.

## Setup

1. Install Langtrace's SDK and [initialize](/quickstart) the SDK in your code.

*Note: You'll need API keys from Langtrace and Pinecone. Sign up for [Langtrace](https://langtrace.ai) and/or [Pinecone](https://www.pinecone.io/) if you haven't done so already.*

```bash Python theme={null}
# Install the SDK
pip install -U langtrace-python-sdk pinecone
```

```bash Typescript theme={null}
npm install @pinecone-database/pinecone-client
```

2. Setup environment variables:

```bash Shell theme={null}
export LANGTRACE_API_KEY=YOUR_LANGTRACE_API_KEY
export PINECONE_API_KEY=YOUR_PINECONE_API_KEY
```

## Usage

Generate a simple output with your deployment's model:

<CodeGroup>
  ```python Python theme={null}
  import os
     from langtrace_python_sdk import langtrace # Must precede any llm module imports
  langtrace.init(api_key = os.environ['LANGTRACE_API_KEY'])
  from pinecone import Pinecone, ServerlessSpec
  pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])

  # Create an Index and upsert some data in Pinecone:
  pc.create_index(
      name="index",
      dimension=8, # Replace with your model dimensions
      metric="euclidean", # Replace with your model metric
      spec=ServerlessSpec(
          cloud="aws",
          region="us-east-1"
      )
  )
  index = pc.Index("index")
  index.upsert(
    vectors=[
      {"id": "A", "values": [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]},
      {"id": "B", "values": [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]},
      {"id": "C", "values": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]},
      {"id": "D", "values": [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]}
    ]
  )

  ```

  ```javascript Typescript theme={null}
  import { init as langtraceInit } from 'langtrase/typescript-sdk';
  import { PineconeClient, ServerlessSpec } from '@pinecone-database/pinecone-client';

  langtraceInit({
   apiKey: langtraceApiKey,
   });

  // Step 3: Initialize Pinecone client
  const pinecone = new PineconeClient({
   apiKey: pineconeApiKey,
  });

  // Step 4: Create an Index and upsert some data in Pinecone
  (async () => {
  await pinecone.createIndex({
  name: "index",
  dimension: 8, // Replace with your model dimensions
  metric: "euclidean", // Replace with your model metric
  spec: new ServerlessSpec({
  cloud: "aws",
  region: "us-east-1",
  }),
  });

  const index = pinecone.Index("index");

  await index.upsert({
  vectors: [
  { id: "A", values: [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1] },
  { id: "B", values: [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2] },
  { id: "C", values: [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3] },
  { id: "D", values: [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4] },
  ],
  });
  })();

  ```
</CodeGroup>

You can now view your traces on the Langtrace dashboard:

<img src="https://mintcdn.com/langtraceai-2/m7BVnnhK3Cx2yRxS/images/pinecone.png?fit=max&auto=format&n=m7BVnnhK3Cx2yRxS&q=85&s=37ef5f6fb9dfaf4a93a89e8468c631e8" alt="traces" width="2086" height="977" data-path="images/pinecone.png" />

Want to see more supported methods? Checkout the sample code in the [Langtrace Pinecone Python Example](https://github.com/Scale3-Labs/langtrace-recipes/blob/main/integrations/vector-db/pinecone/starter.ipynb) repository.