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

# Pinecone

## Overview

Install pinecone related dependencies using the following command:

```bash theme={null}
pip install --upgrade 'pinecone-client pinecone-text'
```

In order to use Pinecone as vector database, set the environment variable `PINECONE_API_KEY` which you can find on [Pinecone dashboard](https://app.pinecone.io/).

<CodeGroup>
  ```python main.py theme={null}
  from embedchain import App

  # Load pinecone configuration from yaml file
  app = App.from_config(config_path="pod_config.yaml")
  # Or
  app = App.from_config(config_path="serverless_config.yaml")
  ```

  ```yaml pod_config.yaml theme={null}
  vectordb:
    provider: pinecone
    config:
      metric: cosine
      vector_dimension: 1536
      index_name: my-pinecone-index
      pod_config:
        environment: gcp-starter
        metadata_config:
          indexed:
            - "url"
            - "hash"
  ```

  ```yaml serverless_config.yaml theme={null}
  vectordb:
    provider: pinecone
    config:
      metric: cosine
      vector_dimension: 1536
      index_name: my-pinecone-index
      serverless_config:
        cloud: aws
        region: us-west-2
  ```
</CodeGroup>

<br />

<Note>
  You can find more information about Pinecone configuration [here](https://docs.pinecone.io/docs/manage-indexes#create-a-pod-based-index).
  You can also optionally provide `index_name` as a config param in yaml file to specify the index name. If not provided, the index name will be `{collection_name}-{vector_dimension}`.
</Note>

## Usage

### Hybrid search

Here is an example of how you can do hybrid search using Pinecone as a vector database through Embedchain.

```python theme={null}
import os

from embedchain import App

config = {
    'app': {
        "config": {
            "id": "ec-docs-hybrid-search"
        }
    },
    'vectordb': {
        'provider': 'pinecone',
        'config': {
            'metric': 'dotproduct',
            'vector_dimension': 1536,
            'index_name': 'my-index',
            'serverless_config': {
                'cloud': 'aws',
                'region': 'us-west-2'
            },
            'hybrid_search': True, # Remember to set this for hybrid search
        }
    }
}

# Initialize app
app = App.from_config(config=config)

# Add documents
app.add("/path/to/file.pdf", data_type="pdf_file", namespace="my-namespace")

# Query
app.query("<YOUR QUESTION HERE>", namespace="my-namespace")

# Chat
app.chat("<YOUR QUESTION HERE>", namespace="my-namespace")
```

Under the hood, Embedchain fetches the relevant chunks from the documents you added by doing hybrid search on the pinecone index.
If you have questions on how pinecone hybrid search works, please refer to their [offical documentation here](https://docs.pinecone.io/docs/hybrid-search).

<Snippet file="missing-vector-db-tip.mdx" />