> ## Documentation Index
> Fetch the complete documentation index at: https://embedchain-user-dyadav-remove-pipeline.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 🗄️ Vector databases

## Overview

Utilizing a vector database alongside Embedchain is a seamless process. All you need to do is configure it within the YAML configuration file. We've provided examples for each supported database below:

<CardGroup cols={4}>
  <Card title="ChromaDB" href="#chromadb" />

  <Card title="Elasticsearch" href="#elasticsearch" />

  <Card title="OpenSearch" href="#opensearch" />

  <Card title="Zilliz" href="#zilliz" />

  <Card title="LanceDB" href="#lancedb" />

  <Card title="Pinecone" href="#pinecone" />

  <Card title="Qdrant" href="#qdrant" />

  <Card title="Weaviate" href="#weaviate" />
</CardGroup>

## ChromaDB

<CodeGroup>
  ```python main.py
  from embedchain import App

  # load chroma configuration from yaml file
  app = App.from_config(config_path="config1.yaml")
  ```

  ```yaml config1.yaml
  vectordb:
    provider: chroma
    config:
      collection_name: 'my-collection'
      dir: db
      allow_reset: true
  ```

  ```yaml config2.yaml
  vectordb:
    provider: chroma
    config:
      collection_name: 'my-collection'
      host: localhost
      port: 5200
      allow_reset: true
  ```
</CodeGroup>

## Elasticsearch

Install related dependencies using the following command:

```bash
pip install --upgrade 'embedchain[elasticsearch]'
```

<Note>
  You can configure the Elasticsearch connection by providing either `es_url` or `cloud_id`. If you are using the Elasticsearch Service on Elastic Cloud, you can find the `cloud_id` on the [Elastic Cloud dashboard](https://cloud.elastic.co/deployments).
</Note>

You can authorize the connection to Elasticsearch by providing either `basic_auth`, `api_key`, or `bearer_auth`.

<CodeGroup>
  ```python main.py
  from embedchain import App

  # load elasticsearch configuration from yaml file
  app = App.from_config(config_path="config.yaml")
  ```

  ```yaml config.yaml
  vectordb:
    provider: elasticsearch
    config:
      collection_name: 'es-index'
      cloud_id: 'deployment-name:xxxx'
      basic_auth:
        - elastic
        - <your_password>
      verify_certs: false
  ```
</CodeGroup>

## OpenSearch

Install related dependencies using the following command:

```bash
pip install --upgrade 'embedchain[opensearch]'
```

<CodeGroup>
  ```python main.py
  from embedchain import App

  # load opensearch configuration from yaml file
  app = App.from_config(config_path="config.yaml")
  ```

  ```yaml config.yaml
  vectordb:
    provider: opensearch
    config:
      collection_name: 'my-app'
      opensearch_url: 'https://localhost:9200'
      http_auth:
        - admin
        - admin
      vector_dimension: 1536
      use_ssl: false
      verify_certs: false
  ```
</CodeGroup>

## Zilliz

Install related dependencies using the following command:

```bash
pip install --upgrade 'embedchain[milvus]'
```

Set the Zilliz environment variables `ZILLIZ_CLOUD_URI` and `ZILLIZ_CLOUD_TOKEN` which you can find it on their [cloud platform](https://cloud.zilliz.com/).

<CodeGroup>
  ```python main.py
  import os
  from embedchain import App

  os.environ['ZILLIZ_CLOUD_URI'] = 'https://xxx.zillizcloud.com'
  os.environ['ZILLIZ_CLOUD_TOKEN'] = 'xxx'

  # load zilliz configuration from yaml file
  app = App.from_config(config_path="config.yaml")
  ```

  ```yaml config.yaml
  vectordb:
    provider: zilliz
    config:
      collection_name: 'zilliz_app'
      uri: https://xxxx.api.gcp-region.zillizcloud.com
      token: xxx
      vector_dim: 1536
      metric_type: L2
  ```
</CodeGroup>

## LanceDB

*Coming soon*

## Pinecone

Install pinecone related dependencies using the following command:

```bash
pip install --upgrade 'embedchain[pinecone]'
```

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
  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
  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
  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>

## Qdrant

In order to use Qdrant as a vector database, set the environment variables `QDRANT_URL` and `QDRANT_API_KEY` which you can find on [Qdrant Dashboard](https://cloud.qdrant.io/).

<CodeGroup>
  ```python main.py
  from embedchain import App

  # load qdrant configuration from yaml file
  app = App.from_config(config_path="config.yaml")
  ```

  ```yaml config.yaml
  vectordb:
    provider: qdrant
    config:
      collection_name: my_qdrant_index
  ```
</CodeGroup>

## Weaviate

In order to use Weaviate as a vector database, set the environment variables `WEAVIATE_ENDPOINT` and `WEAVIATE_API_KEY` which you can find on [Weaviate dashboard](https://console.weaviate.cloud/dashboard).

<CodeGroup>
  ```python main.py
  from embedchain import App

  # load weaviate configuration from yaml file
  app = App.from_config(config_path="config.yaml")
  ```

  ```yaml config.yaml
  vectordb:
    provider: weaviate
    config:
      collection_name: my_weaviate_index
  ```
</CodeGroup>

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