t6_mem0/docs/components/vector-databases.mdx

---
title: 🗄️ 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>
  <Card title="Elasticsearch" href="#elasticsearch"></Card>
  <Card title="OpenSearch" href="#opensearch"></Card>
  <Card title="Zilliz" href="#zilliz"></Card>
  <Card title="LanceDB" href="#lancedb"></Card>
  <Card title="Pinecone" href="#pinecone"></Card>
  <Card title="Qdrant" href="#qdrant"></Card>
  <Card title="Weaviate" href="#weaviate"></Card>
</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" />