---
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:
## ChromaDB
```python main.py
from embedchain import Pipeline as App
# load chroma configuration from yaml file
app = App.from_config(yaml_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
```
## Elasticsearch
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[elasticsearch]'
```
```python main.py
from embedchain import Pipeline as App
# load elasticsearch configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
```
```yaml config.yaml
vectordb:
provider: elasticsearch
config:
collection_name: 'es-index'
es_url: http://localhost:9200
allow_reset: true
api_key: xxx
```
## OpenSearch
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[opensearch]'
```
```python main.py
from embedchain import Pipeline as App
# load opensearch configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
```
```yaml config.yaml
vectordb:
provider: opensearch
config:
opensearch_url: 'https://localhost:9200'
http_auth:
- admin
- admin
vector_dimension: 1536
collection_name: 'my-app'
use_ssl: false
verify_certs: false
```
## 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/).
```python main.py
import os
from embedchain import Pipeline as 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(yaml_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
```
## 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 variables `PINECONE_API_KEY` and `PINECONE_ENV` which you can find on [Pinecone dashboard](https://app.pinecone.io/).
```python main.py
from embedchain import Pipeline as App
# load pinecone configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
```
```yaml config.yaml
vectordb:
provider: pinecone
config:
metric: cosine
vector_dimension: 1536
collection_name: my-pinecone-index
```
## 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/).
```python main.py
from embedchain import Pipeline as App
# load qdrant configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
```
```yaml config.yaml
vectordb:
provider: qdrant
config:
collection_name: my_qdrant_index
```
## 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).
```python main.py
from embedchain import Pipeline as App
# load weaviate configuration from yaml file
app = App.from_config(yaml_path="config.yaml")
```
```yaml config.yaml
vectordb:
provider: weaviate
config:
collection_name: my_weaviate_index
```