---
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 App
# load chroma configuration from yaml file
app = App.from_config(yaml_path="chroma-config-1.yaml")
```
```yaml chroma-config-1.yaml
vectordb:
provider: chroma
config:
collection_name: 'my-collection'
dir: db
allow_reset: true
```
```yaml chroma-config-2.yaml
vectordb:
provider: chroma
config:
collection_name: 'my-collection'
host: localhost
port: 5200
allow_reset: true
```
## Elasticsearch
```python main.py
from embedchain import App
# load elasticsearch configuration from yaml file
app = App.from_config(yaml_path="elasticsearch.yaml")
```
```yaml elasticsearch.yaml
vectordb:
provider: elasticsearch
config:
collection_name: 'es-index'
es_url: http://localhost:9200
allow_reset: true
api_key: xxx
```
## OpenSearch
```python main.py
from embedchain import App
# load opensearch configuration from yaml file
app = App.from_config(yaml_path="opensearch.yaml")
```
```yaml opensearch.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
```python main.py
from embedchain import App
# load zilliz configuration from yaml file
app = App.from_config(yaml_path="zilliz.yaml")
```
```yaml zilliz.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
_Coming soon_
## Qdrant
_Coming soon_
## Weaviate
_Coming soon_