mem0/docs/advanced/vector_database.mdx

---
title: '💾 Vector Database'
---

We support `Chroma`, `Elasticsearch` and `OpenSearch` as vector databases.
`Chroma` is used as a default database.

## Elasticsearch

### Minimal Example

In order to use `Elasticsearch` as vector database we need to use App type `CustomApp`.

1. Set the environment variables in a `.env` file.
```
OPENAI_API_KEY=sk-SECRETKEY
ELASTICSEARCH_API_KEY=SECRETKEY==
ELASTICSEARCH_URL=https://secret-domain.europe-west3.gcp.cloud.es.io:443
```
Please note that the key needs certain privileges. For testing you can just toggle off `restrict privileges` under `/app/management/security/api_keys/` in your web interface.

2. Load the app
```python
from embedchain import CustomApp
from embedchain.embedder.openai import OpenAIEmbedder
from embedchain.llm.openai import OpenAILlm
from embedchain.vectordb.elasticsearch import ElasticsearchDB

es_app = CustomApp(
    llm=OpenAILlm(),
    embedder=OpenAIEmbedder(),
    db=ElasticsearchDB(),
)
```

### More custom settings

You can get a URL for elasticsearch in the cloud, or run it locally. 
The following example shows you how to configure embedchain to work with a locally running elasticsearch.

Instead of using an API key, we use http login credentials. The localhost url can be defined in .env or in the config.

```python
import os

from embedchain import CustomApp
from embedchain.config import CustomAppConfig, ElasticsearchDBConfig
from embedchain.embedder.openai import OpenAIEmbedder
from embedchain.llm.openai import OpenAILlm
from embedchain.vectordb.elasticsearch import ElasticsearchDB

es_config = ElasticsearchDBConfig(
    # elasticsearch url or list of nodes url with different hosts and ports.
    es_url='https://localhost:9200',
    # pass named parameters supported by Python Elasticsearch client
    http_auth=("elastic", "secret"),
    ca_certs="~/binaries/elasticsearch-8.7.0/config/certs/http_ca.crt" # your cert path
    # verify_certs=False # Alternative, if you aren't using certs
) # pass named parameters supported by elasticsearch-py

es_app = CustomApp(
    config=CustomAppConfig(log_level="INFO"),
    llm=OpenAILlm(),
    embedder=OpenAIEmbedder(),
    db=ElasticsearchDB(config=es_config),
)
```
3. This should log your connection details to the console.
4. Alternatively to a URL, you `ElasticsearchDBConfig` accepts `es_url` as a list of nodes url with different hosts and ports.
5. Additionally we can pass named parameters supported by Python Elasticsearch client.


## OpenSearch 🔍

To use OpenSearch as a vector database with a CustomApp, follow these simple steps:

1. Set the `OPENAI_API_KEY` environment variable:

```
OPENAI_API_KEY=sk-xxxx
```

2. Define the OpenSearch configuration in your Python code:

```python
from embedchain import CustomApp
from embedchain.config import OpenSearchDBConfig
from embedchain.embedder.openai import OpenAIEmbedder
from embedchain.llm.openai import OpenAILlm
from embedchain.vectordb.opensearch import OpenSearchDB

opensearch_url = "https://localhost:9200"
http_auth = ("username", "password")

db_config = OpenSearchDBConfig(
    opensearch_url=opensearch_url,
    http_auth=http_auth,
    collection_name="embedchain-app",
    use_ssl=True,
    timeout=30,
)
db = OpenSearchDB(config=db_config)
```

2. Instantiate the app and add data:

```python
app = CustomApp(llm=OpenAILlm(), embedder=OpenAIEmbedder(), db=db)
app.add("https://en.wikipedia.org/wiki/Elon_Musk")
app.add("https://www.forbes.com/profile/elon-musk")
app.add("https://www.britannica.com/biography/Elon-Musk")
```

3. You're all set! Start querying using the following command:

```python
app.query("What is the net worth of Elon Musk?")
```