🗄️ 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

Elasticsearch

OpenSearch

Zilliz

LanceDB

Pinecone

Qdrant

Weaviate

ChromaDB

main.py

from embedchain import App

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

Elasticsearch

Install related dependencies using the following command:

pip install --upgrade 'embedchain[elasticsearch]'

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.

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

main.py

from embedchain import App

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

OpenSearch

Install related dependencies using the following command:

pip install --upgrade 'embedchain[opensearch]'

main.py

from embedchain import App

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

Zilliz

Install related dependencies using the following command:

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.

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")

LanceDB

Coming soon

Pinecone

Install pinecone related dependencies using the following command:

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.

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")

You can find more information about Pinecone configuration here. 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}.

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.

main.py

from embedchain import App

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

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.

main.py

from embedchain import App

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

Slack

Let us know on our slack community

Discord

Let us know on discord community

GitHub

Open an issue on our GitHub

Schedule a call

Schedule a call with Embedchain founder