Embedchain home page
โจ Search embedchain docs...
โK
Ask AI
GitHub
Join our slack
Join our slack
Search...
Navigation
๐๏ธ Vector databases
Documentation
Examples
API Reference
Talk to founders
Get Started
โก Quickstart
๐ Introduction
โ FAQs
๐ป Full stack
๐ Integrations
Use cases
๐งฑ Introduction
๐ค Chatbots
โ Question Answering
๐ Semantic Search
Components
๐งฉ Introduction
๐๏ธ Data sources
๐๏ธ Vector databases
๐ค Large language models (LLMs)
๐งฉ Embedding models
๐ฌ Evaluation
Deployment
Overview
Fly.io
Modal.com
Render.com
Railway.app
Streamlit.io
Gradio.app
Huggingface.co
Embedchain.ai
Community
๐ค Connect with Us
Contributing
๐ Guidelines
๐จโ๐ป Development
๐ Documentation
๐ Python
๐จ Javascript
Product
๐ Release Notes
On this page
Overview
๐๏ธ Vector databases
โ
Overview
Utilizing a vector database alongside Embedchain is a seamless process. All you need to do is configure it within the YAML configuration file. Weโve provided examples for each supported database below:
ChromaDB
Elasticsearch
OpenSearch
Zilliz
LanceDB
Pinecone
Qdrant
Weaviate
If you can't find specific feature or run into issues, please feel free to reach out through one of the following channels.
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
Was this page helpful?
Yes
No
Suggest edits
Raise issue
Assistant
Responses are generated using AI and may contain mistakes.