embedchain
Embedchain is a framework to easily create LLM powered bots over any dataset. If you want a javascript version, check out embedchain-js
🤝 Schedule a 1-on-1 Session
Book a 1-on-1 Session with Taranjeet, the founder, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.
🔧 Quick install
pip install embedchain🔍 Demo
Try out embedchain in your browser:
📖 Documentation
The documentation for embedchain can be found at docs.embedchain.ai.
💻 Usage
Embedchain empowers you to create chatbot models similar to ChatGPT, using your own evolving dataset.
Data Types Supported
- Youtube video
- PDF file
- Web page
- Sitemap
- Doc file
- Code documentation website loader
- Notion
Queries
For example, you can use Embedchain to create an Elon Musk bot using the following code:
import os
from embedchain import App
# Create a bot instance
os.environ["OPENAI_API_KEY"] = "YOUR API KEY"
elon_bot = App()
# Embed online resources
elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
elon_bot.add("https://tesla.com/elon-musk")
elon_bot.add("https://www.youtube.com/watch?v=MxZpaJK74Y4")
# Query the bot
elon_bot.query("How many companies does Elon Musk run?")
# Answer: Elon Musk runs four companies: Tesla, SpaceX, Neuralink, and The Boring Company🤝 Contributing
Contributions are welcome! Please check out the issues on the repository, and feel free to open a pull request. For more information, please see the contributing guidelines.
For more refrence, please go through Development Guide and Documentation Guide.
Citation
If you utilize this repository, please consider citing it with:
@misc{embedchain,
author = {Taranjeet Singh},
title = {Embedchain: Framework to easily create LLM powered bots over any dataset},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/embedchain/embedchain}},
}
