Embedchain is a framework to easily create LLM powered bots over any dataset. If you want a javascript version, check out embedchain-js

pip install embedchain

For Windows, you'll also need C++ build tools installed.

• [2023/07/19] Released support for 🦙 llama2 model. Start creating your llama2 based bots like this:

  import os
  
  from embedchain import Llama2App
  
  os.environ['REPLICATE_API_TOKEN'] = "REPLICATE API TOKEN"
  
  zuck_bot = Llama2App()
  
  # Embed your data
  zuck_bot.add("https://www.youtube.com/watch?v=Ff4fRgnuFgQ")
  zuck_bot.add("https://en.wikipedia.org/wiki/Mark_Zuckerberg")
  
  # Nice, your bot is ready now. Start asking questions to your bot.
  zuck_bot.query("Who is Mark Zuckerberg?")
  # Answer: Mark Zuckerberg is an American internet entrepreneur and business magnate. He is the co-founder and CEO of Facebook.

Try out embedchain in your browser:

The documentation for embedchain can be found at docs.embedchain.ai.

Embedchain empowers you to create chatbot models similar to ChatGPT, using your own evolving dataset.

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

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.

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}},
}