Sen descrición

Taranjeet Singh 2889799f10 fix: Dont initialize chroma and embedding function in init config. (#289)		%!s(int64=2) %!d(string=hai) anos
.github	05a4eef6ae chores: run lint and format (#284)	%!s(int64=2) %!d(string=hai) anos
docs	c595003481 docs: setup docs for embedchain (#287)	%!s(int64=2) %!d(string=hai) anos
embedchain	2889799f10 fix: Dont initialize chroma and embedding function in init config. (#289)	%!s(int64=2) %!d(string=hai) anos
notebooks	73dd7151cb [Feat]: Add support for running chromadb in server mode with embedchain (#220)	%!s(int64=2) %!d(string=hai) anos
tests	ac68986404 Add project tools and contributing guidelines (#281)	%!s(int64=2) %!d(string=hai) anos
.env.example	ac68986404 Add project tools and contributing guidelines (#281)	%!s(int64=2) %!d(string=hai) anos
.gitignore	c595003481 docs: setup docs for embedchain (#287)	%!s(int64=2) %!d(string=hai) anos
.pre-commit-config.yaml	ac68986404 Add project tools and contributing guidelines (#281)	%!s(int64=2) %!d(string=hai) anos
CITATION.cff	736b645fea Add citation (#137)	%!s(int64=2) %!d(string=hai) anos
CONTRIBUTING.md	c595003481 docs: setup docs for embedchain (#287)	%!s(int64=2) %!d(string=hai) anos
LICENSE	65d1ff37e8 Create LICENSE	%!s(int64=2) %!d(string=hai) anos
Makefile	05a4eef6ae chores: run lint and format (#284)	%!s(int64=2) %!d(string=hai) anos
README.md	e24063caff docs: fix typos in readme (#288)	%!s(int64=2) %!d(string=hai) anos
poetry.toml	ac68986404 Add project tools and contributing guidelines (#281)	%!s(int64=2) %!d(string=hai) anos
pyproject.toml	ac68986404 Add project tools and contributing guidelines (#281)	%!s(int64=2) %!d(string=hai) anos
setup.py	05a4eef6ae chores: run lint and format (#284)	%!s(int64=2) %!d(string=hai) anos

embedchain

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

🔧 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.

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("web_page", "https://en.wikipedia.org/wiki/Elon_Musk")
elon_bot.add("web_page", "https://tesla.com/elon-musk")
elon_bot.add("youtube_video", "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.

Citation

If you utilize this repository, please consider citing it with:

@misc{embedchain,
  author = {Taranjeet Singh},
  title = {Embechain: Framework to easily create LLM powered bots over any dataset},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/embedchain/embedchain}},
}

README.md