89 lines
3.2 KiB
Markdown
89 lines
3.2 KiB
Markdown
# embedchain
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[](https://pypi.org/project/embedchain/)
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[](https://discord.gg/6PzXDgEjG5)
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[](https://twitter.com/embedchain)
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[](https://embedchain.substack.com/)
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[](https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing)
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Embedchain is a framework to easily create LLM powered bots over any dataset. If you want a javascript version, check out [embedchain-js](https://github.com/embedchain/embedchainjs)
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## 🤝 Let's Talk Embedchain!
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Schedule a [Feedback Session](https://cal.com/taranjeetio/ec) with Taranjeet, the founder, to discuss any issues, provide feedback, or explore improvements.
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## 🔧 Quick install
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```bash
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pip install embedchain
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```
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## 🔍 Demo
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Try out embedchain in your browser:
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[](https://colab.research.google.com/drive/138lMWhENGeEu7Q1-6lNbNTHGLZXBBz_B?usp=sharing)
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## 📖 Documentation
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The documentation for embedchain can be found at [docs.embedchain.ai](https://docs.embedchain.ai).
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## 💻 Usage
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Embedchain empowers you to create chatbot models similar to ChatGPT, using your own evolving dataset.
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### Data Types Supported
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* Youtube video
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* PDF file
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* Web page
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* Sitemap
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* Doc file
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* Code documentation website loader
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* Notion
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### Queries
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For example, you can use Embedchain to create an Elon Musk bot using the following code:
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```python
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import os
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from embedchain import App
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# Create a bot instance
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os.environ["OPENAI_API_KEY"] = "YOUR API KEY"
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elon_bot = App()
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# Embed online resources
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elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
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elon_bot.add("https://tesla.com/elon-musk")
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elon_bot.add("https://www.youtube.com/watch?v=MxZpaJK74Y4")
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# Query the bot
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elon_bot.query("How many companies does Elon Musk run?")
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# Answer: Elon Musk runs four companies: Tesla, SpaceX, Neuralink, and The Boring Company
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```
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## 🤝 Contributing
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Contributions are welcome! Please check out the issues on the repository, and feel free to open a pull request.
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For more information, please see the [contributing guidelines](CONTRIBUTING.md).
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For more refrence, please go through [Development Guide](https://docs.embedchain.ai/contribution/dev) and [Documentation Guide](https://docs.embedchain.ai/contribution/docs).
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## Citation
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If you utilize this repository, please consider citing it with:
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```
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@misc{embedchain,
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author = {Taranjeet Singh},
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title = {Embedchain: Framework to easily create LLM powered bots over any dataset},
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year = {2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/embedchain/embedchain}},
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}
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```
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