embedchain 0.1.6

Data platform for LLMs - Load, index, retrieve and sync any unstructured data

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What is Embedchain?

Embedchain is a Data Platform for Large Language Models (LLMs). Seamlessly load, index, retrieve, and sync unstructured data to build dynamic, LLM-powered applications. Check out embedchain-js for a JavaScript implementation.

🔧 Quick install

Python API

pip install --upgrade embedchain

REST API

You can also run Embedchain as a REST API server using the following command:

docker run --name embedchain -p 8080:8080 embedchain/rest-api:latest

Then, navigate to http://0.0.0.0:8080/docs to interact with the API.

🔍 Usage and Demo

For example, you can create an Elon Musk bot using the following code:

import os
from embedchain import Pipeline as 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://www.forbes.com/profile/elon-musk")
elon_bot.add("https://www.youtube.com/watch?v=RcYjXbSJBN8")

# Query the bot
elon_bot.query("How many companies does Elon Musk run and name those?")
# Answer: Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.

# (Optional): Deploy app to Embedchain Platform
app.deploy()
# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
# ec-xxxxxx

# 🛠️ Creating pipeline on the platform...
# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx

# 🛠️ Adding data to your pipeline...
# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.

You can also try it in your browser with Google Colab:

📖 Documentation

Comprehensive guides and API documentation are available to help you get the most out of Embedchain:

• Getting Started
• Introduction
• Examples
• Supported data types

🔗 Join the Community

Connect with fellow developers and users by joining our Slack Workspace. Dive into discussions, ask questions, and share your experiences.

🤝 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.

🌐 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 reference, please go through Development Guide and Documentation Guide.

Telemetry

We collect anonymous usage metrics to enhance our package's quality and user experience. This includes data like feature usage frequency and system info, but never personal details. The data helps us prioritize improvements and ensure compatibility. If you wish to opt-out, set the app.config.collect_metrics = False in the code. We prioritize data security and don't share this data externally.

Citation

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

@misc{embedchain,
  author = {Taranjeet Singh, Deshraj Yadav},
  title = {Embedchain: Data platform for LLMs - load, index, retrieve, and sync any unstructured data},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/embedchain/embedchain}},
}