Embedchain is an Open Source Framework for personalizing LLM responses. It makes it easy to create and deploy personalized AI apps. At its core, Embedchain follows the design principle of being "Conventional but Configurable" to serve both software engineers and machine learning engineers.

Embedchain streamlines the creation of personalized LLM applications, offering a seamless process for managing various types of unstructured data. It efficiently segments data into manageable chunks, generates relevant embeddings, and stores them in a vector database for optimized retrieval. With a suite of diverse APIs, it enables users to extract contextual information, find precise answers, or engage in interactive chat conversations, all tailored to their own data.

pip install embedchain

Checkout the Chat with PDF live demo we created using Embedchain. You can find the source code here.

For example, you can 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>"
app = App()

# Embed online resources
app.add("https://en.wikipedia.org/wiki/Elon_Musk")
app.add("https://www.forbes.com/profile/elon-musk")

# Query the app
app.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.

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

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

• Introduction
• Getting Started
• Examples
• Supported data types

• Connect with fellow developers by joining our Slack Community or Discord Community.

• Dive into GitHub Discussions, ask questions, or share your experiences.

Book a 1-on-1 Session with the founders, to discuss any issues, provide feedback, or explore how we can improve Embedchain for you.

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.

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 environment variable EC_TELEMETRY=false. We prioritize data security and don't share this data externally.

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

@misc{embedchain,
  author = {Taranjeet Singh, Deshraj Yadav},
  title = {Embedchain: The Open Source RAG Framework},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/embedchain/embedchain}},
}