Embedchain - a framework for creating LLM bots

<p>Embedchain abstracts the entire process of loading a dataset, splitting it into parts, creating vector representations (embeddings), and storing them in a vector database.</p>
<p>All of this could previously be done using Python code and the OpenAI API, but it required a deep understanding of how embeddings work, calculating distances between vectors, etc. Embedchain simplifies the entire process, reducing it essentially to a code of a couple of dozen lines.</p>
<p>Imagine you want to create a Naval Ravikant bot based on information from 1 YouTube video, 1 PDF book, and 2 of his blog posts, as well as a few pairs of question-answer that you uploaded. All you need to do is add links to the video, PDF, and blog posts, as well as a couple of question-answer pairs, and Embedchain will create the bot for you.</p>
<p>Embedchain supports the following formats: YouTube videos, PDF files, web pages, text, and question-answer pairs.</p>
<p>The tech stack on which all this works:</p>
<ul>
<li>Langchain as the LLM framework for loading, splitting, and indexing data;</li>
<li>OpenAI's Ada embedding model for creating embeddings;</li>
<li>OpenAI's ChatGPT API as the LLM for getting answers, considering context;</li>
<li>Chroma as the vector database for storing embeddings.</li>
</ul>
<p>👨‍💻 Github: <a href="https://github.com/embedchain/embedchain">https://github.com/embedchain/embedchain</a></p>;
<p>#ai #gpt #llm #langchain #bot</p>