mem0

embedchain

embedchain is a framework to easily create bots over any dataset.

You can add a single or multiple dataset using .add function and then use .query function to find an answer from the added datasets.

• If you want to create a Naval Ravikant bot which has 1 youtube video, 1 book as pdf and 2 of his blog posts, all you need to do is add the links to the videos, pdf and blog posts and embedchain will create a bot for you.

from embedchain import App

app = app()

app.add("youtube_video", "https://www.youtube.com/watch?v=3qHkcs3kG44")
app.add("pdf_file", "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
app.add("web_page", "https://nav.al/feedback")
app.add("web_page", "https://nav.al/agi")

app.query("How to do a startup?")

Getting Started

Installation

First make sure that you have the package installed. If not, then install it using pip

pip install embedchain

Usage

• We use OpenAI's embedding model to create embeddings for chunks and ChatGPT API as LLM to get answer given the relevant docs. Make sure that you have an OpenAI account and an API key.

• Once you have the API key, set it in an environment variable called OPENAI_API_KEY

export OPENAI_API_KEY='sk-xxxxxxxx'

• Next import the App class from embedchain and use .add function to add any dataset.

from embedchain import App

naval_ravikant_chat_bot_app = App()

naval_ravikant_chat_bot_app.add("youtube_video", "https://www.youtube.com/watch?v=3qHkcs3kG44")
naval_ravikant_chat_bot_app.add("pdf_file", "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf")
naval_ravikant_chat_bot_app.add("web_page", "https://nav.al/agi")

• Now you app is created. You can use .query function to get the answer for any query.

print(app.query("How to do a startup?"))

Format supported

We support the following formats:

• Youtube Video

To add any youtube video to your app, use the data_type (first argument to .add) as youtube_video. Eg:

app.add('youtube_video', 'a_valid_youtube_url_here')

• PDF File

To add any pdf file, use the data_type as pdf_file. Eg:

app.add('pdf_file', 'a_valid_url_where_pdf_file_can_be_accessed')

Note that we do not support password protected pdfs as of now.

• Web Page

To add any web page, use the data_type as web_page. Eg:

app.add('web_page', 'a_valid_web_page_url')

How does it work?

Creating a chat bot over any dataset needs the following steps to happen

• load the data
• create meaningful chunks
• create embeddigns for each chunk
• store the chunks in vector database

Whenever a user asks any query, following process happens to find the answer for the query

• create the embedding for query
• find similar documents for this query from vector database
• pass similar documents as context to LLM to get the final answer.

The process of loading the dataset and then querying involves multiple steps and each steps has nuances of it is own.

• How should I chunk the data? What is a meaningful chunk size?
• How should I create embeddings for each chunk? Which embedding model should I use?
• How should I store the chunks in vector database? Which vector database should I use?
• Should I store meta data along with the embeddings?
• How should I find similar documents for a query? Which ranking model should I use?

These questions may be trivial for some but for a lot of us, it needs research, experimentation and time to find out the accurate answers.

embedchain is a framework which takes care of all these nuances and provides a simple interface to create bots over any dataset.

In the first release, we are making it easier for anyone to get a chatbot over any dataset up and running in less than a minute. All you need to do is create an app instance, add the data sets using .add function and then use .query function to get the relevant answer.

Tech Stack

embedchain is built on the following stack:

• langchain as an LLM framework to load, chunk and index data,
• OpenAI's Ada embedding model to create embeddings
• OpenAI's ChatGPT API as LLM to get answers given the context
• Chroma as the vector database to store embeddings

Author

• Taranjeet Singh (@taranjeetio)