embedchain is a framework to easily create LLM powered bots over any dataset. embedchainjs is Javascript version of embedchain. If you want a python version, check out embedchain-python

It abstracts the entire process of loading dataset, chunking it, creating embeddings and then storing in vector database.

You can add a single or multiple dataset using .add and .add_local 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 2 of his blog posts, as well as a question and answer pair you supply, all you need to do is add the links to the blog posts and the QnA pair and embedchain will create a bot for you.

const dotenv = require("dotenv");
dotenv.config();
const { App } = require("embedchain");

//Run the app commands inside an async function only
async function testApp() {
  const naval_chat_bot = await App();

  // Embed Online Resources
  await naval_chat_bot.add("web_page", "https://nav.al/feedback");
  await naval_chat_bot.add("web_page", "https://nav.al/agi");
  await naval_chat_bot.add(
    "pdf_file",
    "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf"
  );

  // Embed Local Resources
  await naval_chat_bot.add_local("qna_pair", [
    "Who is Naval Ravikant?",
    "Naval Ravikant is an Indian-American entrepreneur and investor.",
  ]);

  const result = await naval_chat_bot.query(
    "What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"
  );
  console.log(result);
  // answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
}

testApp();

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

npm install embedchain

• Make sure that dotenv package is installed and your OPENAI_SECRET_KEY in a file called .env in the root folder. You can install dotenv by

npm install dotenv

• Download and install Docker on your device by visiting this link. You will need this to run Chroma vector database on your machine.

• Run the following commands to setup Chroma container in Docker

git clone https://github.com/chroma-core/chroma.git
cd chroma
docker-compose up -d --build

• Once Chroma container has been set up, run it inside Docker

• 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. If you have dont have an API key, you can create one by visiting this link.

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

// Set this inside your .env file
OPENAI_API_KEY = "sk-xxxx";

• Load the environment variables inside your .js file using the following commands

const dotenv = require("dotenv");
dotenv.config();

• Next import the App class from embedchain and use .add function to add any dataset.
• Now your app is created. You can use .query function to get the answer for any query.

const dotenv = require("dotenv");
dotenv.config();
const { App } = require("embedchain");

async function testApp() {
  const naval_chat_bot = await App();

  // Embed Online Resources
  await naval_chat_bot.add("web_page", "https://nav.al/feedback");
  await naval_chat_bot.add("web_page", "https://nav.al/agi");
  await naval_chat_bot.add(
    "pdf_file",
    "https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf"
  );

  // Embed Local Resources
  await naval_chat_bot.add_local("qna_pair", [
    "Who is Naval Ravikant?",
    "Naval Ravikant is an Indian-American entrepreneur and investor.",
  ]);

  const result = await naval_chat_bot.query(
    "What unique capacity does Naval argue humans possess when it comes to understanding explanations or concepts?"
  );
  console.log(result);
  // answer: Naval argues that humans possess the unique capacity to understand explanations or concepts to the maximum extent possible in this physical reality.
}

testApp();

• If there is any other app instance in your script or app, you can change the import as

const { App: EmbedChainApp } = require("embedchain");

// or

const { App: ECApp } = require("embedchain");

We support the following formats:

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

await app.add("pdf_file", "a_valid_url_where_pdf_file_can_be_accessed");

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

await app.add("web_page", "a_valid_web_page_url");

To supply your own QnA pair, use the data_type as qna_pair and enter a tuple. Eg:

await app.add_local("qna_pair", ["Question", "Answer"]);

• If you want to add any other format, please create an issue and we will add it to the list of supported formats.

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

• load the data
• create meaningful chunks
• create embeddings 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.

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

• Taranjeet Singh (@taranjeetio)