https://github.com/mem0ai/qna-bot-template-js
https://github.com/mem0ai/qna-bot-template-js
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/mem0ai/qna-bot-template-js
- Owner: mem0ai
- Created: 2023-07-04T07:09:46.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-07T05:58:57.000Z (about 2 years ago)
- Last Synced: 2025-03-30T16:46:47.816Z (6 months ago)
- Language: JavaScript
- Size: 159 KB
- Stars: 7
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# qna-bot-template-js
[](https://discord.gg/6PzXDgEjG5)
[](https://twitter.com/embedchain)
[](https://embedchain.substack.com/)
qna-bot-template-js is a frontend app created in Next.js powered by [embedchainjs](https://github.com/embedchain/embedchainjs). 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](https://github.com/embedchain/embedchain)
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.
# Getting Started
## Installation
- First make sure that you have the package cloned locally and the packages installed, using the following commands
```bash
git clone https://github.com/embedchain/qna-bot-template-js.git
cd qna-bot-template-js
npm install
```
- 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 don't have an API key, you can create one by visiting [this link](https://platform.openai.com/account/api-keys).
- Rename the `sample.env.local` to `.env.local` and set your environment variables. Here NEXT_PUBLIC_BOT_NAME is the name of the bot that will be displayed in your app.
```bash
OPENAI_API_KEY=""
NEXT_PUBLIC_BOT_NAME=""
```
- Download and install Docker on your device by visiting [this link](https://www.docker.com/). You will need this to run Chroma vector database on your machine.
- Run the following commands to setup Chroma container in Docker
```bash
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.
## Usage
- Run the development server, using
```bash
npm run dev
# or
yarn dev
# or
pnpm dev
```
- Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
- By default we have setup a `Naval Ravikant Bot` app.
- Wait for the data to load completely and then ask any query using the input box and then click on Submit.
- Your answer will be displayed in the result box below.
- To customize and create your own bot app, go to `src/pages/api/init.js` and enter your own data sources in the following manner
```js
//Embedding Online Resources
await chat_bot_app.add("web_page", "https://nav.al/feedback");
await chat_bot_app.add("web_page", "https://nav.al/agi");
await chat_bot_app.add(
"pdf_file",
"https://navalmanack.s3.amazonaws.com/Eric-Jorgenson_The-Almanack-of-Naval-Ravikant_Final.pdf"
);
//Embedding Local QNA Pair
await chat_bot_app.add_local("qna_pair", [
"Who is Naval Ravikant?",
"Naval Ravikant is an Indian-American entrepreneur and investor.",
]);
```
- Go to your `.env.local` file and change the bot name to your desired bot name using the `NEXT_PUBLIC_BOT_NAME` env variable.
- Now reload or run your app again to see the changes.
## Format supported
We support the following formats:
### PDF File
To add any pdf file, use the data_type as `pdf_file`. Eg:
```javascript
await app.add("pdf_file", "a_valid_url_where_pdf_file_can_be_accessed");
```
### Web Page
To add any web page, use the data_type as `web_page`. Eg:
```javascript
await app.add("web_page", "a_valid_web_page_url");
```
### QnA Pair
To supply your own QnA pair, use the data_type as `qna_pair` and enter a tuple. Eg:
```javascript
await app.add_local("qna_pair", ["Question", "Answer"]);
```
# How does it work?
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.
# Tech Stack
embedchain is built on the following stack:
- [Langchain](https://github.com/hwchase17/langchain) as an LLM framework to load, chunk and index data
- [OpenAI's Ada embedding model](https://platform.openai.com/docs/guides/embeddings) to create embeddings
- [OpenAI's ChatGPT API](https://platform.openai.com/docs/guides/gpt/chat-completions-api) as LLM to get answers given the context
- [Chroma](https://github.com/chroma-core/chroma) as the vector database to store embeddings
# Team
## Author
- Taranjeet Singh ([@taranjeetio](https://twitter.com/taranjeetio))
## Maintainer
- [sahilyadav902](https://github.com/sahilyadav902)