chat-bot-template-py

Introduction

Welcome to Embedchain Chat Template tutorial. This repository includes the starter code to quickly get a bot running.

In this tutorial, we will create a Naval Ravikant Bot. This bot will have following context from the following sources.

• Naval Ravikant Joe Rogan Podcast
• The Almanack of Naval Ravikant
• Free Markets Provide the Best Feedback from Naval's blog
• More Compute Power Doesn’t Produce AGI from Naval's blog
• Question / Answer Pair:
  • Q: Who is Naval Ravikant?
  • A: Naval Ravikant is an Indian-American entrepreneur and investor.

Getting Started

Installation

• First make sure that you have the following installed.

• Python 3 and virtualenv

• Make sure that you have the package cloned locally, using the following commands

git clone https://github.com/embedchain/chat-bot-template-py.git
cd chat-bot-template-py

• Create and activate your virtual environment as follows

# For Linux Users
virtualenv -p $(which python3) pyenv
source pyenv/bin/activate

# For Windows users
virtualenv pyenv
.\pyenv\Scripts\activate

• Now install the required packages using

pip install -r requirements.txt

• 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.

• Rename the sample.env to .env and set your environment variables.

OPENAI_API_KEY=""

Usage

• Activate your virtual environment

# For Linux Users
source pyenv/bin/activate

# For Windows Users
.\pyenv\Scripts\activate

• Run the development server, using

python main.py

• Open http://localhost:8000 with your browser to see the result.

• By default we have setup a Naval Ravikant Chat Bot app.

• Wait for the data to load completely and then ask any query using the chat box and then click on Submit.

• Your results will be displayed as chats in the chat window

• To customize and create your own bot app, go to main.py and enter your own data sources in the load_app() function in the following manner

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

# Embed Local Resources
chat_bot_app.add_local("qna_pair", ("Who is Naval Ravikant?", "Naval Ravikant is an Indian-American entrepreneur and investor."))

• To change your bot name, change the global variable in the main.py file as follows

bot_name="Naval Ravikant"

• Now reload or run your app again to see the changes.

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.

Web Page

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

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

Doc File

To add any doc/docx file, use the data_type as doc_file. Eg:

app.add('doc_file', 'a_local_doc_file_path')

Text

To supply your own text, use the data_type as text and enter a string. The text is not processed, this can be very versatile. Eg:

app.add_local('text', 'Seek wealth, not money or status. Wealth is having assets that earn while you sleep. Money is how we transfer time and wealth. Status is your place in the social hierarchy.')

Note: This is not used in the examples because in most cases you will supply a whole paragraph or file, which did not fit.

QnA Pair

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

app.add_local('qna_pair', ("Question", "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