Train ChatGPT on Your Website Data and Build an AI Chatbot that can instantly answer your customers queries.
- Easy Integration
- Data-Specific Responses
- Regular Data Updates
- No code builder
- Customise chatbot
- Fine tuning
- Offline message
Create and train a chatbot for your website in just a few simple steps.
- Just enter your website URL to get started. We'll automatically fetch and prepare training data.
- We’ll automatically train ChatGPT on your website based on the selected parameters and create the chatbot for you.
- To embed the chatbot to your website, simply add the tiny script tag to your website.
WebWhiz allows you to train ChatGPT on your website data and build a chatbot that you can add to your website. No coding required.
Currently we crawl your website once every month. Please contact us if you need your website to be scanned more frequently
WebWhiz collects data from your website pages to train your chatbot. This includes text data from the pages as well as any metadata such as page titles or descriptions. We do not collect any personally identifiable information (PII) or sensitive data from your website. We scan only public data available to search engines
If you exceed your plan's limits for projects or pages, we will notify you. However, if you exceed the token limit for your plan, your chatbots will stop generating AI responses and will instead respond with a predefined message.
Tokens are a unit of measurement used to calculate the amount of text data that is processed by your chatbot. Each token corresponds to a variable number of characters, depending on the complexity of the language used in the message. Each message your chatbot sends uses a certain number of tokens based on the length and complexity of the input and the AI response. You can view the current token usage of your account on the dashboard.
Yes, you can train custom data by simply pasting content to WebWhiz
Not at the moment, but, it will be possible in a couple of days.
WebWhiz have any limitations on the size of context. However, please note that the number of pages you can crawl may be limited based on the plan you choose. Please refer to our plans page to learn more about the specific limitations of each plan.
Webwhiz is open-source under the GNU Affero General Public License Version 3 (AGPLv3)
- Docker - Easy
- Manual Setup - Involved (but provides more flexibility)
Prerequisites
- Docker & docker-compose
Running Webwhiz with docker
- Clone the repo
- Edit the
.env.dockerfile present in the root of the repo and add yourOPENAI_KEY&OPENAI_KEY_2 - Use docker-compose to start the stack
# Bring up webwhiz
# Once the building is done and webwhiz starts the UI will be available at
# http://localhost:3030, backend is available at http://localhost:3000
# To exit Press Ctrl-C
docker-compose up
# Alternatively Run webwhiz as a daemon
docker-compose up -d
# Stop Webwhiz
docker-compose down
# Force rebuild all containers (required only if some change is not picked up)
sudo docker-compose up --build --force-recreateWebWhiz is designed to be used as a production grade Chatbot that can be scaled up or down to handle any volume of data.
WebWhiz consists of mainly 3 components
- The API server - This is the main webwhiz backend web server using NestJS
- JS Celery Worker - Handles crawling, embeddings generation
- Python Celery Worker - Container Cosine similarity calculator & HTML / PDF content extractor
For Database and Caching Webwhiz uses
- MongoDB
- Redis
The backend server uses third part services (including OpenAI) for powering the chatbot, as well as for error monitoring etc. Only OpenAI key is mandatory and you can ignore the others if you prefer to.
NOTE: WebWhiz keeps embeddings in Redis to improve the performance of chatbot responses. For most organisations the chatbots created would be conatins data for a few hundren or thousands of pages, and Redis should work well while providing better performance. If you would like to use a dedicated vector database for searching relavant chunks please reach out to us.
- MongoDB v6
- Redis v7
- Node v18 + Yarn
- Python v3.6+
- Create a copy of the
.env.samplefile and rename as.env
The following variables as mandatory
- HOST - IP to which the web server should bind to typically
0.0.0.0 - PORT - Port on which web server should listen to (Default 3000)
- SECRET_KEY - Secret used for encryption (JWT, etc).
- MONGO_URI - MongoDB uri to use
- MONGO_DBNAME - Name of Database inside MongoDB
- OPENAI_KEY - OpenAI API key
- OPENAI_KEY_2 - Alternate OpenAI API key, used when the primary one raises error. You can use the same API key for both if you don't want to provide two separate API keys
- Inside the workers folder create a copy of the
.env.sampleand rename as.env.
Set the value for the following variables - MONGO_URI, MONGO_DBNAME, REDIS_HOST, REDIS_PORT
From the root folder run the following commands
# Install node dependencies
yarn install
# Install python worker dependencies
cd workers
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# Run application with pm2
cd ..
yarn run build
npm install -g pm2 # Use sudo if required
pm2 start ecosystem.config.jsThis will start the backend http server, the js worker and the python worker
Create .env file in the frontend folder and add the following variables
REACT_APP_BASE_URL='https://api.website.com'
GOOGLE_AUTH_ID='Only if you need google login'From the frontend folder run the following commands to start the server
# Install dependencies
npm install
# Run front end app
npm run startRun npm run build to package the frontend app
If you face any issues, reach out to hi@webwhiz.ai