https://github.com/aibunny/cloudwhiz
CloudWhiz is a chatbot built with Streamlit, Langchain, Hugging Face's LLM, and Faiss. It serves as a helpful Amazon Web Service assistant, capable of answering questions regarding AWS solutions based on an AWS Solutions Architect PDF.
https://github.com/aibunny/cloudwhiz
faiss langchain llms streamlit
Last synced: 2 months ago
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CloudWhiz is a chatbot built with Streamlit, Langchain, Hugging Face's LLM, and Faiss. It serves as a helpful Amazon Web Service assistant, capable of answering questions regarding AWS solutions based on an AWS Solutions Architect PDF.
- Host: GitHub
- URL: https://github.com/aibunny/cloudwhiz
- Owner: aibunny
- Created: 2023-11-30T06:35:25.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-12-14T13:31:43.000Z (over 2 years ago)
- Last Synced: 2024-05-21T03:08:43.126Z (about 2 years ago)
- Topics: faiss, langchain, llms, streamlit
- Language: Python
- Homepage: https://cloudwhiz.streamlit.app/
- Size: 28.1 MB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# CloudWhiz: [AWS Assistant Chatbot](https://cloudwhiz.streamlit.app/)
CloudWhiz is a chatbot built with Streamlit, Langchain, Hugging Face's LLM, and Faiss. It serves as a helpful Amazon Web Service assistant, capable of answering questions regarding AWS solutions based on an AWS Solutions Architect PDF.
## Getting Started
### Prerequisites
Before you begin, make sure you have the following installed:
- Python (>=3.10)
- [Virtualenv](https://virtualenv.pypa.io/en/latest/)
- [Git](https://git-scm.com/)
### Installation
1. Clone the repository:
```bash
git clone git@github.com:aibunny/cloudwhiz.git
```
2. Navigate to the project directory:
```bash
cd cloudwhiz
```
3. Create a virtual environment:
```bash
virtualenv venv
```
4. Activate the virtual environment:
- On Windows:
```bash
.\venv\Scripts\activate
```
- On Unix or MacOS:
```bash
source venv/bin/activate
```
5. Create a `.env` file in the project root and add your Hugging Face API token:
```env
HUGGINGFACEHUB_API_TOKEN=hf_your_token_here
```
6. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Running the Application
After setting up the environment, you can run the CloudWhiz using Streamlit. Execute the following command:
```bash
streamlit run helper.py
```