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https://github.com/lsyurea/ai_chatbot
QueryAI with GPT turbo, a more intuitive interface
https://github.com/lsyurea/ai_chatbot
docker docker-compose docker-image mantine mongodb nextjs openai python3 tailwindcss typescript
Last synced: about 2 months ago
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QueryAI with GPT turbo, a more intuitive interface
- Host: GitHub
- URL: https://github.com/lsyurea/ai_chatbot
- Owner: lsyurea
- License: mit
- Created: 2024-02-16T13:23:49.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-05-12T17:45:17.000Z (8 months ago)
- Last Synced: 2024-05-12T18:50:08.488Z (8 months ago)
- Topics: docker, docker-compose, docker-image, mantine, mongodb, nextjs, openai, python3, tailwindcss, typescript
- Language: TypeScript
- Homepage:
- Size: 1.12 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AI_ChatBot
## Introduction
This is a simple AI chatbot that uses GPT 3 turbo engine to generate responses.
This project has a front-end folder and a back-end folder. The front-end folder contains the code for the user interface and the back-end folder contains the code for the server and the GPT 3 engine.
## Frontend
I will use NextJS with tailwindcss to build the frontend of the chatbot.
## Installation for the frontend
First, run the development server:
```bash
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
```
Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
## Demo
### Our Chat
![Demo]()
## Our Dashboard
![Demo]()
## Backend
I will use Python >= 3.8, FastAPI, Pydantic Beanie and GPT 3 engine to build the backend of the chatbot.
## Installation for the backend
Download the project and navigate to the backend folder.
Run ```docker compose build```.
Then run ```docker compose up -d``` to start the application in detached mode.
## api routes
The api routes are specified by the openapi schema. You can access the routes via
If docker is not working as expected, go to the debugging section. With the debugging section, you can access the api routes via
Once the localhost is started, you can access the api routes via
This is an example of how the api routes are accessed using postman.
![example]()
![example]()
## Usage
MongoDB is running on port 27017.
## Debugging
Currently the application in docker is working as expected.
If the docker fails as a result of different port names of mongo in dp.py, it is possible to bypass it.
On top of running docker compose up -d, you will need to run the following commands to start the application and access the api.:
```cd app && uvicorn main:app --host 0.0.0.0 --port 8000 --reload```
Note that this means that the api routes will be accessible via
Alternatively, you can run the following command to start the application:
```cd app && python3 run.py```
## Closing the application
Run ```docker compose down``` to stop the application.