How to: Running Locally on Windows, MacOS, and Linux:
**1. Clone Project Repo**

```bash
git clone https://github.com/mckaywrigley/chatbot-ui.git
```

**2. Install Dependencies**

```bash
npm i
```

**3. Provide OpenAI API Key**

Create a .env.local file in the root of the repo with your **[OpenAI API Key](https://platform.openai.com/account/api-keys)**:

```bash
OPENAI_API_KEY=YOUR_KEY
```

* **You can set `OPENAI_API_HOST` where access to the official OpenAI host is restricted or unavailable, allowing users to configure an alternative host for their specific needs.**

* **Additionally, if you have multiple OpenAI Organizations, you can set `OPENAI_ORGANIZATION` to specify one.**

**4. Run App**

```bash
npm run dev
```

**You done you should be able to start chatting with ChatGPT!**
Chatbot UI
[MiniGPT-4](https://minigpt-4.github.io/) is an enhancing Vision-language Understanding with Advanced Large Language Models

**Launching Demo Locally**

Try out the demo [demo.py](https://github.com/Vision-CAIR/MiniGPT-4/blob/main/demo.py) on your local machine by running

```python demo.py --cfg-path eval_configs/minigpt4_eval.yaml  --gpu-id 0```

Here, the demo loads Vicuna as 8 bit by default to save some GPU memory usage. Besides, the default beam search width is 1. Under this setting, the **demo cost about 23G GPU memory**. If you have a more powerful GPU with larger GPU memory, you can run the model in 16 bit by setting low_resource to False in the config file [minigpt4_eval.yaml](https://github.com/Vision-CAIR/MiniGPT-4/blob/main/eval_configs/minigpt4_eval.yaml) and use a larger beam search width.
MiniGPT-4 Demo
[GPT4All](https://github.com/nomic-ai/gpt4all) is an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue based on [LLaMa](https://github.com/facebookresearch/llama).
[GPT4All UI](https://github.com/nomic-ai/gpt4all-ui) is a Flask web application that provides a chat UI for interacting with the GPT4All chatbot.
[Alpaca.cpp](https://github.com/antimatter15/alpaca.cpp) is a fast ChatGPT-like model locally on your device. It combines the [LLaMA foundation model](https://github.com/facebookresearch/llama) with an [open reproduction](https://github.com/tloen/alpaca-lora) of [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) a fine-tuning of the base model to obey instructions (akin to the [RLHF](https://huggingface.co/blog/rlhf) used to train ChatGPT) and a set of modifications to [llama.cpp](https://github.com/ggerganov/llama.cpp) to add a chat interface.

[llama.cpp](https://github.com/ggerganov/llama.cpp) is a Port of Facebook's LLaMA model in C/C++.

[Serge](https://github.com/serge-chat/serge) is a web interface for chatting with Alpaca through llama.cpp. Fully self-hosted & dockerized, with an easy to use API. 

[OpenPlayground](https://github.com/nat/openplayground) is a playfround for running ChatGPT-like models locally on your device.

[Vicuna](https://vicuna.lmsys.org/) is an open source chatbot trained by fine tuning LLaMA. It apparently achieves more than 90% quality of chatgpt and costs $300 to train.

[Yeagar ai](https://github.com/yeagerai/yeagerai-agent) is a Langchain Agent creator designed to help you build, prototype, and deploy AI-powered agents with ease.

[LocalAI](https://localai.io/) is a self-hosted, community-driven, local OpenAI-compatible API. Drop-in replacement for OpenAI running LLMs on consumer-grade hardware with no GPU required. It's an API to run ggml compatible models: llama, gpt4all, rwkv, whisper, vicuna, koala, gpt4all-j, cerebras, falcon, dolly, starcoder, and many others.

[DoctorGPT](https://github.com/ingyamilmolinar/doctorgpt) is a lightweight self-contained binary that monitors your application logs for problems and diagnoses them.

[HttpGPT](https://github.com/lucoiso/UEHttpGPT/releases) is an Unreal Engine 5 plugin that facilitates integration with OpenAI's GPT based services (ChatGPT and DALL-E) through asynchronous REST requests, making it easy for developers to communicate with these services. It also includes Editor Tools to integrate Chat GPT and DALL-E image generation directly in the Engine.