everything-ai

Your fully proficient, AI-powered and local chatbot assistant🤖

Flowchart for everything-ai

Quickstart

1. Clone this repository

git clone https://github.com/AstraBert/everything-ai.git
cd everything-ai

2. Set your .env file

Modify the VOLUME variable in the .env file so that you can mount your local file system into Docker container.

An example could be:

VOLUME="c:/Users/User/:/User/"

This means that now everything that is under "c:/Users/User/" on your local machine is under "/User/" in your Docker container.

3. Pull the necessary images

docker pull astrabert/everything-ai
docker pull qdrant/qdrant

4. Run the multi-container app

docker compose up

5. Go to localhost:8670 and choose your assistant

You will see something like this:

Choose the task among:

• retrieval-text-generation: use qdrant backend to build a retrieval-friendly knowledge base, which you can query and tune the response of your model on. You have to pass either a pdf/a bunch of pdfs specified as comma-separated paths or a directory where all the pdfs of interest are stored (DO NOT provide both); you can also specify the language in which the PDF is written, using ISO nomenclature - MULTILINGUAL
• agnostic-text-generation: ChatGPT-like text generation (no retrieval architecture), but supports every text-generation model on HF Hub (as long as your hardware supports it!) - MULTILINGUAL
• text-summarization: summarize text and pdfs, supports every text-summarization model on HF Hub - ENGLISH ONLY
• image-generation: stable diffusion, supports every text-to-image model on HF Hub - MULTILINGUAL
• image-generation-pollinations: stable diffusion, use Pollinations AI API; if you choose 'image-generation-pollinations', you do not need to specify anything else apart from the task - MULTILINGUAL
• image-classification: classify an image, supports every image-classification model on HF Hub - ENGLISH ONLY
• image-to-text: describe an image, supports every image-to-text model on HF Hub - ENGLISH ONLY
• audio-classification: classify audio files or microphone recordings, supports audio-classification models on HF hub
• speech-recognition: transcribe audio files or microphone recordings, supports automatic-speech-recognition models on HF hub.
• video-generation: generate video upon text prompt, supports text-to-video models on HF hub - ENGLISH ONLY
• protein-folding: get the 3D structure of a protein from its amino-acid sequence, using ESM-2 backbone model - GPU ONLY
• autotrain: fine-tune a model on a specific downstream task with autotrain-advanced, just by specifying you HF username, HF writing token and the path to a yaml config file for the training
• spaces-api-supabase: use HF Spaces API in combination with Supabase PostgreSQL databases in order to unleash more powerful LLMs and larger RAG-oriented vector databases - MULTILINGUAL
• image-retrieval-search: search an image database uploading a folder as database input. The folder should have the following structure:

./
├── test/
|   ├── label1/
|   └── label2/
└── train/
    ├── label1/
    └── label2/

You can query the database starting from your own pictures.

6. Go to localhost:7860 and start using your assistant

Once everything is ready, you can head over to localhost:7860 and start using your assistant:

Complete documentation is coming soon...🚀