everything-ai

<h1 align="center">everything-ai</h1> <h2 align="center">Your fully proficient, AI-powered and local chatbot assistant🤖</h2> <div align="center"> <img src="https://img.shields.io/github/languages/top/AstraBert/everything-ai" alt="GitHub top language"> <img src="https://img.shields.io/github/commit-activity/t/AstraBert/everything-ai" alt="GitHub commit activity"> <img src="https://img.shields.io/badge/everything_ai-stable-green" alt="Static Badge"> <img src="https://img.shields.io/badge/Release-v4.2.0-purple" alt="Static Badge"> <img src="https://img.shields.io/docker/image-size/astrabert/everything-ai " alt="Docker image size"> <img src="https://img.shields.io/badge/Supported_platforms-Windows/macOS-brown" alt="Static Badge"> <div> <a href="https://huggingface.co/spaces/as-cle-bert/everything-rag"><img src="./imgs/everything-ai.drawio.png" alt="Flowchart" align="center"></a> <p><i>Flowchart for everything-ai</i></p> </div> </div>

Quickstart

1. Clone this repository

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

2. Set your .env file

Modify:

• VOLUME variable in the .env file so that you can mount your local file system into Docker container.
• MODELS_PATH variable in the .env file so that you can tell llama.cpp where you stored the GGUF models you downloaded.
• MODEL variable in the .env file so that you can tell llama.cpp what model to use (use the actual name of the gguf file, and do not forget the .gguf extension!)
• MAX_TOKENS variable in the .env file so that you can tell llama.cpp how many new tokens it can generate as output.

An example of a .env file could be:

VOLUME="c:/Users/User/:/User/"
MODELS_PATH="c:/Users/User/.cache/llama.cpp/"
MODEL="stories260K.gguf"
MAX_TOKENS="512"

This means that now everything that is under "c:/Users/User/" on your local machine is under "/User/" in your Docker container, that llama.cpp knows where to look for models and what model to look for, along with the maximum new tokens for its output.

3. Pull the necessary images

docker pull astrabert/everything-ai:latest
docker pull qdrant/qdrant:latest
docker pull ghcr.io/ggerganov/llama.cpp:server

4. Run the multi-container app

docker compose up

5. Go to localhost:8670 and choose your assistant

You will see something like this:

<div align="center"> <img src="./imgs/select_and_run.png" alt="Task choice interface"> </div>

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
• llama.cpp-and-qdrant: same as retrieval-text-generation, but uses llama.cpp as inference engine, so you MUST NOT specify a model - MULTILINGUAL
• build-your-llm: Build a customizable chat LLM combining a Qdrant database with your PDFs and the power of Anthropic, OpenAI, Cohere or Groq models: you just need an API key! To build the Qdrant database, 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, LANGFUSE INTEGRATION
• simply-chatting: Build a customizable chat LLM with the power of Anthropic, OpenAI, Cohere or Groq models (no RAG pipeline): you just need an API key! - MULTILINGUAL, LANGFUSE INTEGRATION
• fal-img2img: Use fal.ai ComfyUI API to generate images starting from yur PNG and JPEG images: you just need an API key! You can aklso customize the generation working with prompts and seeds - ENGLISH ONLY
• 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:

<div align="center"> <img src="./imgs/chatbot.png" alt="Chat interface"> </div>