Everything-AI

Sep 2, 2020 | Educational

Your fully proficient, AI-powered and local chatbot assistant

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Flowchart

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 following variables in your .env file:

    • VOLUME: mounts your local file system into the Docker container.
    • MODELS_PATH: specifies where llama.cpp can find the GGUF models you downloaded.
    • MODEL: indicates which model to use (gguf file name with extension).
    • MAX_TOKENS: tells llama.cpp the maximum number of tokens it can generate as output.

    Example of a .env file:

    VOLUME=c:/Users/User:User
    MODELS_PATH=c:/Users/User/.cache/llama.cpp
    MODEL=stories260K.gguf
    MAX_TOKENS=512
  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 a task choice interface like the following:

    Task choice interface

    Choose among various tasks such as:

    • retrieval-text-generation: Use Qdrant backend for building a retrieval-friendly knowledge base.
    • agnostic-text-generation: ChatGPT-like text generation.
    • text-summarization: Summarize text and PDFs.
    • image-generation: Generate images using stable diffusion.
    • audio-classification: Classify audio files.
    • video-generation: Generate video based on text prompts.

    And many more tasks tailored to your needs.

  6. Start using your assistant

    Once everything is ready, navigate to localhost:7860 to start your assistant:

    Chat interface

Understanding the Components

Imagine your AI chatbot as a highly organized library. The process begins when you clone the repository, which is like constructing the library building itself. Next, you set up your .env file, akin to categorizing the books—defining where each genre (or model) is located and how many books (tokens) can be borrowed at a time.

Pulling the necessary images is like stocking the shelves with books. Finally, running the multi-container app brings the library to life, allowing users (you!) to access the knowledge (AI responses) stored within, simply by navigating to the designated URLs (like finding the right aisle in the library).

Troubleshooting Ideas

If you encounter issues during the setup, consider the following steps:

  • Ensure Docker is properly installed and running.
  • Double-check the paths specified in your .env file for accuracy.
  • Confirm that all necessary images have pulled without errors.
  • If your assistant doesn’t respond, make sure the services are still running; restart if necessary.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

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