Description

Learn how to integrate Elixir and Phoenix Liveview with LLMs by building an application that can provide answers to user queries based on a corpus of custom-trained data.

Goals

Develop an Elixir application via the Phoenix framework that:

  • Employs Retrieval Augmented Generation (RAG) techniques
  • Supports the integration and utilization of various Large Language Models (LLMs).
  • Is designed with extensibility and adaptability in mind to accommodate future enhancements and modifications.

Resources

  • https://elixir-lang.org/
  • https://www.phoenixframework.org/
  • https://github.com/elixir-nx/bumblebee
  • https://ollama.com/

Looking for hackers with the skills:

elixir-lang ollama ai

This project is part of:

Hack Week 24

Activity

  • 27 days ago: mpiala liked this project.
  • about 1 month ago: socon liked this project.
  • about 1 month ago: ninopaparo added keyword "ai" to this project.
  • about 1 month ago: ncuralli started this project.
  • about 1 month ago: ninopaparo added keyword "elixir-lang" to this project.
  • about 1 month ago: ninopaparo added keyword "ollama" to this project.
  • about 1 month ago: ninopaparo originated this project.

  • Comments

    Be the first to comment!

    Similar Projects

    Multi-pod, autoscalable Elixir application in Kubernetes using K8s resources by socon

    Description

    Elixir / Erlang use their own solutions to create clusters that work together. Kubernetes provide its own orchestration. Due to the nature of the BEAM, it looks a very promising technology for applications that run in Kubernetes and requite to be always on, specifically if they are created as web pages using Phoenix.

    Goals

    • Investigate and provide solutions that work in Phoenix LiveView using Kubernetes resources, so a multi-pod application can be used
    • Provide an end to end example that creates and deploy a container from source code.

    Resources

    https://github.com/dwyl/phoenix-liveview-counter-tutorial https://github.com/propedeutica/elixir-k8s-counter


    Run local LLMs with Ollama and explore possible integrations with Uyuni by PSuarezHernandez

    Description

    Using Ollama you can easily run different LLM models in your local computer. This project is about exploring Ollama, testing different LLMs and try to fine tune them. Also, explore potential ways of integration with Uyuni.

    Goals

    • Explore Ollama
    • Test different models
    • Fine tuning
    • Explore possible integration in Uyuni

    Resources

    • https://ollama.com/
    • https://huggingface.co/
    • https://apeatling.com/articles/part-2-building-your-training-data-for-fine-tuning/


    Use local/private LLM for semantic knowledge search by digitaltomm

    Description

    Use a local LLM, based on SUSE AI (ollama, openwebui) to power geeko search (public instance: https://geeko.port0.org/).

    Goals

    Build a SUSE internal instance of https://geeko.port0.org/ that can operate on internal resources, crawling confluence.suse.com, gitlab.suse.de, etc.

    Resources

    Repo: https://github.com/digitaltom/semantic-knowledge-search

    Public instance: https://geeko.port0.org/

    Results

    Internal instance:

    I have an internal test instance running which has indexed a couple of internal wiki pages from the SCC team. It's using the ollama (llama3.1:8b) backend of suse-ai.openplatform.suse.com to create embedding vectors for indexed resources and to create a chat response. The semantic search for documents is done with a vector search inside of sqlite, using sqlite-vec.

    image


    Automated Test Report reviewer by oscar-barrios

    Description

    In SUMA/Uyuni team we spend a lot of time reviewing test reports, analyzing each of the test cases failing, checking if the test is a flaky test, checking logs, etc.

    Goals

    Speed up the review by automating some parts through AI, in a way that we can consume some summary of that report that could be meaningful for the reviewer.

    Resources

    No idea about the resources yet, but we will make use of:

    • HTML/JSON Report (text + screenshots)
    • The Test Suite Status GithHub board (via API)
    • The environment tested (via SSH)
    • The test framework code (via files)


    ghostwrAIter - a local AI assisted tool for helping with support cases by paolodepa

    Description

    This project is meant to fight the loneliness of the support team members, providing them an AI assistant (hopefully) capable of scraping supportconfigs in a RAG fashion, trying to answer specific questions.

    Goals

    • Setup an Ollama backend, spinning one (or more??) code-focused LLMs selected by license, performance and quality of the results between:
    • Setup a Web UI for it, choosing an easily extensible and customizable option between:
    • Extend the solution in order to be able to:
      • Add ZIU/Concord shared folders to its RAG context
      • Add BZ cases, splitted in comments to its RAG context
        • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query BZ
      • Add specific packages picking them from IBS repos
        • A plus would be to login using the IDP portal to ghostwrAIter itself and use the same credentials to query IBS
        • A plus would be to desume the packages of interest and the right channel and version to be picked from the added BZ cases


    COOTWbot by ngetahun

    Project Description

    At SCC, we have a rotating task of COOTW (Commanding Office of the Week). This task involves responding to customer requests from jira and slack help channels, monitoring production systems and doing small chores. Usually, we have documentation to help the COOTW answer questions and quickly find fixes. Most of these are distributed across github, trello and SUSE Support documentation. The aim of this project is to explore the magic of LLMs and create a conversational bot.

    Goal for this Hackweek

    • Build data ingestion Data source:
      • SUSE KB docs
      • scc github docs
      • scc trello knowledge board
    • Test out new RAG architecture

    • https://gitlab.suse.de/ngetahun/cootwbot


    Make more sense of openQA test results using AI by livdywan

    Description

    AI has the potential to help with something many of us spend a lot of time doing which is making sense of openQA logs when a job fails.

    User Story

    Allison Average has a puzzled look on their face while staring at log files that seem to make little sense. Is this a known issue, something completely new or maybe related to infrastructure changes?

    Goals

    • Leverage a chat interface to help Allison
    • Create a model from scratch based on data from openQA
    • Proof of concept for automated analysis of openQA test results

    Bonus

    • Use AI to suggest solutions to merge conflicts
      • This would need a merge conflict editor that can suggest solving the conflict
    • Use image recognition for needles

    Resources

    Timeline

    Day 1

    • Conversing with open-webui to teach me how to create a model based on openQA test results

    Day 2

    Highlights

    • I briefly tested compared models to see if they would make me more productive. Between llama, gemma and mistral there was no amazing difference in the results for my case.
    • Convincing the chat interface to produce code specific to my use case required very explicit instructions.
    • Asking for advice on how to use open-webui itself better was frustratingly unfruitful both in trivial and more advanced regards.
    • Documentation on source materials used by LLM's and tools for this purpose seems virtually non-existent - specifically if a logo can be generated based on particular licenses

    Outcomes

    • Chat interface-supported development is providing good starting points and open-webui being open source is more flexible than Gemini. Although currently some fancy features such as grounding and generated podcasts are missing.
    • Allison still has to be very experienced with openQA to use a chat interface for test review. Publicly available system prompts would make that easier, though.


    Save pytorch models in OCI registries by jguilhermevanz

    Description

    A prerequisite for running applications in a cloud environment is the presence of a container registry. Another common scenario is users performing machine learning workloads in such environments. However, these types of workloads require dedicated infrastructure to run properly. We can leverage these two facts to help users save resources by storing their machine learning models in OCI registries, similar to how we handle some WebAssembly modules. This approach will save users the resources typically required for a machine learning model repository for the applications they need to run.

    Goals

    Allow PyTorch users to save and load machine learning models in OCI registries.

    Resources