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

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

  • Comments

    Be the first to comment!

    Similar Projects

    Try out Neovim Plugins supporting AI Providers by enavarro_suse

    Description

    Experiment with several Neovim plugins that integrate AI model providers such as Gemini and Ollama.

    Goals

    Evaluate how these plugins enhance the development workflow, how they differ in capabilities, and how smoothly they integrate into Neovim for day-to-day coding tasks.

    Resources


    Extended private brain - RAG my own scripts and data into offline LLM AI by tjyrinki_suse

    Description

    For purely studying purposes, I'd like to find out if I could teach an LLM some of my own accumulated knowledge, to use it as a sort of extended brain.

    I might use qwen3-coder or something similar as a starting point.

    Everything would be done 100% offline without network available to the container, since I prefer to see when network is needed, and make it so it's never needed (other than initial downloads).

    Goals

    1. Learn something about RAG, LLM, AI.
    2. Find out if everything works offline as intended.
    3. As an end result have a new way to access my own existing know-how, but so that I can query the wisdom in them.
    4. Be flexible to pivot in any direction, as long as there are new things learned.

    Resources

    To be found on the fly.

    Timeline

    Day 1 (of 4)

    • Tried out a RAG demo, expanded on feeding it my own data
    • Experimented with qwen3-coder to add a persistent chat functionality, and keeping vectors in a pickle file
    • Optimizations to keep everything within context window
    • Learn and add a bit of PyTest

    Day 2

    • More experimenting and more data
    • Study ChromaDB
    • Add a Web UI that works from another computer even though the container sees network is down

    Day 3

    • The above RAG is working well enough for demonstration purposes.
    • Pivot to trying out OpenCode, configuring local Ollama qwen3-coder there, to analyze the RAG demo.
    • Figured out how to configure Ollama template to be usable under OpenCode. OpenCode locally is super slow to just running qwen3-coder alone.

    Day 4 (final day)

    • Battle with OpenCode that was both slow and kept on piling up broken things.
    • Call it success as after all the agentic AI was working locally.
    • Clean up the mess left behind a bit.

    Blog Post

    Summarized the findings at blog post.


    Uyuni Health-check Grafana AI Troubleshooter by ygutierrez

    Description

    This project explores the feasibility of using the open-source Grafana LLM plugin to enhance the Uyuni Health-check tool with LLM capabilities. The idea is to integrate a chat-based "AI Troubleshooter" directly into existing dashboards, allowing users to ask natural-language questions about errors, anomalies, or performance issues.

    Goals

    • Investigate if and how the grafana-llm-app plug-in can be used within the Uyuni Health-check tool.
    • Investigate if this plug-in can be used to query LLMs for troubleshooting scenarios.
    • Evaluate support for local LLMs and external APIs through the plugin.
    • Evaluate if and how the Uyuni MCP server could be integrated as another source of information.

    Resources

    Grafana LMM plug-in

    Uyuni Health-check


    Song Search with CLAP by gcolangiuli

    Description

    Contrastive Language-Audio Pretraining (CLAP) is an open-source library that enables the training of a neural network on both Audio and Text descriptions, making it possible to search for Audio using a Text input. Several pre-trained models for song search are already available on huggingface

    SUSE Hackweek AI Song Search

    Goals

    Evaluate how CLAP can be used for song searching and determine which types of queries yield the best results by developing a Minimum Viable Product (MVP) in Python. Based on the results of this MVP, future steps could include:

    • Music Tagging;
    • Free text search;
    • Integration with an LLM (for example, with MCP or the OpenAI API) for music suggestions based on your own library.

    The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.

    Result

    In this MVP we implemented:

    • Async Song Analysis with Clap model
    • Free Text Search of the songs
    • Similar song search based on vector representation
    • Containerised version with web interface

    We also documented what went well and what can be improved in the use of AI.

    You can have a look at the result here:

    Future implementation can be related to performance improvement and stability of the analysis.

    References


    Is SUSE Trending? Popularity and Developer Sentiment Insight Using Native AI Capabilities by terezacerna

    Description

    This project aims to explore the popularity and developer sentiment around SUSE and its technologies compared to Red Hat and their technologies. Using publicly available data sources, I will analyze search trends, developer preferences, repository activity, and media presence. The final outcome will be an interactive Power BI dashboard that provides insights into how SUSE is perceived and discussed across the web and among developers.

    Goals

    1. Assess the popularity of SUSE products and brand compared to Red Hat using Google Trends.
    2. Analyze developer satisfaction and usage trends from the Stack Overflow Developer Survey.
    3. Use the GitHub API to compare SUSE and Red Hat repositories in terms of stars, forks, contributors, and issue activity.
    4. Perform sentiment analysis on GitHub issue comments to measure community tone and engagement using built-in Copilot capabilities.
    5. Perform sentiment analysis on Reddit comments related to SUSE technologies using built-in Copilot capabilities.
    6. Use Gnews.io to track and compare the volume of news articles mentioning SUSE and Red Hat technologies.
    7. Test the integration of Copilot (AI) within Power BI for enhanced data analysis and visualization.
    8. Deliver a comprehensive Power BI report summarizing findings and insights.
    9. Test the full potential of Power BI, including its AI features and native language Q&A.

    Resources

    1. Google Trends: Web scraping for search popularity data
    2. Stack Overflow Developer Survey: For technology popularity and satisfaction comparison
    3. GitHub API: For repository data (stars, forks, contributors, issues, comments).
    4. Gnews.io API: For article volume and mentions analysis.
    5. Reddit: SUSE related topics with comments.


    Try out Neovim Plugins supporting AI Providers by enavarro_suse

    Description

    Experiment with several Neovim plugins that integrate AI model providers such as Gemini and Ollama.

    Goals

    Evaluate how these plugins enhance the development workflow, how they differ in capabilities, and how smoothly they integrate into Neovim for day-to-day coding tasks.

    Resources