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:
This project is part of:
Hack Week 24
Activity
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.
AI for product management by a_jaeger
Description
Learn about AI and how it can help myself
What are the jobs that a PM does where AI can help - and how?
Goals
- Investigate how AI can help with different tasks
- Check out different AI tools, which one is best for which job
- Summarize learning
Resources
- Reading some blog posts by PMs that looked into it
- Popular and less popular AI tools
Work is done SUSE internally at https://confluence.suse.com/display/~a_jaeger/Hackweek+25+-+AI+for+a+PM and subpages.
Research how LLMs could help to Linux developers and/or users by anicka
Description
Large language models like ChatGPT have demonstrated remarkable capabilities across a variety of applications. However, their potential for enhancing the Linux development and user ecosystem remains largely unexplored. This project seeks to bridge that gap by researching practical applications of LLMs to improve workflows in areas such as backporting, packaging, log analysis, system migration, and more. By identifying patterns that LLMs can leverage, we aim to uncover new efficiencies and automation strategies that can benefit developers, maintainers, and end users alike.
Goals
- Evaluate Existing LLM Capabilities: Research and document the current state of LLM usage in open-source and Linux development projects, noting successes and limitations.
- Prototype Tools and Scripts: Develop proof-of-concept scripts or tools that leverage LLMs to perform specific tasks like automated log analysis, assisting with backporting patches, or generating packaging metadata.
- Assess Performance and Reliability: Test the tools' effectiveness on real-world Linux data and analyze their accuracy, speed, and reliability.
- Identify Best Use Cases: Pinpoint which tasks are most suitable for LLM support, distinguishing between high-impact and impractical applications.
- Document Findings and Recommendations: Summarize results with clear documentation and suggest next steps for potential integration or further development.
Resources
- Local LLM Implementations: Access to locally hosted LLMs such as LLaMA, GPT-J, or similar open-source models that can be run and fine-tuned on local hardware.
- Computing Resources: Workstations or servers capable of running LLMs locally, equipped with sufficient GPU power for training and inference.
- Sample Data: Logs, source code, patches, and packaging data from openSUSE or SUSE repositories for model training and testing.
- Public LLMs for Benchmarking: Access to APIs from platforms like OpenAI or Hugging Face for comparative testing and performance assessment.
- Existing NLP Tools: Libraries such as spaCy, Hugging Face Transformers, and PyTorch for building and interacting with local LLMs.
- Technical Documentation: Tutorials and resources focused on setting up and optimizing local LLMs for tasks relevant to Linux development.
- Collaboration: Engagement with community experts and teams experienced in AI and Linux for feedback and joint exploration.
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/
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
Gen-AI chatbots and test-automation of generated responses by mdati
Description
Start experimenting the generative SUSE-AI chat bot, asking questions on different areas of knowledge or science and possibly analyze the quality of the LLM model response, specific and comparative, checking the answers provided by different LLM models to a same query, using proper quality metrics or tools or methodologies.
Try to define basic guidelines and requirements for quality test automation of AI-generated responses.
First approach of investigation can be based on manual testing: methodologies, findings and data can be useful then to organize valid automated testing.
Goals
- Identify criteria and measuring scales for assessment of a text content.
- Define quality of an answer/text based on defined criteria .
- Identify some knowledge sectors and a proper list of problems/questions per sector.
- Manually run query session and apply evaluation criteria to answers.
- Draft requirements for test automation of AI answers.
Resources
- Announcement of SUSE-AI for Hack Week in Slack
- Openplatform and related 3 LLM models gemma:2b, llama3.1:8b, qwen2.5-coder:3b.
Notes
Foundation models (FMs):
are large deep learning neural networks, trained on massive datasets, that have changed the way data scientists approach machine learning (ML). Rather than develop artificial intelligence (AI) from scratch, data scientists use a foundation model as a starting point to develop ML models that power new applications more quickly and cost-effectively.Large language models (LLMs):
are a category of foundation models pre-trained on immense amounts of data acquiring abilities by learning statistical relationships from vast amounts of text during a self- and semi-supervised training process, making them capable of understanding and generating natural language and other types of content , to perform a wide range of tasks.
LLMs can be used for generative AI (artificial intelligence) to produce content based on input prompts in human language.
Validation of a AI-generated answer is not an easy task to perform, as manually as automated.
An LLM answer text shall contain a given level of informations: correcness, completeness, reasoning description etc.
We shall rely in properly applicable and measurable criteria of validation to get an assessment in a limited amount of time and resources.