Repository navigation
Replies: 2 comments
|
I have built a Keycloak finetune based on qwen2.5-14B-Instructure. It also includes a local index for RAG and a router to combine the RAG, finetune model and commercial LLM in single generations. |
0 replies
keycloak public signals: 2025-12-07 02:34 PM ESTGenerated 2025-12-07 14:34:21 EST (2025-12-07 19:34:21 UTC) :[59b2e7dedfadf22c950e90ce5dcfd8994f84154a]
117,151 chunks (176.9 MB) — 🔑=primary ✅=official 👤=community ❓=uncategorized API Keys Required
Q/A Coverage
|
0 replies
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Uh oh!
There was an error while loading. Please reload this page.
Introduction
Keycloak is an incredibly powerful and versatile identity and access management solution. However, its vast feature set, extensive configuration options, and integration points can make it challenging for both new users and seasoned developers to quickly find specific answers, troubleshoot issues, or understand best practices.
While Keycloak's official documentation is comprehensive, navigating it for highly specific or contextual queries can sometimes be time-consuming. Community forums, Stack Overflow, and GitHub Issues also hold a wealth of knowledge, but this information is often fragmented and requires significant effort to synthesize.
This discussion proposes an initiative to build a meticulously curated and structured knowledge base specifically designed to train or power a Large Language Model (LLM). The ultimate goal is to enable an "LLM agent" capable of providing instant, accurate, and context-aware assistance for Keycloak-related queries.
The Vision: A Keycloak AI Assistant
Imagine an AI assistant that could:
Why an LLM Knowledge Base?
Simply dumping all existing documentation into an LLM isn't enough. For an LLM to be truly effective as an agent, it needs:
This project is about building that specialized, high-quality data set.
Potential Use Cases of the Keycloak AI Agent
Proposed Scope & Data Sources for the Knowledge Base
The knowledge base would be built upon (but not limited to) the following sources, carefully curated and structured:
Content Types to Extract/Curate:
All reactions