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== Retrieval-Augmented Generation (RAG) ==
== Retrieval-Augmented Generation (RAG) ==


= Technology Stack =
== Low-Code / No-Code ==
 
== LangChain ==
 
'''LangChain''' is an '''application framework''' that '''helps structure your prompts''' using PromptTemplate. For example, with an alerting system, when the '''AI is queried''', you can '''create a template''' that guides it to follow a consistent '''debugging structure''' in its responses :
 
<pre class="linux">
from langchain import PromptTemplate
 
prompt = PromptTemplate(
    input_variables=["alert", "logs", "metrics"],
    template="""
Tu es un expert Kubernetes.
Un incident a été détecté :
{alert}
 
Voici les logs du pod :
{logs}
 
Voici ses métriques :
{metrics}
 
Analyse les causes probables et propose des actions correctives précises.
"""
)
</pre>
 
== K8sGPT ==
 
'''K8sGPT''' is an '''open-source tool''' that '''scans Kubernetes clusters''', detects issues, and uses a Large Language Model (LLM) such as Azure OpenAI to explain problems and suggest solutions in natural language.
 
== Ollama ==
 
'''Ollama''' is an '''open-source tool''' that lets you '''download and run large language models''' (LLMs) like Llama 3 or Mistral locally, allowing you to use AI '''without relying on the cloud'''.

Version du 15 décembre 2025 à 21:36

Glossary

Large Language Model (LLM)

A Large Language Model (LLM) is the engine behind an AI application such as ChatGPT. In this case, the engine powering ChatGPT is GPT-4 (or GPT-4o, previously), which is the LLM used by the application.

Azure AI Foundry is a service that allows you to choose which Large Language Model (LLM) you want to use.

Model Context Protocol (MCP)

A Model Context Protocol (MCP) is a protocol that standardizes communication between Large Language Models (LLMs) and external systems , such as ITSM tools (like ServiceNow), Kubernetes clusters, and more.

You can use an MCP client , for example, Continue.dev in your IDE (like VS Code) and then configure MCP servers, such as your Kubernetes cluster, to enable your LLM to interact with these systems.

Retrieval-Augmented Generation (RAG)

Low-Code / No-Code

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