---
title: "#이맥스 #인공지능 #패키지 §org-ai §ellama"
url: "https://notes.junghanacs.com/notes/20240620T113005"
author: "Junghan Kim"
date: "2024-06-20T11:30:00+09:00"
lastmod: "2025-04-28T00:00:00+09:00"
tags: ["bib","emacs","gptel","howtouse","llmclient"]
description: "gptel 이외의 org-ai와 ellama 같은 이맥스 인공지능 패키지를 살펴보는 노트."
---

> [!abstract] 이 노트에 대하여
> 
> gptel 외에도 Emacs 안에서 쓸 수 있는 org-ai, ellama 계열 패키지의 가능성을 정리한다. 로컬 모델과 개인 비서 흐름까지 염두에 둔 LLM 클라이언트 비교 노트다.

## 히스토리

-   [2025-07-18 Fri 10:36] [#이맥스 #코드블록 #리터레이트# #인공지능 #패키지 §elysium §gpt-babel §copilot-chat](https://notes.junghanacs.com/notes/20250402T184512)
-   [2024-06-20 Thu 11:30] 쥐피텔 이외에 다른 녀석들 활용법

## 관련메타

-   [#인공지능](https://notes.junghanacs.com/meta/20240522T143822)

## BIBLIOGRAPHY

- “Rksm/Org-Ai.” 2025. <https://github.com/rksm/org-ai>.
- “S-Kostyaev/Ellama #Llmclient #Gptel.” 2024. <https://github.com/s-kostyaev/ellama>.

## 관련노트

-   [#이맥스: #쥐피텔 #활용법 #프로젝트](https://notes.junghanacs.com/notes/20240830T161957)

## @Rksm Org-Ai

([“Rksm/Org-Ai” 2025](https://notes.junghanacs.com/notes/20240620T113005#citeproc_bib_item_1))

Krahn, Robert 2025

Emacs as your personal AI assistant. Use LLMs such as ChatGPT or LLaMA for text generation or DALL-E and Stable Diffusion for image generation. Also supports speech input / output.

### org-ai

## @s-kostyaev ellama #llmclient #gptel

([“S-Kostyaev/Ellama #Llmclient #Gptel” 2024](https://notes.junghanacs.com/notes/20240620T113005#citeproc_bib_item_2))

-   Kostyaev, Sergey 2024
-   Ellama is a tool for interacting with large language models from Emacs.

### use local llm

4.1GB <https://ollama.com/library/zephyr>

```text

➜ curl -fsSL https://ollama.com/install.sh | sh
➜ ollama pull zephyr
pulling manifest
pulling 730ebed2578e... 100% ▕████████████████████████████████████████████████▏ 4.1 GB
pulling a406579cd136... 100% ▕████████████████████████████████████████████████▏ 1.1 KB
pulling a96be15318d6... 100% ▕████████████████████████████████████████████████▏  101 B
pulling fa956ab37b8c... 100% ▕████████████████████████████████████████████████▏   98 B
pulling 7d3eb0692a36... 100% ▕████████████████████████████████████████████████▏  483 B
verifying sha256 digest
writing manifest
removing any unused layers
success
(base) ~ via  v20.14.0 took 5m26s
➜
```

### ollama 설치

[ollama/ollama - github.com](https://github.com/ollama/ollama)

### ollama elisa install

&lt;2024-03-17 Sun 21:40&gt; [icon and emoji on web development::nil](https://notes.junghanacs.com/notes/20230623T125000#h-d7fa68d8-925a-4c85-9915-f9391ea5fd4e)

<https://github.com/s-kostyaev/elisa?tab=readme-ov-file>

여기서 설치 방법을 보라.

먼저 ollama 를 설치한다.

```text

ollama
curl -fsSL https://ollama.com/install.sh | sh

```

sqlite-vss 를 설치한다. [asg017/sqlite-vss - github.com](https://github.com/asg017/sqlite-vss)

```text
sudo apt-get install -y libgomp1 libatlas-base-dev liblapack-dev
npm install sqlite-vss
```

ollama 가져온다.

```text
ollama pull nomic-embed-text
ollama pull sskostyaev/openchat:8k-rag

-------------
~/doomemacs/.local/cache/elisa master ?1
➜ ollama pull nomic-embed-text
ollama pull sskostyaev/openchat:8k-rag
pulling manifest
pulling 970aa74c0a90... 100% ▕████████████████████████████████████████████████▏ 274 MB
pulling c71d239df917... 100% ▕████████████████████████████████████████████████▏  11 KB
pulling ce4a164fc046... 100% ▕████████████████████████████████████████████████▏   17 B
pulling 31df23ea7daa... 100% ▕████████████████████████████████████████████████▏  420 B
verifying sha256 digest
writing manifest
removing any unused layers
success
```