Using Ollama Part 2 (Local LLM for Code Generation and Execution, qwen2.5-coder, Node-RED)
管理人Introduction
This time, I tried generating code with a local LLM and created a Node-RED flow so that it can be executed immediately.
Usually, I copy and paste code generated through ChatGPT prompts to run it. I wanted to see if this process could be automated.
I tried using my custom python-venv node to feed back execution results and install missing packages automatically.
▼Previous articles:
Control Microcontrollers with Voice (Node-RED, Gemma2, Faster Whisper, XIAO ESP32C3)
Introduction In this article, I tried to control a small robot using a microcontroller by voice. This is a summary of what I have tried before. To operate …
趣味的ロボット研究所 
Searching with Python and Summarizing with a Local LLM (Ollama, Node-RED)
Info This article is translated from Japanese to English. Introduction In this post, I experimented with a Python-based search program to automate information …
Trying qwen2.5-coder
For the local LLM model, I used qwen2.5-coder. There are so many models available in Ollama that it’s hard to know which is optimal, but this one seemed suited for code.
▼This time, I downloaded the 7b model:
https://ollama.com/library/qwen2.5-coder:7b
I installed and started it with the following command:
ollama run qwen2.5-coder:7bIt generated a Python program that prints “Hello” in Japanese.
▼The responses were natural in Japanese, fast, and streamed sequentially.
▼When I asked it to provide only code, it included triple backticks (```), which may need processing later in Node-RED.
I also tried generating a YOLO program that I usually use for image processing.
▼It suggested a version using YOLOv5 and OpenCV.
▼When I ran it, I encountered a ModuleNotFoundError.
Since I usually use the ultralytics package, I asked the model to generate code using that package.
▼It generated code using ultralytics, and I was able to run it using the already installed YOLOv8n model:
import cv2
from ultralytics import YOLO
# モデルの読み込み（ここではYOLOv5sを使用）
model = YOLO('yolov5s.pt')
# カメラの初期化
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
print("カメラから映像が取得できません。")
break
# モデルの予測
results = model(frame)
# 結果を描画
for result in results:
boxes = result.boxes.cpu().numpy()
for box in boxes:
x1, y1, x2, y2 = map(int, box.xyxy[0])
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), thickness=3)
# フレームを表示
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()Later, I asked it to output JSON since it would be easier to handle in Node-RED.
▼The output included required package names and Python code in JSON format.
This confirmed that Node-RED could use the output.
Using Node-RED
Basic command test
I used the Ollama node, which I have used previously, to interact with the local LLM.
▼Previous usage:
Using Ollama Part 1 (Gemma2, Node-RED)
Info This article is translated from Japanese to English. Introduction This time, I tried using Ollama, a tool that lets you run LLMs locally. You can install …
For Python execution, I used my custom python-venv node.
▼Development history:
https://qiita.com/background/items/d2e05e8d85427761a609
First, I created a simple flow to check the response from the Ollama node.
▼Flow example: Sending a command to display “Hello”
[{"id":"3d7edf383c255911","type":"template","z":"22eb2b8f4786695c","name":"","field":"payload","fieldType":"msg","format":"handlebars","syntax":"mustache","template":"{\n \"model\": \"qwen2.5-coder\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"{{payload}}\"\n }\n ]\n}","output":"json","x":1820,"y":3260,"wires":[["b2bed944826694b1"]]},{"id":"b2bed944826694b1","type":"ollama-chat","z":"22eb2b8f4786695c","name":"Chat","server":"","model":"","modelType":"str","messages":"","messagesType":"msg","format":"","stream":false,"keepAlive":"","keepAliveType":"str","tools":"","options":"","x":1650,"y":3320,"wires":[["1b0face17407124f"]]},{"id":"1b0face17407124f","type":"change","z":"22eb2b8f4786695c","name":"","rules":[{"t":"set","p":"payload","pt":"msg","to":"payload.message.content","tot":"msg"},{"t":"change","p":"payload","pt":"msg","from":"```python","fromt":"re","to":"","tot":"str"},{"t":"change","p":"payload","pt":"msg","from":"```","fromt":"str","to":"","tot":"str"}],"action":"","property":"","from":"","to":"","reg":false,"x":1820,"y":3320,"wires":[["94af02c6d4aea6ef"]]},{"id":"aa64a21b2f9adfec","type":"inject","z":"22eb2b8f4786695c","name":"","props":[{"p":"payload"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"Helloと表示したいです。","payloadType":"str","x":1450,"y":3260,"wires":[["cec439e00a4acf38"]]},{"id":"cec439e00a4acf38","type":"template","z":"22eb2b8f4786695c","name":"","field":"payload","fieldType":"msg","format":"handlebars","syntax":"mustache","template":"あなたはソフトウェアの一部です。\n命令をもとに、Pythonで実行するコードを生成してください。\nあなたの応答をもとにコードを実行するので、コード以外の回答は不要です。\n\n命令：{{payload}}","output":"str","x":1660,"y":3260,"wires":[["3d7edf383c255911"]]},{"id":"94af02c6d4aea6ef","type":"debug","z":"22eb2b8f4786695c","name":"debug 451","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":2010,"y":3320,"wires":[]}]▼The first template node specifies the role, and the second formats the data for the Ollama node.
The change node removes unnecessary parts from the response.
▼Without this, the response still included ``` markers.
▼The change node removes them.
▼As a result, the code can be extracted with line breaks intact.
This output is passed to the venv node, which can execute Python code from msg.code.
▼The following flow was created.
[{"id":"3d7edf383c255911","type":"template","z":"22eb2b8f4786695c","name":"","field":"payload","fieldType":"msg","format":"handlebars","syntax":"mustache","template":"{\n \"model\": \"qwen2.5-coder\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"{{payload}}\"\n }\n ]\n}","output":"json","x":1820,"y":3260,"wires":[["b2bed944826694b1"]]},{"id":"b2bed944826694b1","type":"ollama-chat","z":"22eb2b8f4786695c","name":"Chat","server":"","model":"","modelType":"str","messages":"","messagesType":"msg","format":"","stream":false,"keepAlive":"","keepAliveType":"str","tools":"","options":"","x":1650,"y":3320,"wires":[["1b0face17407124f"]]},{"id":"1b0face17407124f","type":"change","z":"22eb2b8f4786695c","name":"","rules":[{"t":"set","p":"payload","pt":"msg","to":"payload.message.content","tot":"msg"},{"t":"change","p":"payload","pt":"msg","from":"```python","fromt":"re","to":"","tot":"str"},{"t":"change","p":"payload","pt":"msg","from":"```","fromt":"str","to":"","tot":"str"}],"action":"","property":"","from":"","to":"","reg":false,"x":1820,"y":3320,"wires":[["94af02c6d4aea6ef","1557307e33fbd272"]]},{"id":"aa64a21b2f9adfec","type":"inject","z":"22eb2b8f4786695c","name":"","props":[{"p":"payload"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"Helloと表示したいです。","payloadType":"str","x":1450,"y":3260,"wires":[["e5abb95362f98b24"]]},{"id":"94af02c6d4aea6ef","type":"debug","z":"22eb2b8f4786695c","name":"debug 451","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":2010,"y":3320,"wires":[]},{"id":"fa4619be43ee2e11","type":"venv","z":"22eb2b8f4786695c","venvconfig":"c99155da59825db2","name":"","code":"","continuous":true,"x":1830,"y":3380,"wires":[["61cb970e3c67a69b"]]},{"id":"1557307e33fbd272","type":"change","z":"22eb2b8f4786695c","name":"","rules":[{"t":"set","p":"code","pt":"msg","to":"payload","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":1670,"y":3380,"wires":[["fa4619be43ee2e11"]]},{"id":"61cb970e3c67a69b","type":"debug","z":"22eb2b8f4786695c","name":"debug 453","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":1990,"y":3380,"wires":[]},{"id":"e5abb95362f98b24","type":"template","z":"22eb2b8f4786695c","name":"","field":"payload","fieldType":"msg","format":"handlebars","syntax":"mustache","template":"あなたはソフトウェアの一部です。\n命令をもとに、Pythonで実行するコードを生成してください。\nあなたの応答をもとにコードを実行するので、コード以外の回答は不要です。\n\n命令：{{payload}}","output":"str","x":1660,"y":3260,"wires":[["3d7edf383c255911"]]},{"id":"c99155da59825db2","type":"venv-config","venvname":"pyenv","version":"default"}]▼The output confirms that print("Hello") executed correctly.
Creating a flow including package installation
I modified the previous flow by changing only the instruction.
▼It is instructed to output the necessary packages and Python code in JSON format.
▼The reply came back as a string.
This string is parsed as JSON, packages are installed if necessary, and Python code is executed.
▼The following flow was created.
The code to print “Hello” ran without problems.
▼The output confirmed correct execution.
Next, I tried giving the command: “I want to perform real-time object detection with YOLO on the camera using yolov8n.pt, using ultralytics, and view the camera feed with OpenCV.” Various errors occurred, which I addressed step by step. The final flow is shown at the end.
▼The generated code used pip to install ultralytics, but errors occurred.
It included """ markers. Since multi-line code needs line breaks when parsed as JSON, I specified this in the instruction.
▼I indicated to parse it using a JSON node in Node-RED.
After correcting, I ran it again.
▼Packages were installed.
▼Sometimes “json” appeared at the start of the first line, sometimes not.
▼The change node removes the leading “json” text.
The generated code did not display a window using OpenCV, so I added the instruction: “I want to view the camera feed using OpenCV.”
▼The code ran and detection worked, though errors occurred along the way.
Various issues arose with properties, but I was eventually able to run it.
▼The detection state was displayed in real time, and results were output!
Because execution was needed each time an error occurred, I connected an error node. This allowed automatic execution until the code ran correctly.
▼The final flow looked like this.
[{"id":"4670a91b11fe217a","type":"catch","z":"22eb2b8f4786695c","name":"","scope":["51bf2cfb9a7e738f","7531b18cdd18c0b8","dd6f7612338bcd1f"],"uncaught":false,"x":1570,"y":3180,"wires":[["97ea72a11a11f075"]]},{"id":"97ea72a11a11f075","type":"template","z":"22eb2b8f4786695c","name":"","field":"payload","fieldType":"msg","format":"handlebars","syntax":"mustache","template":"エラーが起きました：{{payload}}\n直前の命令：{{flow.command}}","output":"str","x":1720,"y":3180,"wires":[["3d7edf383c255911"]]},{"id":"3d7edf383c255911","type":"template","z":"22eb2b8f4786695c","name":"","field":"payload","fieldType":"msg","format":"handlebars","syntax":"mustache","template":"{\n \"model\": \"qwen2.5-coder\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"{{payload}}\"\n }\n ]\n}","output":"json","x":1820,"y":3260,"wires":[["b2bed944826694b1"]]},{"id":"b2bed944826694b1","type":"ollama-chat","z":"22eb2b8f4786695c","name":"Chat","server":"","model":"","modelType":"str","messages":"","messagesType":"msg","format":"","stream":false,"keepAlive":"","keepAliveType":"str","tools":"","options":"","x":1650,"y":3320,"wires":[["1b0face17407124f"]]},{"id":"1b0face17407124f","type":"change","z":"22eb2b8f4786695c","name":"","rules":[{"t":"set","p":"payload","pt":"msg","to":"payload.message.content","tot":"msg"},{"t":"change","p":"payload","pt":"msg","from":"```python","fromt":"str","to":"","tot":"str"},{"t":"change","p":"payload","pt":"msg","from":"```","fromt":"str","to":"","tot":"str"},{"t":"change","p":"payload","pt":"msg","from":"^json","fromt":"re","to":"","tot":"str"},{"t":"change","p":"payload","pt":"msg","from":"\"\"\"","fromt":"str","to":"\"","tot":"str"}],"action":"","property":"","from":"","to":"","reg":false,"x":1820,"y":3320,"wires":[["94af02c6d4aea6ef","7531b18cdd18c0b8"]]},{"id":"7531b18cdd18c0b8","type":"json","z":"22eb2b8f4786695c","name":"LLM：JSON形式に変換","property":"payload","action":"obj","pretty":false,"x":1450,"y":3380,"wires":[["b7a3e4a8bc54bccf","73914d706c4e8af3"]]},{"id":"aa64a21b2f9adfec","type":"inject","z":"22eb2b8f4786695c","name":"","props":[{"p":"payload"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"Helloと表示したいです。","payloadType":"str","x":1450,"y":3260,"wires":[["cec439e00a4acf38"]]},{"id":"cec439e00a4acf38","type":"template","z":"22eb2b8f4786695c","name":"","field":"payload","fieldType":"msg","format":"handlebars","syntax":"mustache","template":"あなたはソフトウェアの一部です。\n命令をもとに、Pythonで実行するコードを生成してください。\n\n以下のようなJSON形式で回答してください。\n{\n \"pip\": <スペース区切りでインストールが必要なパッケージ名>,\n \"python\": <Pythonのコード>\n}\n\nPythonのコードは、複数行の場合は必ず改行文字を入れてください。\nあなたの回答をNode-REDのJSONノードでパースします。\nあなたの回答をもとにコードを実行するので、それ以外の回答は不要です。\n命令：{{payload}}","output":"str","x":1660,"y":3260,"wires":[["3d7edf383c255911","b4563966bae22d59"]]},{"id":"b7a3e4a8bc54bccf","type":"debug","z":"22eb2b8f4786695c","name":"debug 450","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":1670,"y":3380,"wires":[]},{"id":"94af02c6d4aea6ef","type":"debug","z":"22eb2b8f4786695c","name":"debug 451","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":2010,"y":3320,"wires":[]},{"id":"dd6f7612338bcd1f","type":"venv","z":"22eb2b8f4786695c","venvconfig":"c99155da59825db2","name":"LLM：コードの実行","code":"","continuous":true,"x":1980,"y":3480,"wires":[["9cbd84a26d1c55f1"]]},{"id":"9cbd84a26d1c55f1","type":"debug","z":"22eb2b8f4786695c","name":"debug 452","active":true,"tosidebar":true,"console":false,"tostatus":false,"complete":"false","statusVal":"","statusType":"auto","x":2190,"y":3480,"wires":[]},{"id":"e91fc18c6dd51401","type":"switch","z":"22eb2b8f4786695c","name":"pipの有無","property":"payload","propertyType":"msg","rules":[{"t":"nempty"},{"t":"else"}],"checkall":"true","repair":false,"outputs":2,"x":1600,"y":3440,"wires":[["f133f395ac894a59"],["b15f647449aca0ed"]]},{"id":"f133f395ac894a59","type":"pip","z":"22eb2b8f4786695c","venvconfig":"4657b6fbdbaf6f7e","name":"LLM：パッケージのインストール","arg":"","action":"install","tail":false,"x":1840,"y":3420,"wires":[["b15f647449aca0ed"]]},{"id":"61090aee4620d386","type":"inject","z":"22eb2b8f4786695c","name":"","props":[{"p":"payload"}],"repeat":"","crontab":"","once":false,"onceDelay":0.1,"topic":"","payload":"ultralyticsを利用した、yolov8n.ptによるYOLOのカメラに対するリアルタイム物体検出を行いたいです。カメラの映像はOpenCVで確認したいです。","payloadType":"str","x":1510,"y":3220,"wires":[["cec439e00a4acf38"]]},{"id":"b4563966bae22d59","type":"change","z":"22eb2b8f4786695c","name":"","rules":[{"t":"set","p":"command","pt":"flow","to":"payload","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":1850,"y":3220,"wires":[[]]},{"id":"73914d706c4e8af3","type":"change","z":"22eb2b8f4786695c","name":"","rules":[{"t":"set","p":"code","pt":"flow","to":"payload.python","tot":"msg"},{"t":"set","p":"payload","pt":"msg","to":"payload.pip","tot":"msg"}],"action":"","property":"","from":"","to":"","reg":false,"x":1420,"y":3440,"wires":[["e91fc18c6dd51401"]]},{"id":"b15f647449aca0ed","type":"change","z":"22eb2b8f4786695c","name":"","rules":[{"t":"set","p":"code","pt":"msg","to":"code","tot":"flow"}],"action":"","property":"","from":"","to":"","reg":false,"x":1770,"y":3480,"wires":[["dd6f7612338bcd1f"]]},{"id":"c99155da59825db2","type":"venv-config","venvname":"pyenv","version":"default"},{"id":"4657b6fbdbaf6f7e","type":"venv-config","venvname":"pyenv","version":"default"}]▼Here is a video of the actual execution.
I recorded about three runs, all successful without errors. Ideally, I would also capture the feedback when errors occur.
Conclusion
I was able to directly link local LLM code generation with execution. I plan to combine this with other nodes and flows I have tested so far.
It seems possible to input instructions via a dashboard, save working code, and automate tasks.
Because accidental file deletion is possible, it may be wise to isolate the environment to some degree.
▼There is an increasing variety of input and output options, such as voice commands, translation, and speech synthesis. These can also be combined.
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