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Trainer_LLM/.bash_history

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Wiki-Importer: Deduplizierung (kein Doppelimport)
2025-08-10 08:25:45 +02:00
sudo systemctl stop cloud-init
sudo systemctl disable cloud-init
sudo systemctl mask cloud-init
sudo touch /etc/cloud/cloud-init.disabled
sudo reboot
hostnamectl
sudo hostnamectl set-hostname llm-node
timedatectl list-timezones | grep Berlin
sudo timedatectl set-timezone Europe/Berlin
sudo update-locale LANG=de_DE.UTF-8
PermitRootLogin no
PasswordAuthentication yes
sudo nano /etc/ssh/sshd_config
sudo systemctl reload ssh
sudo apt install ufw -y
sudo ufw default deny incoming
sudo ufw default allow outgoing
sudo ufw allow OpenSSH
sudo ufw enable
sudo ufw status verbose
sudo apt update
sudo apt install -y ca-certificates curl gnupg lsb-release
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg
echo "deb [arch=$(dpkg --print-architecture) \
signed-by=/etc/apt/keyrings/docker.gpg] \
https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
docker --version
docker compose version
docker run hello-world
newgrp docker
docker run hello-world
curl "http://localhost:8000/search?query=Mae-geri"
source /home/llmadmin/venvs/embedding/bin/activate
exit
echo 'source ~/venvs/embedding/bin/activate' >> ~/.bash_aliases
source ~/.bash_aliases
embedding
sudo nano /home/llmadmin/llm-api/app.py
ls
cd venvs
ls
cd embedding/
ls
cd /
ls
cd home
ls
cd llmadmin/
ls
mkdir -p ~/llm-api
ls
cd llm-api/
nano app.py
mkdir logs
ls
uvicorn app:app --host 0.0.0.0 --port 8000
groups
sudo usermod -aG docker $USER
newgrp docker
curl -fsSL https://ollama.com/install.sh | sh
ollama --version
ollama version is 0.10.1
ollama run mistral
curl http://localhost:11434/api/tags
mkdir -p ~/docker/qdrant
cd ~/docker/qdrant
nano docker-compose.yml
docker compose up -d
docker ps
curl http://localhost:6333/collections
sudo systemctl enable ollama
sudo systemctl start ollama
ollama run mistral --keep-alive
sudo apt install python3 python3-pip -y
pip3 install --upgrade pip
# 1. Installiere venv (falls noch nicht da)
sudo apt install python3-venv -y
# 2. Erstelle eine neue virtuelle Umgebung z. B. im Projektverzeichnis
python3 -m venv ~/venvs/embedding
# 3. Aktiviere die virtuelle Umgebung
source ~/venvs/embedding/bin/activate
# 4. Upgrade pip (jetzt erlaubt!)
pip install --upgrade pip
# 5. Installiere deine Embedding-Tools, z. B.
pip install sentence-transformers qdrant-client
sudo nano embed_to_qdrant.py
python embed_to_qdrant.py
sudo nano search_qdrant.py
python search_qdrant.py
sudo nano search_qdrant.py
python search_qdrant.py
sudo nano search_qdrant.py
python search_qdrant.py
pip install fastapi uvicorn
pip install requests
sudo nano app.py
uvicorn app:app --host 0.0.0.0 --port 8000
ls
rm app.py
ls
curl -X POST http://localhost:8000/embed -H "Content-Type: application/json" -d '{"texts": ["Mae-geri ist ein gerader Tritt.", "Hikite stabilisiert die Technik."]}'
curl "http://localhost:8000/search?query=Wie tritt man Mae-geri?"
curl -X POST http://localhost:8000/prompt -H "Content-Type: application/json" -d '{"query": "Wie wird Mae-geri ausgeführt?"}'
curl "http://localhost:8000/search?query=Wie tritt man Mae-geri?"
curl "http://localhost:8000/search?query=Wie%20tritt%20man%20Mae-geri?"
curl --get http://localhost:8000/search --data-urlencode "query=Wie tritt man Mae-geri?"
/home/llmadmin/llm-api/app.py
/home/llmadmin/venvs/embedding/
/home/llmadmin/venvs/embedding/bin/python --version
cd /
cd ~/llm-api
uvicorn app:app --host 0.0.0.0 --port 8000
sudo nano /etc/systemd/system/llm-api.service
sudo systemctl daemon-reexec
sudo systemctl daemon-reload
sudo systemctl enable llm-api
sudo systemctl start llm-api
sudo systemctl status llm-api
deactivate
ls
cd /
ls
cd home
ls
cd llmadmin/
ls
cd docker/
ls
cd q
cd qdrant/
ls
nano docker-compose.yml
sudo restart
sudo systemctl status llm-api
docker ps
curl http://localhost:8000/search?query=test
cd /
curl http://localhost:8000/search?query=test
sudo reboot
sudo systemctl status llm-api
docker ps
curl http://localhost:8000/search?query=test
sudo systemctl status llm-api
docker ps
curl http://localhost:8000/search?query=test
docker ps
curl http://localhost:8000/search?query=test
● llm-api.service - FastAPI Embedding API (LLM)
Aug 04 13:41:14 llm-node systemd[1]: Started llm-api.service - FastAPI Embeddin>
Aug 04 13:41:23 llm-node uvicorn[1312]: INFO: Started server process [1312]
Aug 04 13:41:23 llm-node uvicorn[1312]: INFO: Waiting for application start>
Aug 04 13:41:23 llm-node uvicorn[1312]: INFO: Application startup complete.
Aug 04 13:41:23 llm-node uvicorn[1312]: INFO: Uvicorn running on http://0.0>
Aug 04 13:42:05 llm-node uvicorn[1312]: /home/llmadmin/venvs/embedding/lib/pyth>
Aug 04 13:42:05 llm-node uvicorn[1312]: return forward_call(*args, **kwargs)
Aug 04 13:42:05 llm-node uvicorn[1312]: INFO: 127.0.0.1:58988 - "GET /searc>
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
30dfd2e38f0b qdrant/qdrant "./entrypoint.sh" 2 hours ago Up About a minute 0.0.0.0:6333->6333/tcp, [::]:6333->6333/tcp, 6334/tcp qdrant
[{"score":0.105378054,"text":"Hikite stabilisiert die Technik."},{"score":0.067576095,"text":"Ein Kumite-Drill verbessert die Reaktionsgeschwindigkeit."},{"score":0.042029735,"text":"Mae-geri ist ein gerader Tritt."}](embedding) llmadmin@llm-node:~$
deaktivate
Aug 04 13:41:23 llm-node uvicorn[1312]: INFO: Uvicorn running on http://0.0>deactivate
deaktivate
deactivate
sudo systemctl status llm-api
docker ps
curl http://localhost:8000/search?query=test
sudo systemctl status llm-api
sudo ss -tuln | grep 8000
sudo journalctl -fu llm-api
sudo ufw status verbose
sudo ufw allow 8000/tcp
sudo ufw reload
sudo ufw status verbose
cd home
ls
cd llm-api
nano app.py
cd ~/llm-api
uvicorn app:app --host 0.0.0.0 --port 8000
source ~/venvs/embedding/bin/activate
cd ~/llm-api
uvicorn app:app --host 0.0.0.0 --port 8000
sudo reboot
uvicorn app:app --host 0.0.0.0 --port 8000
cd llm-api/
uvicorn app:app --host 0.0.0.0 --port 8000
uvicorn llm-api:app --host 0.0.0.0 --port 8000
ls
./test_llm_api.sh
pkill -f "uvicorn llm_api:app"
uvicorn llm-api:app --host 0.0.0.0 --port 8000
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
uvicorn app:app --host 0.0.0.0 --port 8000
pkill -f "uvicorn llm_api:app"
lsof -i TCP:8000 -sTCP:LISTEN
kill 52619
lsof -i TCP:8000 -sTCP:LISTEN
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
lsof -i TCP:8000 -sTCP:LISTEN
./test_llm_api.sh
pkill -f "uvicorn llm_api:app"
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
./test_llm_api.sh
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
./test_llm_api.sh
pkill -f "uvicorn llm_api:app"
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
lsof -i TCP:8000 -sTCP:LISTEN
kill 54019
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
pkill -f "uvicorn llm_api:app"
lsof -i TCP:8000 -sTCP:LISTEN
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
# Tötet alle Prozesse, die auf Port 8000 lauschen
lsof -ti TCP:8000 | xargs -r kill -9
lsof -i TCP:8000 -sTCP:LISTEN
# im llm-api-Verzeichnis und aktiviertem venv
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 &
SERVER_PID=$!
echo "Uvicorn läuft als PID $SERVER_PID"
lsof -ti TCP:8000 | xargs -r kill -9
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 &
pip install nltk
python - <<EOF
import nltk
nltk.download('punkt')
EOF
pip install pdfplumber
pip install pillow
# im virtuellen Environment
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
(embedding) llmadmin@llm-node:~$ # im virtuellen Environment
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
INFO: Will watch for changes in these directories: ['/home/llmadmin']
ERROR: [Errno 98] Address already in use
lsof -i TCP:8000 -sTCP:LISTEN
kill 27023
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
lsof -i TCP:8000 -sTCP:LISTEN
cd llm-api/
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
lsof -i TCP:8000 -sTCP:LISTEN
kill 48457
lsof -i TCP:8000 -sTCP:LISTEN
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
lsof -i TCP:8000 -sTCP:LISTEN
sudo ufw allow 8000/tcp
sudo ufw status
lsof -i TCP:8000 -sTCP:LISTEN
kill 48507
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
curl -i http://127.0.0.1:8000/openapi.json
curl -X POST http://127.0.0.1:8000/embed -H "Content-Type: application/json" -d '{"text": "Test"}'
curl -i -X POST http://127.0.0.1:8000/embed -H "Content-Type: application/json" -d '{
"collection": "test",
"chunks": [
{
"text": "Das ist ein Testtext.",
"source": "unit-test"
}
]
}'
curl -G http://127.0.0.1:8000/search --data-urlencode "query=Test"
curl -i -G http://127.0.0.1:8000/search --data-urlencode "query=Test" -H "Accept: application/json"
curl -i -G http://127.0.0.1:8000/search --data-urlencode "query=Test" --data-urlencode "collection=test" -H "Accept: application/json"
lsof -i TCP:8000 -sTCP:LISTEN
kill 48633
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
lsof -i TCP:8000 -sTCP:LISTEN
kill 49437
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
lsof -i TCP:8000 -sTCP:LISTEN
cd ~/llm-api
chmod +x test_llm_api.sh
./test_llm_api.sh
chmod +x ~/llm-api/test_llm_api.sh
cd ~/llm-api
./test_llm_api.sh
nano test_llm_api.sh
./test_llm_api.sh
# Achte darauf, dass im aktuellen Shell-Environment OLLAMA_URL stimmt (Default ist bereits /api/generate).
pkill -f "uvicorn llm_api:app" # alten Prozess beenden
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
cd ~/llm-api
./test_llm_api.sh
pkill -f "uvicorn llm_api:app"
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
pkill -f "uvicorn llm_api:app" # alten Prozess beenden
lsof -i TCP:8000 -sTCP:LISTEN
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000
pkill -f "uvicorn llm_api:app"
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
pkill -f "uvicorn llm_api:app"
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
./test_llm_api.sh
pkill -f "uvicorn llm_api:app"
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
./test_llm_api.sh
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
./test_llm_api.sh
pkill -f "uvicorn llm_api:app"
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
./test_llm_api.sh
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
./test_llm_api.sh
pkill -f "uvicorn llm_api:app"
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 --log-level debug
./test_llm_api.sh
pkill -f "uvicorn llm_api:app" # alten Prozess beenden
lsof -i TCP:8000 -sTCP:LISTEN
uvicorn llm_api:app --reload --host 0.0.0.0 --p ort 8000
deactivate
uvicorn llm_api:app --reload --host 0.0.0.0 --p ort 8000
./test_llm_api.sh
# Server beenden, falls noch laufend
lsof -ti TCP:8000 | xargs -r kill -9
# Server neu starten
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 &
# Kurz warten
sleep 2
# Tests
./test_llm_api.sh
lsof -i TCP:8000 -sTCP:LISTEN
lsof -ti TCP:8000 | xargs -r kill -9
lsof -i TCP:8000 -sTCP:LISTEN
uvicorn llm_api:app --reload --host 0.0.0.0 --port 8000 &
deactivate
curl -i -X POST http://127.0.0.1:11434/api/generate -H "Content-Type: application/json" -d '{
"model": "mistral:latest",
"prompt": "Hallo",
"stream": false
}'
cd knowledge/
cd karatetrainer/
nano Lateral.txt
ls
cd _imported/
ls
cd ..
ls
nano Literal.txt
nano Lateral.txt
cd llm-api/
sudo systemctl restart llm-api
sudo journalctl -fu llm-api
sudo systemctl status llm-api
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