Plataformas e automação de IA

# Install Docker (if not already installed)
sudo apt update
sudo apt install -y docker.io docker-compose-v2
sudo usermod -aG docker $USER
newgrp docker
 
# Clone the Dify repository
git clone https://github.com/langgenius/dify.git
cd dify/docker
 
# Copy the environment variable configuration file
cp .env.example .env
 
# Edit the configuration file (modify as needed)
nano .env

# Important settings in .env file
 
# Secret key (must change to a random value)
SECRET_KEY=your-random-secret-key-here
 
# Database configuration
DB_USERNAME=postgres
DB_PASSWORD=your-secure-password
DB_HOST=db
DB_PORT=5432
DB_DATABASE=dify
 
# Redis configuration
REDIS_HOST=redis
REDIS_PORT=6379
REDIS_PASSWORD=your-redis-password
 
# Storage configuration
STORAGE_TYPE=local
STORAGE_LOCAL_PATH=storage
 
# Web configuration
WEB_API_URL=http://localhost/api

# Start Dify
docker compose up -d
 
# Check startup status
docker compose ps
 
# View logs
docker compose logs -f
 
# Wait for all services to start (about 1-2 minutes)

# Dify web interface defaults to port 80
# Open your browser and visit
# http://localhost
 
# First visit requires setting up an admin account
# Set email and password, then log in

# Connect OpenAI
# Settings > Model Providers > OpenAI > Enter API Key
 
# Connect Anthropic (Claude)
# Settings > Model Providers > Anthropic > Enter API Key
 
# Connect local Ollama
# Settings > Model Providers > Ollama
# Model name: qwen2.5:14b
# Base URL: http://host.docker.internal:11434
# (Use host.docker.internal to access host machine from Docker)

# Modify Ollama listen address
sudo systemctl edit ollama
# Add:
# [Service]
# Environment="OLLAMA_HOST=0.0.0.0:11434"
 
sudo systemctl restart ollama

cd ~/dify/docker
 
# Pull latest code
git pull origin main
 
# Update images and restart
docker compose pull
docker compose down
docker compose up -d

# Backup Dify data (database and storage)
cd ~/dify/docker
 
# Stop services
docker compose down
 
# Backup data volumes
sudo tar -czf ~/dify-backup-$(date +%Y%m%d).tar.gz \
  ./volumes/
 
# Restart services
docker compose up -d

# Create data directory
mkdir -p ~/.n8n
 
# Run n8n with Docker
docker run -d \
  --name n8n \
  -p 5678:5678 \
  -v ~/.n8n:/home/node/.n8n \
  -e N8N_SECURE_COOKIE=false \
  --restart always \
  n8nio/n8n

mkdir -p ~/n8n && cd ~/n8n
 
cat > docker-compose.yml << 'EOF'
services:
  n8n:
    image: n8nio/n8n
    container_name: n8n
    ports:
      - "5678:5678"
    environment:
      - N8N_SECURE_COOKIE=false
      - GENERIC_TIMEZONE=Asia/Shanghai
    volumes:
      - n8n_data:/home/node/.n8n
    extra_hosts:
      - "host.docker.internal:host-gateway"
    restart: always
 
volumes:
  n8n_data:
EOF
 
docker compose up -d

# Install Node.js
curl -fsSL https://deb.nodesource.com/setup_24.x | sudo -E bash -
sudo apt install -y nodejs
 
# Install n8n globally
npm install -g n8n
 
# Start n8n
n8n start
 
# Or run in background
n8n start &

# Open your browser
# http://localhost:5678
 
# First visit requires creating an account

Webhook Trigger -> AI Agent (Claude/GPT) -> Knowledge Base Retrieval -> Generate Reply -> Send Response

File Monitor -> Read File -> AI Summary Generation -> Save Result -> Email Notification

1. Add a "When chat message received" trigger
2. Add an "AI Agent" node
3. Connect a model: Add an "Ollama Chat Model" sub-node
4. Add tools:
   - "Calculator" -- Math calculations
   - "Code" -- Execute code
   - "HTTP Request" -- Call APIs
   - "Wikipedia" -- Search Wikipedia
5. Add memory: Connect a "Window Buffer Memory" node
6. Save and activate the workflow

# Create a project virtual environment
mkdir -p ~/langchain-project && cd ~/langchain-project
python3 -m venv venv
source venv/bin/activate
 
# Install LangChain core packages
pip install langchain langchain-core
 
# Install model provider packages
pip install langchain-anthropic    # Claude
pip install langchain-openai       # OpenAI
pip install langchain-ollama       # Ollama (local models)
 
# Install LangGraph
pip install langgraph
 
# Install common tools
pip install langchain-community    # Community integrations
pip install langchain-chroma       # Chroma vector database
pip install langchain-text-splitters  # Text splitting

# basic_chain.py
from langchain_ollama import ChatOllama
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
 
# Use local Ollama model
llm = ChatOllama(model="qwen2.5:14b", base_url="http://localhost:11434")
 
# Create a prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an Ubuntu Linux expert skilled at answering system administration questions."),
    ("human", "{question}")
])
 
# Build the chain
chain = prompt | llm | StrOutputParser()
 
# Invoke the chain
response = chain.invoke({"question": "How do I check disk usage on Ubuntu?"})
print(response)

# Run the example
python basic_chain.py

# rag_example.py
from langchain_ollama import ChatOllama, OllamaEmbeddings
from langchain_chroma import Chroma
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
 
# 1. Prepare documents
documents = [
    "Ubuntu 26.04 LTS, codenamed Resolute Raccoon, is scheduled for release on April 23, 2026.",
    "Ubuntu uses APT as its package manager. You can install software with apt install.",
    "Ubuntu's default desktop environment is GNOME, but it also supports KDE, XFCE, etc.",
    "Ubuntu Server edition does not include a graphical interface, making it suitable for server deployments.",
]
 
# 2. Split text
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=200, chunk_overlap=50
)
splits = text_splitter.create_documents(documents)
 
# 3. Create vector database
embeddings = OllamaEmbeddings(model="nomic-embed-text")
vectorstore = Chroma.from_documents(splits, embeddings)
retriever = vectorstore.as_retriever()
 
# 4. Build RAG chain
llm = ChatOllama(model="qwen2.5:14b")
prompt = ChatPromptTemplate.from_template("""
Answer the question based on the following context. If the answer is not in the context, say you don't know.
 
Context: {context}
Question: {question}
""")
 
rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)
 
# 5. Query
result = rag_chain.invoke("What is the codename for Ubuntu 26.04?")
print(result)

# Install additional dependencies and run
pip install chromadb
python rag_example.py

# agent_example.py
from langchain_ollama import ChatOllama
from langgraph.prebuilt import create_react_agent
from langchain_core.tools import tool
 
# Define tools
@tool
def search_docs(query: str) -> str:
    """Search Ubuntu documentation for information"""
    docs = {
        "apt": "APT is Ubuntu's package management tool. Use apt install to install software",
        "systemd": "systemd is Ubuntu's service management system. Use systemctl to manage services",
    }
    for key, value in docs.items():
        if key in query.lower():
            return value
    return "No relevant documentation found"
 
@tool
def run_command(command: str) -> str:
    """Simulate running a Linux command and return the result"""
    return f"Command '{command}' executed (simulated)"
 
# Create Agent
llm = ChatOllama(model="qwen2.5:14b")
agent = create_react_agent(llm, [search_docs, run_command])
 
# Run Agent
result = agent.invoke({
    "messages": [{"role": "user", "content": "How do I install nginx on Ubuntu?"}]
})
for message in result["messages"]:
    print(f"[{message.type}]: {message.content}")

# LangSmith provides visual debugging and monitoring for LangChain applications
# Register at https://smith.langchain.com to get an API key
 
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY="your-langsmith-api-key"
export LANGCHAIN_PROJECT="my-ubuntu-project"
 
# LangChain code run afterward will automatically report chain data to LangSmith