┌─────────────────┐
│  Your App/UI    │
└────────┬────────┘
         │
    ┌────▼─────────────────────┐
    │  Agent Orchestrator       │
    │  (Python + LangChain)     │
    └────┬─────────────────────┘
         │
    ┌────┴──────────┬──────────────┬──────────┐
    │               │              │          │
┌───▼──┐      ┌────▼────┐   ┌────▼────┐  ┌──▼───┐
│ LLM  │      │ Tools    │   │ Memory  │  │APIs  │
│Model │      │ Executor │   │ Storage │  │      │
└──────┘      └──────────┘   └─────────┘  └──────┘

# SSH into your VPS
ssh root@your_vps_ip

# Install dependencies
apt update && apt upgrade -y
apt install -y python3.11 python3-pip git curl

# Create project directory
mkdir ai-agent && cd ai-agent
python3 -m venv venv
source venv/bin/activate

# Install core packages
pip install --upgrade pip
pip install langchain ollama python-dotenv requests

# Install Ollama
curl https://ollama.ai/install.sh | sh

# Pull the model (one-time, ~5GB download)
ollama pull mistral

# Start the Ollama server (runs on localhost:11434)
ollama serve

from langchain.llms import Ollama
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.memory import ConversationBufferMemory
import requests

# Initialize the local model
llm = Ollama(model="mistral", base_url="http://localhost:11434")

# Define tools your agent can use
def get_weather(location: str) -> str:
    """Fetch weather for a location"""
    try:
        response = requests.get(
            f"https://wttr.in/{location}?format=j1"
        )
        data = response.json()
        current = data['current_condition'][0]
        return f"Weather in {location}: {current['temp_C']}°C, {current['weatherDesc'][0]['value']}"
    except Exception as e:
        return f"Error fetching weather: {str(e)}"

def calculate_something(expression: str) -> str:
    """Simple calculator tool"""
    try:
        result = eval(expression)
        return f"Result: {result}"
    except:
        return "Invalid expression"

def search_knowledge_base(query: str) -> str:
    """Search your own knowledge base"""
    # This would connect to your vector DB
    return f"Found information about {query}"

# Create tool objects
tools = [
    Tool(
        name="Weather",
        func=get_weather,
        description="Get current weather for a location"
    ),
    Tool(
        name="Calculator",
        func=calculate_something,
        description="Perform mathematical calculations"
    ),
    Tool(
        name="Knowledge",
        func=search_knowledge_base,
        description="Search the knowledge base"
    ),
]

# Set up memory
memory = ConversationBufferMemory(memory_key="chat_history")

# Initialize the agent
agent = initialize_agent(
    tools,
    llm,
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    memory=memory,
    verbose=True
)

# Use the agent
result = agent.run("What's the weather in London and what's 25 * 4?")
print(result)

python
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Qdrant
from langchain.memory import VectorStoreRetrieverMemory
from qdrant_client import QdrantClient

# Initialize embeddings (runs locally, free)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# Initialize Qdrant client (in-memory for now, persists to disk)
client = QdrantClient(":memory:")
collection_name = "agent_memory"

# Create vector store
vectorstore = Qdrant(
    client=client,
    collection_name=collection_name,
    embedding_function=embeddings.embed_query,
)

# Create retriever-based memory
retriever = vectorstore.as_retriever()
memory = VectorStoreRet

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