Run LLMs locally on your own hardware using Ollama

import requests

response = requests.post("http://localhost:11434/api/chat", json={
    "model": "llama3.1:8b",
    "messages": [{"role": "user", "content": "Explain quantum computing"}],
    "stream": False,
    "temperature": 0.3,      # Lower for factual responses
    "top_p": 0.9,            # Nucleus sampling
    "num_predict": 500,      # Limit response length
    "repeat_penalty": 1.2,   # Reduce repetition
    "stop": ["```", "---"]   # Stop at code blocks or separators
})
data = response.json()
print(data["message"]["content"])

from langchain_community.chat_models import ChatOllama

llm = ChatOllama(
    model="llama3.1:8b",
    temperature=0.7,
    top_p=0.9,
    num_predict=256,
    repeat_penalty=1.1
)
response = llm.invoke("Hello!")
print(response.content)

import asyncio
from typing import Dict, List
from langchain_community.chat_models import ChatOllama
from langchain_core.prompts import ChatPromptTemplate

class SimpleAnalysisAgent:
    """A simple agent that analyzes text and provides insights."""

    def __init__(self, model_name: str = "llama3.1:8b"):
        self.llm = ChatOllama(model=model_name, temperature=0.1)

    def classify_text(self, text: str) -> str:
        """Classify the type of text."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Classify this text as one of: news, blog, email, code, academic, or other. Respond with just the category."),
            ("human", "{text}")
        ])
        chain = prompt | self.llm
        result = chain.invoke({"text": text[:500]})
        return result.content.strip().lower()

    def extract_key_points(self, text: str) -> List[str]:
        """Extract key points from text."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Extract 3-5 key points from this text. Return as a simple numbered list."),
            ("human", "{text}")
        ])
        chain = prompt | self.llm
        result = chain.invoke({"text": text})
        lines = result.content.strip().split('\n')
        return [l.strip() for l in lines if l.strip() and any(c.isdigit() for c in l[:3])][:5]

    def summarize(self, text: str) -> str:
        """Create a summary of the text."""
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Summarize this text in 2-3 sentences. Be concise and clear."),
            ("human", "{text}")
        ])
        chain = prompt | self.llm
        result = chain.invoke({"text": text})
        return result.content.strip()

    def analyze_text(self, text: str) -> Dict:
        """Complete analysis of text."""
        return {
            "category": self.classify_text(text),
            "key_points": self.extract_key_points(text),
            "summary": self.summarize(text),
            "length": len(text)
        }

pip install langchain langchain-community requests