LLMs Basics

uv venv && source .venv/bin/activate
uv pip install "openai>=1.60" "anthropic>=0.40" "tiktoken>=0.8"
export OPENAI_API_KEY="sk-..."  # or ANTHROPIC_API_KEY

from openai import OpenAI
 
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain Python list comprehensions in one sentence."}],
)
print(response.choices[0].message.content)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a concise Python tutor. Answer in 2 sentences max."},
        {"role": "user", "content": "What is a generator expression?"},
    ],
)

import tiktoken
 
enc = tiktoken.encoding_for_model("gpt-4o-mini")
text = "Explain gradient descent for a junior developer."
tokens = enc.encode(text)
print(f"tokens: {len(tokens)}")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Give me a creative team name for a Python project."}],
    temperature=0.9,  # creative
)
 
response2 = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Is 2+2 equal to 4?"}],
    temperature=0.0,  # deterministic
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Summarize the Python GIL."}],
    max_tokens=100,
)

messages = [
    {"role": "user", "content": "My app uses FastAPI and PostgreSQL."},
    {"role": "assistant", "content": "Great stack! What would you like help with?"},
    {"role": "user", "content": "How should I structure database migrations?"},
]
response = client.chat.completions.create(model="gpt-4o-mini", messages=messages)

import anthropic
 
claude = anthropic.Anthropic()
message = claude.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=256,
    system="You are a helpful Python assistant.",
    messages=[{"role": "user", "content": "What is pydantic used for?"}],
)
print(message.content[0].text)

messages = [
    {"role": "system", "content": "Classify sentiment as positive, negative, or neutral."},
    {"role": "user", "content": "Text: I love this library!\nSentiment: positive"},
    {"role": "assistant", "content": "positive"},
    {"role": "user", "content": "Text: It crashed twice today.\nSentiment:"},
]
response = client.chat.completions.create(model="gpt-4o-mini", messages=messages, temperature=0)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "List 3 Python web frameworks as JSON with name and year fields."}],
    response_format={"type": "json_object"},
)
import json
data = json.loads(response.choices[0].message.content)

import os
from openai import OpenAI
 
api_key = os.environ.get("OPENAI_API_KEY")
if not api_key:
    raise RuntimeError("Set OPENAI_API_KEY")
 
client = OpenAI(api_key=api_key)