Chat Models

from openai import OpenAI
client = OpenAI(api_key="your_openai_key")

res = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is Python?"}]
)
# Accessing content requires deep nesting:
print(res.choices[0].message.content)

import google.generativeai as genai
genai.configure(api_key="your_gemini_key")
model = genai.GenerativeModel("gemini-2.5-flash")

res = model.generate_content("What is Python?")
# Accessing content uses .text:
print(res.text)

import requests
API_URL = "https://api-inference.huggingface.co/models/gpt2"
headers = {"Authorization": "Bearer your_hf_token"}

res = requests.post(API_URL, headers=headers, json={"inputs": "What is Python?"})
# Accessing content requires list/dictionary parsing:
print(res.json()[0]['generated_text'])

from langchain.chat_models import init_chat_model

# Standardized Initialization:
llm = init_chat_model("gemini-2.5-flash", model_provider="google_genai")
# To switch to OpenAI: llm = init_chat_model("gpt-4o", model_provider="openai")
# To switch to Groq: llm = init_chat_model("llama-3.3-70b-versatile", model_provider="groq")

# Standardized Invocation & Response Extraction (.content)
response = llm.invoke("What is Python?")
print(response.content)