Phase 1Beginner⏱ 15 minutes

Working with
Different AI Models

This beginner-friendly tutorial teaches you how to work with various AI providers, compare their strengths, and choose the right model for your needs. Understanding different models is crucial before diving into LangChain, where you'll seamlessly switch between them.

🎯

Learning Objectives

Set up and use APIs from OpenAI, Anthropic, and Google
Compare model capabilities, pricing, and performance
Run open source models locally with Ollama
Build provider-agnostic applications

🌟 Why Learn About Different Models Before LangChain?

As a beginner, understanding different AI models helps you make informed decisions when building applications. LangChain makes it easy to switch between models, but knowing their strengths and limitations ensures you choose the right tool for each task. This tutorial gives you that foundation!

⚖️

Model Comparison Overview

Provider	Best For	Key Features	Pricing Model
GPT OpenAI	General purpose, code generation, creative tasks	Function calling, JSON mode, vision, DALL-E	Per token (input/output)
C Anthropic	Long context, analysis, safety-critical	200K context, constitutional AI, artifacts	Per token (input/output)
G Google	Multimodal, Google ecosystem integration	Native multimodal, search grounding	Per character/request
OS Open Source	Privacy, customization, offline use	Local deployment, fine-tuning, no limits	Free (compute costs only)

🔧

Setting Up Different Providers

C
Anthropic Claude Setup

1. Install the Anthropic SDK:

pip install anthropic

2. Use Claude in Python:

import anthropic

client = anthropic.Anthropic(
    api_key="your-api-key"
)

response = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1000,
    messages=[
        {"role": "user", "content": "Hello, Claude!"}
    ]
)

print(response.content[0].text)

G
Google Gemini Setup

1. Install the Google AI SDK:

pip install google-generativeai

2. Use Gemini in Python:

import google.generativeai as genai

genai.configure(api_key="your-api-key")
model = genai.GenerativeModel('gemini-pro')

response = model.generate_content("Hello, Gemini!")
print(response.text)

🦙
Open Source Models with Ollama

1. Install Ollama from ollama.ai

2. Pull and run a model:

ollama pull llama2
ollama run llama2

3. Use with Python:

import requests
import json

response = requests.post(
    "http://localhost:11434/api/generate",
    json={
        "model": "llama2",
        "prompt": "Hello, Llama!",
        "stream": False
    }
)

print(json.loads(response.text)["response"])

🔗

Unified Interface with LangChain

LangChain provides a unified interface to work with multiple providers seamlessly:

from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_community.llms import Ollama

# Initialize different models
gpt4 = ChatOpenAI(model="gpt-4", temperature=0.7)
claude = ChatAnthropic(model="claude-3-opus-20240229")
gemini = ChatGoogleGenerativeAI(model="gemini-pro")
llama = Ollama(model="llama2")

# Use the same interface for all
prompt = "Explain quantum computing in simple terms"

# Each model uses the same method
gpt_response = gpt4.invoke(prompt)
claude_response = claude.invoke(prompt)
gemini_response = gemini.invoke(prompt)
llama_response = llama.invoke(prompt)

Benefits of LangChain:

• Consistent API across all providers
• Easy provider switching
• Built-in retry and error handling
• Streaming support for all models

🎯

Step-by-Step Guide: Choosing the Right AI Model for Beginners

For Speed & Cost Efficiency

→GPT-3.5 Turbo: Fast, affordable, good quality
→Claude Haiku: Anthropic's fastest model
→Gemini Flash: Google's lightweight option

For Maximum Quality

→GPT-4: Best for complex reasoning
→Claude Opus: Excellent for analysis
→Gemini Ultra: Strong multimodal capabilities

For Specific Use Cases

→Long documents: Claude (200K context)
→Code generation: GPT-4, Claude
→Multimodal: Gemini, GPT-4V

For Privacy & Control

→Llama 2/3: Run locally, no data sent
→Mistral: Efficient local models
→Phi-2: Small but capable

💰

Cost Optimization Strategies

1. Use Model Routing

Route simple queries to cheaper models, complex ones to premium models.

2. Implement Caching

Cache common responses to avoid repeated API calls.

3. Batch Processing

Some providers offer discounts for batch processing.

4. Monitor Usage

Track token usage and costs across all providers.

✨ Best Practices

Development

• Start with cheaper models during development
• Test prompts across multiple providers
• Build provider-agnostic abstractions
• Implement proper error handling

Production

• Use environment variables for API keys
• Implement fallback models
• Monitor latency and costs
• Consider rate limits and quotas

🎉 Congratulations! Ready for LangChain

Excellent work completing this beginner's guide to AI models! You've mastered the fundamentals of AI APIs, prompt engineering, and model selection - all essential skills for your LangChain journey.

You're now ready to dive into LangChain, where you'll use these skills to build sophisticated AI applications with chains, agents, and more. Let's continue your learning adventure!

Previous: Prompt Engineering Next: LangChain Architecture