Instructor Installation Guide: Setup for LLM Structured Outputs¶

Learn how to install Instructor, the leading Python library for extracting structured data from LLMs like GPT-4, Claude, and Gemini. This comprehensive installation tutorial covers all major LLM providers and gets you ready for production use.

Quick Start: Install Instructor for LLM Development¶

Get started with structured LLM outputs in seconds. Install Instructor using pip:

pip install instructor

Instructor leverages Pydantic for type-safe LLM data extraction:

pip install pydantic

Pro Tip: Use uv for faster installation: uv pip install instructor

LLM Provider Installation Guide¶

Instructor supports 15+ LLM providers. Here's how to install and configure each:

OpenAI (GPT-4, GPT-3.5)¶

OpenAI is the default LLM provider for Instructor. Perfect for GPT-4 and GPT-3.5-turbo structured outputs:

pip install instructor

Configure your OpenAI API key for LLM access:

export OPENAI_API_KEY=your_openai_key

Anthropic Claude LLM Setup¶

Extract structured data from Claude 3 models (Opus, Sonnet, Haiku) with native tool support:

pip install "instructor[anthropic]"

Configure Claude API access:

export ANTHROPIC_API_KEY=your_anthropic_key

Google Gemini LLM Integration¶

Use Gemini Pro and Flash models for structured outputs with function calling:

pip install "instructor[google-generativeai]"

Set up Gemini API access:

export GOOGLE_API_KEY=your_google_key

Cohere¶

To use with Cohere's models:

pip install "instructor[cohere]"

Set up your Cohere API key:

export COHERE_API_KEY=your_cohere_key

Mistral¶

To use with Mistral AI's models:

pip install "instructor[mistralai]"

Set up your Mistral API key:

export MISTRAL_API_KEY=your_mistral_key

LiteLLM (Multiple Providers)¶

To use LiteLLM for accessing multiple providers:

pip install "instructor[litellm]"

Set up API keys for the providers you want to use.

Verify Your Instructor LLM Setup¶

Test your Instructor installation with this simple LLM structured output example:

import instructor
from openai import OpenAI
from pydantic import BaseModel

class Person(BaseModel):
    name: str
    age: int

client = instructor.from_openai(OpenAI())
person = client.chat.completions.create(
    model="gpt-3.5-turbo",
    response_model=Person,
    messages=[
        {"role": "user", "content": "John Doe is 30 years old"}
    ]
)

print(f"Name: {person.name}, Age: {person.age}")

Next Steps in Your LLM Tutorial Journey¶

With Instructor installed, you're ready to build powerful LLM applications:

1. Create Your First LLM Extraction - Build structured outputs with any LLM
2. Master Response Models - Learn Pydantic models for LLM data validation
3. Configure LLM Clients - Set up OpenAI, Anthropic, Google, and more

Common Installation Issues¶

• Import Errors: Ensure you've installed the provider-specific extras (e.g., instructor[anthropic])
• API Key Issues: Verify your environment variables are set correctly
• Version Conflicts: Use pip install --upgrade instructor to get the latest version

Ready to extract structured data from LLMs? Continue to Your First Extraction →