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Parea for Observing, Testing & Fine-tuning of Instructor

Parea is a platform that enables teams to monitor, collaborate, test & label for LLM applications. In this blog we will explore how Parea can be used to enhance the OpenAI client alongside instructor and debug + improve instructor calls. Parea has some features which makes it particularly useful for instructor:

• it automatically groups any LLM calls due to reties under a single trace
• it automatically tracks any validation error counts & fields that occur when using instructor
• it provides a UI to label JSON responses by filling out a form instead of editing JSON objects

Example: Writing Emails with URLs from Instructor Docs

We will demonstrate Parea by using instructor to write emails which only contain URLs from the instructor docs. We'll need to install our dependencies before proceeding so simply run the command below.

Analyzing Youtube Transcripts with Instructor

Extracting Chapter Information

In this post, we'll show you how to summarise Youtube video transcripts into distinct chapters using instructor before exploring some ways you can adapt the code to different applications.

By the end of this article, you'll be able to build an application as per the video below.

Why Instructor is the best way to get JSON from LLMs

Large Language Models (LLMs) like GPT are incredibly powerful, but getting them to return well-formatted JSON can be challenging. This is where the Instructor library shines. Instructor allows you to easily map LLM outputs to JSON data using Python type annotations and Pydantic models.

Instructor makes it easy to get structured data like JSON from LLMs like GPT-3.5, GPT-4, GPT-4-Vision, and open-source models including Mistral/Mixtral, Ollama, and llama-cpp-python.

It stands out for its simplicity, transparency, and user-centric design, built on top of Pydantic. Instructor helps you manage validation context, retries with Tenacity, and streaming Lists and Partial responses.

• Instructor provides support for a wide range of programming languages, including:
• Python
• TypeScript
• Ruby
• Go
• Elixir

Enhancing RAG with Time Filters Using Instructor

Retrieval-augmented generation (RAG) systems often need to handle queries with time-based constraints, like "What new features were released last quarter?" or "Show me support tickets from the past week." Effective time filtering is crucial for providing accurate, relevant responses.

Instructor is a Python library that simplifies integrating large language models (LLMs) with data sources and APIs. It allows defining structured output models using Pydantic, which can be used as prompts or to parse LLM outputs.

Why Logfire is a perfect fit for FastAPI + Instructor

Logfire is a new tool that provides key insight into your application with Open Telemetry. Instead of using ad-hoc print statements, Logfire helps to profile every part of your application and is integrated directly into Pydantic and FastAPI, two popular libraries amongst Instructor users.

In short, this is the secret sauce to help you get your application to the finish line and beyond. We'll show you how to easily integrate Logfire into FastAPI, one of the most popular choices amongst users of Instructor using two examples

1. Data Extraction from a single User Query
2. Using asyncio to process multiple users in parallel
3. Streaming multiple objects using an Iterable so that they're available on demand

Logfire

Introduction

Logfire is a new observability platform coming from the creators of Pydantic. It integrates almost seamlessly with many of your favourite libraries such as Pydantic, HTTPx and Instructor. In this article, we'll show you how to use Logfire with Instructor to gain visibility into the performance of your entire application.

We'll walk through the following examples

1. Classifying scam emails using Instructor
2. Performing simple validation using the llm_validator
3. Extracting data into a markdown table from an infographic with GPT4V

Unified Provider Interface with String-Based Initialization

Instructor now offers a simplified way to initialize any supported LLM provider with a single consistent interface. This approach makes it easier than ever to switch between different LLM providers while maintaining the same structured output functionality you rely on.

The Problem

As the number of LLM providers grows, so does the complexity of initializing and working with different client libraries. Each provider has its own initialization patterns, API structures, and quirks. This leads to code that isn't portable between providers and requires significant refactoring when you want to try a new model.

The Solution: String-Based Initialization

We've introduced a new unified interface that allows you to initialize any supported provider with a simple string format:

import instructor
from pydantic import BaseModel

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

# Initialize any provider with a single consistent interface
client = instructor.from_provider("openai/gpt-4")
client = instructor.from_provider("anthropic/claude-3-sonnet")
client = instructor.from_provider("google/gemini-pro")
client = instructor.from_provider("mistral/mistral-large")

The from_provider function takes a string in the format "provider/model-name" and handles all the details of setting up the appropriate client with the right model. This provides several key benefits:

• Simplified Initialization: No need to manually create provider-specific clients
• Consistent Interface: Same syntax works across all providers
• Reduced Dependency Exposure: You don't need to import specific provider libraries in your application code
• Easy Experimentation: Switch between providers with a single line change

Supported Providers

The string-based initialization currently supports all major providers in the ecosystem:

• OpenAI: "openai/gpt-4", "openai/gpt-4o", "openai/gpt-3.5-turbo"
• Anthropic: "anthropic/claude-3-opus-20240229", "anthropic/claude-3-sonnet-20240229", "anthropic/claude-3-haiku-20240307"
• Google Gemini: "google/gemini-pro", "google/gemini-pro-vision"
• Mistral: "mistral/mistral-small-latest", "mistral/mistral-medium-latest", "mistral/mistral-large-latest"
• Cohere: "cohere/command", "cohere/command-r", "cohere/command-light"
• Perplexity: "perplexity/sonar-small-online", "perplexity/sonar-medium-online"
• Groq: "groq/llama2-70b-4096", "groq/mixtral-8x7b-32768", "groq/gemma-7b-it"
• Writer: "writer/palmyra-instruct", "writer/palmyra-instruct-v2"
• AWS Bedrock: "bedrock/anthropic.claude-v2", "bedrock/amazon.titan-text-express-v1"
• Cerebras: "cerebras/cerebras-gpt", "cerebras/cerebras-gpt-2.7b"
• Fireworks: "fireworks/llama-v2-70b", "fireworks/firellama-13b"
• Vertex AI: "vertexai/gemini-pro", "vertexai/text-bison"
• Google GenAI: "genai/gemini-pro", "genai/gemini-pro-vision"

Each provider will be initialized with sensible defaults, but you can also pass additional keyword arguments to customize the configuration. For model-specific details, consult each provider's documentation.

Async Support

The unified interface fully supports both synchronous and asynchronous clients:

# Synchronous client (default)
client = instructor.from_provider("openai/gpt-4")

# Asynchronous client
async_client = instructor.from_provider("anthropic/claude-3-sonnet", async_client=True)

# Use like any other async client
response = await async_client.chat.completions.create(
    response_model=UserInfo,
    messages=[{"role": "user", "content": "Extract information about John who is 30 years old"}]
)

Mode Selection

You can also specify which structured output mode to use with the provider:

import instructor
from instructor import Mode

# Override the default mode for a provider
client = instructor.from_provider(
    "anthropic/claude-3-sonnet", 
    mode=Mode.ANTHROPIC_TOOLS
)

# Use JSON mode instead of the default tools mode
client = instructor.from_provider(
    "mistral/mistral-large", 
    mode=Mode.MISTRAL_STRUCTURED_OUTPUTS
)

# Use reasoning tools instead of regular tools for Anthropic
client = instructor.from_provider(
    "anthropic/claude-3-opus", 
    mode=Mode.ANTHROPIC_REASONING_TOOLS
)

If not specified, each provider will use its recommended default mode:

• OpenAI: Mode.OPENAI_FUNCTIONS
• Anthropic: Mode.ANTHROPIC_TOOLS
• Google Gemini: Mode.GEMINI_JSON
• Mistral: Mode.MISTRAL_TOOLS
• Cohere: Mode.COHERE_TOOLS
• Perplexity: Mode.JSON
• Groq: Mode.GROQ_TOOLS
• Writer: Mode.WRITER_JSON
• Bedrock: Mode.ANTHROPIC_TOOLS (for Claude on Bedrock)
• Vertex AI: Mode.VERTEXAI_TOOLS

You can always customize this based on your specific needs and model capabilities.

Error Handling

The from_provider function includes robust error handling to help you quickly identify and fix issues:

# Missing dependency
try:
    client = instructor.from_provider("anthropic/claude-3-sonnet")
except ImportError as e:
    print("Error: Install the anthropic package first")
    # pip install anthropic

# Invalid provider format
try:
    client = instructor.from_provider("invalid-format")
except ValueError as e:
    print(e)  # Model string must be in format "provider/model-name"

# Unsupported provider
try:
    client = instructor.from_provider("unknown/model")
except ValueError as e:
    print(e)  # Unsupported provider: unknown. Supported providers are: ...

The function validates the provider string format, checks if the provider is supported, and ensures the necessary packages are installed.

Environment Variables

Like the native client libraries, from_provider respects environment variables set for each provider:

# Set environment variables 
import os
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key" 
os.environ["MISTRAL_API_KEY"] = "your-mistral-key"

# No need to pass API keys directly
client = instructor.from_provider("openai/gpt-4")

Troubleshooting

Here are some common issues and solutions when using the unified provider interface:

Model Not Found Errors

If you receive a 404 error, check that you're using the correct model name format:

Error code: 404 - {'type': 'error', 'error': {'type': 'not_found_error', 'message': 'model: claude-3-haiku'}}

For Anthropic models, always include the version date: - ✅ Correct: anthropic/claude-3-haiku-20240307 - ❌ Incorrect: anthropic/claude-3-haiku

Provider-Specific Parameters

Some providers require specific parameters for API calls:

# Anthropic requires max_tokens
anthropic_client = instructor.from_provider(
    "anthropic/claude-3-haiku-20240307", 
    max_tokens=400  # Required for Anthropic
)

# Use models with vision capabilities for multimodal content
gemini_client = instructor.from_provider(
    "google/gemini-pro-vision"  # Required for image processing
)

Working Example

Here's a complete example that demonstrates the automodel functionality with multiple providers:

import os
import asyncio
import instructor
from pydantic import BaseModel, Field

class UserInfo(BaseModel):
    """User information extraction model."""
    name: str = Field(description="The user's full name")
    age: int = Field(description="The user's age in years")
    occupation: str = Field(description="The user's job or profession")

async def main():
    # Test OpenAI
    openai_client = instructor.from_provider("openai/gpt-3.5-turbo")
    openai_result = openai_client.chat.completions.create(
        response_model=UserInfo,
        messages=[{"role": "user", "content": "Jane Doe is a 28-year-old data scientist."}]
    )
    print(f"OpenAI result: {openai_result.model_dump()}")

    # Test Anthropic with async client
    if os.environ.get("ANTHROPIC_API_KEY"):
        anthropic_client = instructor.from_provider(
            model="anthropic/claude-3-haiku-20240307",
            async_client=True,
            max_tokens=400  # Required for Anthropic
        )
        anthropic_result = await anthropic_client.chat.completions.create(
            response_model=UserInfo,
            messages=[{"role": "user", "content": "John Smith is a 35-year-old software engineer."}]
        )
        print(f"Anthropic result: {anthropic_result.model_dump()}")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

String-based initialization is a significant step toward making Instructor even more user-friendly and flexible. It reduces the learning curve for working with multiple providers and makes it easier than ever to experiment with different models.

Benefits include: - Simplified initialization with a consistent interface - Automatic selection of appropriate default modes - Support for both synchronous and asynchronous clients - Clear error messages to quickly identify issues - Respect for provider-specific environment variables - Comprehensive model selection across the entire LLM ecosystem

Whether you're building a new application or migrating an existing one, the unified provider interface offers a cleaner, more maintainable way to work with structured outputs across the LLM ecosystem.

Try it today with instructor.from_provider() and check out the complete example code in our repository!

Announcing instructor=1.0.0

Over the past 10 months, we've build up instructor with the principle of 'easy to try, and easy to delete'. We accomplished this by patching the openai client with the instructor package and adding new arguments like response_model, max_retries, and validation_context. As a result I truly believe isntructor is the best way to get structured data out of llm apis.

But as a result, we've been a bit stuck on getting typing to work well while giving you more control at development time. I'm excited to launch version 1.0.0 which cleans up the api w.r.t. typing without compromising the ease of use.

Matching Language in Multilingual Summarization Tasks

When asking language models to summarize text, there's a risk that the generated summary ends up in English, even if the source text is in another language. This is likely due to the instructions being provided in English, biasing the model towards English output.

In this post, we explore techniques to ensure the language of the generated summary matches the language of the source text. We leverage Pydantic for data validation and the langdetect library for language identification.

Structured Outputs with Anthropic

A special shoutout to Shreya for her contributions to the anthropic support. As of now, all features are operational with the exception of streaming support.

For those eager to experiment, simply patch the client with ANTHROPIC_JSON, which will enable you to leverage the anthropic client for making requests.

pip install instructor[anthropic]

from pydantic import BaseModel
from typing import List
import anthropic
import instructor

# Patching the Anthropics client with the instructor for enhanced capabilities
anthropic_client = instructor.from_openai(
    create=anthropic.Anthropic().messages.create,
    mode=instructor.Mode.ANTHROPIC_JSON
)

class Properties(BaseModel):
    name: str
    value: str

class User(BaseModel):
    name: str
    age: int
    properties: List[Properties]

user_response = anthropic_client(
    model="claude-3-haiku-20240307",
    max_tokens=1024,
    max_retries=0,
    messages=[
        {
            "role": "user",
            "content": "Create a user for a model with a name, age, and properties.",
        }
    ],
    response_model=User,
)  # type: ignore

print(user_response.model_dump_json(indent=2))
"""
{
    "name": "John",
    "age": 25,
    "properties": [
        {
            "key": "favorite_color",
            "value": "blue"
        }
    ]
}

We're encountering challenges with deeply nested types and eagerly invite the community to test, provide feedback, and suggest necessary improvements as we enhance the anthropic client's support.