Parsing structured outputs from Large Language Models (LLMs) is a crucial skill when developing AI-powered applications.
It allows developers to extract precise, predictable information from models, transforming unstructured text into structured data.
In LangChain, there are various methods for parsing structured outputs from LLMs, each with its own strengths and use cases.
In this article, we'll explore three primary techniques: the with_structured_output method, the PydanticOutputParser class, and the StructuredOutputParser class.
Read the whole post to discover how to use these methods provide the flexibility to handle different requirements, ensuring type safety, and enable users to extract key-value information accurately from LLM outputs.
The Importance of Structured Outputs
Working with structured outputs is one of the key components of many AI-driven applications.
By extracting structured data from LLMs, we can integrate the responses more seamlessly into our software solutions.
LangChain provides methods to do this with varying levels of type safety and flexibility, allowing developers to define specific formats for model responses.
with_structured_output Method
The with_structured_output method is a powerful feature in LangChain that allows you to guide LLMs in generating structured responses based on a provided schema.
It's like giving the LLM a blueprint for its output, ensuring that the response adheres to a specific format.
This method shines when you're working with LLMs that support JSON mode or structured output APIs, such as OpenAI and Anthropic.
However, if the model doesn't natively support such features, you need to use an output parser, such as the PydanticOutputParser or StructuredOutputParser, to extract the structured response.
It's particularly useful when you need to:
Enforce a specific output structure directly within the prompt.
Leverage the LLM's native capabilities for structured outputs.
Ensure type safety and validation of the generated content.
Weather Forecast Example
Imagine we want to create a weather forecast generator using an LLM.
We'll use the with_structured_output method to ensure our weather forecast is properly formatted.
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
# Define our WeatherForecast model
class WeatherForecast(BaseModel):
temperature: float = Field(description="The temperature in Celsius")
condition: str = Field(description="The weather condition (e.g., sunny, rainy, cloudy)")
humidity: int = Field(description="The humidity percentage")
wind_speed: float = Field(description="The wind speed in km/h")
# Set up the LLM
model = ChatOpenAI(model="gpt-4", temperature=0)
# Create the prompt template
prompt = ChatPromptTemplate.from_template(
"Given the context below, provide a weather forecast for {city} tomorrow, respond in JSON with `temperature`, `condition`, `humidity`, and `wind_speed` keys\n\n{context}"
)
# Apply structured output to the LLM
structured_llm = model.with_structured_output(WeatherForecast, method="json_mode")
# Chain the prompt and structured LLM
weather_chain = prompt | structured_llm
# Generate a weather forecast
result = weather_chain.invoke({"city": "New York", "context": "The weather in New York will be sunny with a chance of rain."})
print(result)
# Output: WeatherForecast(temperature=22.5, condition='Partly cloudy', humidity=65, wind_speed=10.2)
In this example, we've defined a WeatherForecast model using Pydantic, specifying the structure we want for our weather forecast. The prompt template now asks for a weather forecast for a given city. And finally, we use with_structured_output to ensure the LLM's response adheres to the WeatherForecast structure.
Benefits of with_structured_output
Type safety: The Pydantic model ensures that the output matches the expected types.
Integration with LangChain's runnables: This method wraps the LLM call in a runnable, allowing for easy chaining of operations.
Flexibility: You can use various schema types, including TypeDict, JSON schema, or Pydantic classes.
Limitations and Considerations
While powerful, with_structured_output has some limitations to keep in mind:
LLM compatibility: It only works with LLMs that support structured output APIs.
Complexity in advanced scenarios: When dealing with more complex workflows, the use of runnables can introduce additional complexity.
Learning curve: Understanding LangChain's expression language (LCEL) and runnables requires some additional learning.
The PydanticOutputParser Class
The PydanticOutputParser class is another tool in LangChain's arsenal for extracting structured information from LLM outputs.
It's particularly useful when working with LLMs that don't support native structured output features.
It allows you to enforce type safety by ensuring that the output conforms to a predefined Pydantic schema.
This parser works best when you need reliable type validation or when your LLM lacks structured output features.
Film Festival Example
Suppose we want to create a system that extracts structured information about movies from an LLM's output.
from typing import List
from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
# Define our Movie and FilmFestival models
class Movie(BaseModel):
title: str = Field(..., description="The title of the movie")
director: str = Field(..., description="The director of the movie")
runtime: int = Field(..., description="The runtime of the movie in minutes")
class FilmFestival(BaseModel):
movies: List[Movie]
# Set up the parser
parser = PydanticOutputParser(pydantic_object=FilmFestival)
# Create the prompt template
prompt = ChatPromptTemplate.from_messages([
("system", "Answer the user query about movies in the film festival. Wrap the output in `json` format following the schema below\n{format_instructions}"),
("human", "{query}"),
]).partial(format_instructions=parser.get_format_instructions())
# Set up the LLM and chain
llm = ChatOpenAI(model="gpt-4", temperature=0)
chain = prompt | llm | parser
# Generate movie information
query = "Please provide details about the movies 'Inception' directed by Christopher Nolan with a runtime of 148 minutes and 'Parasite' directed by Bong Joon-ho with a runtime of 132 minutes."
result = chain.invoke({"query": query})
print(result)
# Expected output: FilmFestival(movies=[Movie(title='Inception', director='Christopher Nolan', runtime=148), Movie(title='Parasite', director='Bong Joon-ho', runtime=132)])
In this example, we've defined Movie and FilmFestival models using Pydantic.
We've defined Movie and FilmFestival models using Pydantic, specifying the structure we want for our movie information.
The Movie model includes fields for the title, director, and runtime of each movie.
The FilmFestival model contains a list of Movie objects.
The prompt template now asks for details about movies in a film festival.
We use PydanticOutputParser to ensure the LLM's output adheres to our defined structure.
The chain is invoked with a query asking for information about two specific movies.
Benefits of PydanticOutputParser
Strict type enforcement: Ensures that the LLM output matches the expected data types.
Flexible schema definition: Allows for complex nested structures and custom validation logic.
Integration with LangChain's runnable interface: Enables easy incorporation into LangChain workflows.
Limitations and Considerations
While powerful, PydanticOutputParser has some limitations to keep in mind:
Overhead: Requires additional processing to parse the LLM output.
Prompt engineering: Needs careful prompt design to guide the LLM in producing the correct output format.
Learning curve: Requires familiarity with Pydantic for defining schemas.
The StructuredOutputParser Class
The StructuredOutputParser class is a versatile tool in LangChain that allows you to extract structured information from LLM outputs using custom-defined schemas.
It works with diverse models and provides flexibility to define custom output structures through ResponseSchema objects.
This parser is particularly useful when you need to extract information that doesn't fit neatly into pre-built schemas.
Example: Recipe and Ingredient Extractor
Imagine we want to create a system that extracts recipes and ingredient lists from an LLM's output.
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
# Define the response schemas
response_schemas = [
ResponseSchema(name="recipe", description="the recipe for the dish requested by the user"),
ResponseSchema(name="ingredients", description="list of ingredients required for the recipe, should be a detailed list"),
]
# Create the parser
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
# Generate format instructions
format_instructions = output_parser.get_format_instructions()
# Create the prompt template
prompt = PromptTemplate(
template="Provide the recipe for the dish requested.\n{format_instructions}\n{dish}",
input_variables=["dish"],
partial_variables={"format_instructions": format_instructions},
)
# Set up the LLM and chain
model = ChatOpenAI(model="gpt-4-0613", temperature=0)
chain = prompt | model | output_parser
# Generate recipe and ingredients
response = chain.invoke({"dish": "Spaghetti Bolognese"})
print(response)
# Output:
# {
# "recipe": "To make Spaghetti Bolognese, cook minced beef with onions, garlic, tomatoes, and Italian herbs. Simmer until thickened and serve over cooked spaghetti.",
# "ingredients": "Minced beef, onions, garlic, tomatoes, Italian herbs, spaghetti, olive oil, salt, pepper."
# }
In this example, we've defined two ResponseSchema objects for "recipe" and "ingredients".
The StructuredOutputParser ensures that the LLM's output contains these specific fields, allowing us to easily extract and use this information.
Benefits of StructuredOutputParser
Flexibility: Allows for custom-defined output structures tailored to your specific needs.
Wide compatibility: Works with a broad range of LLMs, including those without native structured output support.
Fine-grained control: Enables extraction of specific key-value pairs from the LLM's response.
Limitations and Considerations
While versatile, StructuredOutputParser has some limitations to keep in mind:
Complexity in schema definition: Requires careful definition of
ResponseSchemaobjects.Prompt engineering: Needs thoughtful prompt design to guide the LLM in producing the correct output format.
Less rigid type enforcement: Compared to
PydanticOutputParser, it provides less strict type validation.
Choosing the Right Parser for Your Needs
Now that we've explored the three main methods for parsing structured outputs in LangChain, you might be wondering which one to choose for your specific use case. Here's a quick guide to help you decide:
Use
with_structured_outputwhen:You're working with LLMs that support native structured output features (e.g., OpenAI, Anthropic, Cohere).
You want to define the output schema directly within the prompt.
Type safety and validation are essential.
You're comfortable working with LangChain's runnables.
Choose
PydanticOutputParserwhen:You need strict type safety and validation.
Your LLM might not support structured output features directly.
You're familiar with Pydantic models for schema definition.
You're working with complex, nested data structures.
Opt for
StructuredOutputParserwhen:You require custom and flexible output structures.
Existing parsers don't handle your desired schema.
You need fine-grained control over the extracted information.
You're working with a wide range of LLMs, including those without native structured output support.
Remember, the best choice often depends on your specific use case, the LLMs you're working with, and your familiarity with different schema definition methods.
Best Practices for Parsing Structured Outputs
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Regardless of which parser you choose, here are some best practices to keep in mind when working with structured outputs in LangChain:
Design clear schemas: Clearly define your output structure, considering all possible fields and their types.
Use descriptive field names: Choose field names that are self-explanatory and consistent with your application's terminology.
Provide detailed descriptions: When defining schemas, include clear descriptions for each field to guide the LLM.
Craft effective prompts: Design your prompts carefully to guide the LLM in producing the desired output format.
Handle errors gracefully: Implement error handling to deal with cases where the LLM's output doesn't match the expected structure.
Test thoroughly: Test your parsing logic with a variety of inputs to ensure robustness.
Consider performance: Be mindful of the performance impact of parsing, especially when dealing with large volumes of data.
Keep it simple: Start with simple structures and gradually increase complexity as needed.
Document your schemas: Maintain clear documentation of your output schemas for ease of maintenance and collaboration.
Stay updated: Keep an eye on LangChain updates, as new features and improvements to parsing capabilities are regularly released.
FAQs
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What are output parsers in LangChain?
Output parsers structure and extract specific information from unstructured LLM outputs, ensuring the responses adhere to a predefined schema.
What's the difference between with_structured_output, PydanticOutputParser, and StructuredOutputParser?
with_structured_output works with LLMs that support structured output APIs, using schemas like Pydantic models to guide responses.
PydanticOutputParser enforces type safety for structured outputs using Pydantic schemas.
StructuredOutputParser provides flexibility for custom output structures with user-defined response schemas.
When should I use with_structured_output?
Use it when your LLM natively supports structured output features, and you want to define the output schema directly within the prompt.
When is PydanticOutputParser a good choice?
Choose it when strict type safety and validation are required, especially if the LLM doesn’t support structured output natively.
In what situations should I consider StructuredOutputParser?
Use it when you require a custom output structure or need fine-grained control over the extracted information.
Can I use these output parsers with any LLM?
with_structured_output is limited to compatible LLMs, while PydanticOutputParser and StructuredOutputParser work with a broader range of models.
What are the benefits of using output parsers in LangChain?
They transform unstructured LLM output into organized data, enforce type safety, and provide flexibility in schema definition.
Conclusion
Parsing structured outputs from LLMs is a crucial skill in the world of AI-powered applications.
LangChain's suite of tools – with_structured_output, PydanticOutputParser, and StructuredOutputParser – provide powerful and flexible options for taming the wild outputs of language models.
By mastering these tools, you can transform the raw, unstructured potential of LLMs into structured, actionable data.
This not only enhances the reliability and usability of your AI applications but also opens up new possibilities for complex, data-driven workflows.
Depending on your needs, whether it’s native structured output, type safety, or flexible schema definition, LangChain has the right tool for you.
Start building AI applications that harness the full power of LLMs with confidence and precision today and share your experiences with us on post.
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