PydanticOutputParser for typed objects in LangChain - Mini Project: Build & Apply

Using PydanticOutputParser for Typed Objects in LangChain

📖 Scenario: You are building a chatbot that extracts structured information from text using LangChain. You want to parse the output into a typed Python object for easy access and validation.

🎯 Goal: Create a Pydantic model to define the data structure, set up a PydanticOutputParser with this model, parse a sample text output, and finally print the parsed object.

📋 What You'll Learn

Create a Pydantic model called Person with fields name (str) and age (int).

Create a PydanticOutputParser instance using the Person model.

Parse a string containing JSON data representing a person using the parser's parse method.

Print the parsed Person object.

💡 Why This Matters

🌍 Real World

Parsing structured data from language model outputs is common in chatbots, data extraction, and automation tasks.

💼 Career

Understanding how to use typed parsers like PydanticOutputParser helps build reliable applications that handle AI-generated data safely and clearly.

1

Define the Pydantic model

Create a Pydantic model called Person with two fields: name of type str and age of type int. Import BaseModel from pydantic.

LangChain

from pydantic import BaseModel

# Define the Person model here
# Your code here

Need a hint?

Use class Person(BaseModel): and define the fields with type annotations.

2

Create the PydanticOutputParser instance

Import PydanticOutputParser from langchain.output_parsers. Create a variable called parser and assign it to PydanticOutputParser initialized with the Person model.

LangChain

from pydantic import BaseModel

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

# Create the parser instance here
# Your code here

Need a hint?

Use parser = PydanticOutputParser(pydantic_object=Person) to create the parser.

3

Parse a JSON string using the parser

Create a variable called text_output and assign it the string '{"name": "Alice", "age": 30}'. Then create a variable called person_obj and assign it the result of calling parser.parse(text_output).

LangChain

from pydantic import BaseModel
from langchain.output_parsers import PydanticOutputParser

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

parser = PydanticOutputParser(pydantic_object=Person)

# Define text_output and parse it
# Your code here

Need a hint?

Assign the JSON string to text_output and parse it with parser.parse(text_output).

4

Print the parsed Person object

Add a line to print the person_obj variable.

LangChain

from pydantic import BaseModel
from langchain.output_parsers import PydanticOutputParser

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

parser = PydanticOutputParser(pydantic_object=Person)

text_output = '{"name": "Alice", "age": 30}'
person_obj = parser.parse(text_output)

# Print the parsed object
# Your code here

Need a hint?

Use print(person_obj) to display the parsed data.