4. Pydantic Output Parser

from pydantic import BaseModel, Field

class PersonProfile(BaseModel):
    name: str = Field(description="The full name of the person")
    occupation: str = Field(description="The primary job/occupation of the person")
    skills: list[str] = Field(description="A list of 3-5 core professional skills")
    years_of_experience: int = Field(description="Estimated years of professional experience in their field")

from dotenv import load_dotenv
from langchain.prompts import ChatPromptTemplate
from langchain.output_parsers import PydanticOutputParser
from langchain.chat_models import init_chat_model

load_dotenv()

# Initialize the Groq model using core abstractions
model = init_chat_model("llama-3.3-70b-versatile", model_provider="groq")

# Instantiate the parser with the schema
pydantic_parser = PydanticOutputParser(pydantic_object=PersonProfile)

# Retrieve the formatting instructions
format_instructions = pydantic_parser.get_format_instructions()

# Create prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", "Generate information based on the input name. {format_instructions}"),
    ("human", "Generate a profile for 'Ada Lovelace'.")
])

# 1. Format the prompt
messages = prompt.format_messages(format_instructions=format_instructions)

# 2. Invoke the model to get response content
response = model.invoke(messages)

# 3. Parse the response content using the parser
result = pydantic_parser.parse(response.content)

# The result is parsed directly into a Pydantic object
print(f"Name: {result.name}")
print(f"Occupation: {result.occupation}")
print(f"Skills: {result.skills}")
print(f"Years of Experience: {result.years_of_experience}")