Parsing Model Output Demo 3

Learn how to validate and convert LLM outputs into Python objects using LangChain’s PydanticOutputParser. In this tutorial, we will:

1. Define a Pydantic model for a movie ticket.
2. Generate prompt templates including format instructions.
3. Inspect the complete prompt JSON schema.
4. Call the LLM and get a structured response.
5. Parse the response into a Ticket instance.

1. Imports & Model Definition

Begin by importing the required modules and defining a Pydantic model for a movie ticket:

from typing import List
from langchain_openai import OpenAI
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain.prompts import PromptTemplate
from langchain.output_parsers import PydanticOutputParser

model = OpenAI()

class Ticket(BaseModel):
    """Schema for a movie ticket reservation."""
    date:    str = Field(description="show date")
    time:    str = Field(description="show time")
    theater: str = Field(description="theater name")
    count:   int = Field(description="number of tickets")
    movie:   str = Field(description="preferred movie")

Ticket Model Fields

2. Initialize the Parser & Build the Prompt

Create a PydanticOutputParser from the Ticket model. Then define a PromptTemplate that embeds the JSON schema instructions:

# Initialize the parser
parser = PydanticOutputParser(pydantic_object=Ticket)

# Template with a placeholder for {query} and format instructions
ticket_template = """\
Book us a movie ticket for two this Friday at 6:00 PM.
Choose any theater. Send the confirmation by email.
Our preferred movie is: {query}

Format instructions:
{format_instructions}
"""

prompt = PromptTemplate(
    template=ticket_template,
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()},
)

3. Inspect the Full Prompt

Format the prompt for the movie “Interstellar” and print it:

formatted_prompt = prompt.format_prompt(query="Interstellar")
print(formatted_prompt.to_string())

Example output:

Book us a movie ticket for two this Friday at 6:00 PM.
Choose any theater. Send the confirmation by email.
Our preferred movie is: Interstellar

Format instructions:
The output should be formatted as a JSON instance that conforms to the JSON Schema below.

As an example, for the schema
{"properties": {"foo": {"title":"Foo","description":"a list of strings","type":"array","items":{"type":"string"}}},"required":["foo"]}
the object {"foo":["bar","baz"]} is valid.
Here is the output schema:

{"properties":{
  "date":    {"title":"Date","description":"show date","type":"string"},
  "time":    {"title":"Time","description":"show time","type":"string"},
  "theater": {"title":"Theater","description":"theater name","type":"string"},
  "count":   {"title":"Count","description":"number of tickets","type":"integer"},
  "movie":   {"title":"Movie","description":"preferred movie","type":"string"}
},"required":["date","time","theater","count","movie"]}

4. Invoke the LLM

Send the formatted prompt to the model and capture its raw response:

llm_output = model.invoke(formatted_prompt.to_string())
print("LLM Response:\n", llm_output)

Example Raw Response

{
  "date": "Friday",
  "time": "6:00 PM",
  "theater": "AMC Lincoln Square",
  "count": 2,
  "movie": "Interstellar"
}

5. Parse into a Python Object

Finally, hand the LLM output back to PydanticOutputParser for validation and conversion:

reservation = parser.parse(llm_output)
print("Reservation object:", reservation)
print("Type:", type(reservation))

Output:

Reservation object: Ticket(date='Friday', time='6:00 PM', theater='AMC Lincoln Square', count=2, movie='Interstellar')
Type: <class 'Ticket'>

Conclusion

By leveraging PydanticOutputParser, you can:

• Instruct the LLM to generate schema-compliant JSON.
• Automatically validate and convert responses into Python objects.

This method ensures structured, reliable LLM outputs in your applications.

Links and References

• LangChain Output Parsers: https://langchain.com/docs/modules/output_parsers
• Pydantic Documentation: https://docs.pydantic.dev/
• JSON Schema Standard: https://json-schema.org/