Parsing Model Output Demo 1

In this guide, you’ll learn how to convert large language model (LLM) responses into native Python structures using LangChain’s output parsers. We’ll demonstrate:

• Turning a numbered list into a comma-separated string
• Parsing that string into a Python list

This approach makes LLM outputs easy to manipulate in your codebase.

Table of Contents

1. Prerequisites
2. Initializing LangChain and OpenAI
3. Generating an Unstructured List
4. Formatting as CSV with CommaSeparatedListOutputParser
5. Parsing the CSV into a Python List
6. Next Steps
7. References

Prerequisites

• Python 3.7+
• An OpenAI API key
• langchain installed

pip install langchain langchain-openai

Initializing LangChain and OpenAI

Import the core components:

from langchain_openai import OpenAI
from langchain.prompts import PromptTemplate
from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.output_parsers.list import ListOutputParser

Create the OpenAI LLM client:

llm = OpenAI()

Generating an Unstructured List

Define a simple prompt template without format constraints:

prompt = PromptTemplate(
    template="List 3 {things}.",
    input_variables=["things"]
)

Invoke the model to list three World Cup cricket teams:

response = llm.invoke(input=prompt.format(things="countries that play cricket in the World Cup"))
print(response)

Output might look like:

1. India
2. Australia
3. England

Formatting as CSV with CommaSeparatedListOutputParser

1. Instantiate the CSV parser and retrieve its instructions:

   output_parser = CommaSeparatedListOutputParser()
   format_instructions = output_parser.get_format_instructions()
   print(format_instructions)
   

   Expected instruction:

   Your response should be a list of comma separated values, eg: `foo, bar, baz`
   

2. Embed these instructions into a new prompt template:

   prompt_with_format = PromptTemplate(
       template="List 3 {things}.\n{format_instructions}",
       input_variables=["things"],
       partial_variables={"format_instructions": format_instructions}
   )
   

3. Invoke the LLM with the CSV constraint:

   final_prompt = prompt_with_format.format(things="countries that play cricket in the World Cup")
   output = llm.invoke(input=final_prompt).strip()
   print(output)
   

   Now the model returns:

   India, Australia, England
   

Parsing the CSV into a Python List

Convert the raw string into a Python list:

# Before parsing
print(type(output))  # <class 'str'>

# Parse into list
things = output_parser.parse(output)
print(things)        # ['India', 'Australia', 'England']
print(type(things))  # <class 'list'>

Now you can work with things directly in your application.

Next Steps

Explore more advanced parsers for structured outputs:

You can также try the JSONOutputParser to extract richer data types from LLM responses.

References

• LangChain Output Parsers
• PromptTemplate Documentation
• OpenAI LLM Client
• CommaSeparatedListOutputParser Source