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In this guide, you’ll learn how to use configurable fields in LangChain to override default runtime parameters—ideal for switching between OpenAI models (e.g., GPT-3.5 and GPT-4) based on your prompt’s complexity or cost requirements.

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Defining a Configurable Model

Begin by importing the required classes and creating a ChatOpenAI instance where model_name is exposed as a configurable field. By default, this example uses GPT-3.5 Turbo. 
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.runnables import ConfigurableField

# Initialize ChatOpenAI with a configurable `model_name`
model = ChatOpenAI(model_name="gpt-3.5-turbo").configurable_fields(
    model_name=ConfigurableField(
        id="model_name",
        name="Model Name",
        description="Select the OpenAI chat model to use",
    )
)
If you don’t override model_name, the chain will always use gpt-3.5-turbo by default.

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Building and Invoking the Chain

Next, define a simple haiku prompt template and compose it with your configurable model. Invoking the chain without further configuration uses the default settings. 
# Create a haiku prompt template
prompt = PromptTemplate.from_template("Write a Haiku on {subject}")

# Chain the prompt with the model
chain = prompt | model

# Invoke using default model (gpt-3.5-turbo)
response = chain.invoke({"subject": "cat"})
print(response)
Example response: 
AIMessage(
  content='Whiskers soft and fine
Purring gently in the sun
Graceful feline friend',
  response_metadata={
    'token_usage': {'completion_tokens': 19, 'prompt_tokens': 13, 'total_tokens': 32},
    'model_name': 'gpt-3.5-turbo',
    'finish_reason': 'stop'
  }
)

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Overriding the Model at Runtime

To leverage a more advanced model like GPT-4 for complex prompts, call .with_config() with a dictionary matching your configurable field’s key: 
# Override to use GPT-4
response_gpt4 = chain.with_config(configurable={"model_name": "gpt-4"}).invoke(
    {"subject": "cat"}
)
print(response_gpt4)
Example response: 
AIMessage(
  content="Soft purr in the night,
Eyes gleaming in moon's soft light,
Cat in calm delight.",
  response_metadata={
    'token_usage': {'completion_tokens': 22, 'prompt_tokens': 13, 'total_tokens': 35},
    'model_name': 'gpt-4',
    'finish_reason': 'stop'
  }
)
Switching to higher-capability models (like gpt-4) may increase your API costs. Monitor usage via your OpenAI dashboard.

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Benefits of Configurable Fields

Configurable fields provide a flexible, cost-effective way to manage runtime parameters:
BenefitDescriptionExample
Cost OptimizationDefault to a less expensive model, upgrade only when needed.Start with GPT-3.5, switch to GPT-4 selectively
Developer FlexibilityLet users or downstream code adjust parameters without redeploy.Expose temperature, max_tokens, or model_name
Seamless IntegrationCombine with memory, callbacks, and dynamic prompts for workflows.Multi-turn chatbots with user preferences

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Further Reading

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