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Build a Simple LLM Application

In this quickstart we'll show you how to build a simple LLM application with LangChain. This application will translate text from English into another language. This is a relatively simple LLM application - it's just a single LLM call plus some prompting. Still, this is a great way to get started with LangChain - a lot of features can be built with just some prompting and an LLM call!

After reading this tutorial, you'll have a high level overview of:

Let's dive in!

Setup​

Jupyter Notebook​

This and other tutorials are perhaps most conveniently run in a Jupyter notebooks. Going through guides in an interactive environment is a great way to better understand them. See here for instructions on how to install.

Installation​

To install LangChain run:

pip install langchain

For more details, see our Installation guide.

LangSmith​

Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. The best way to do this is with LangSmith.

After you sign up at the link above, make sure to set your environment variables to start logging traces:

export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_API_KEY="..."

Or, if in a notebook, you can set them with:

import getpass
import os

os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

Using Language Models​

First up, let's learn how to use a language model by itself. LangChain supports many different language models that you can use interchangeably. For details on getting started with a specific model, refer to supported integrations.

pip install -qU langchain-openai
import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o-mini")

Let's first use the model directly. ChatModels are instances of LangChain Runnables, which means they expose a standard interface for interacting with them. To simply call the model, we can pass in a list of messages to the .invoke method.

from langchain_core.messages import HumanMessage, SystemMessage

messages = [
    SystemMessage(content="Translate the following from English into Italian"),
    HumanMessage(content="hi!"),
]

model.invoke(messages)
API Reference:HumanMessage | SystemMessage
AIMessage(content='Ciao!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 3, 'prompt_tokens': 20, 'total_tokens': 23, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_9ee9e968ea', 'finish_reason': 'stop', 'logprobs': None}, id='run-ad371806-6082-45c3-b6fa-e44622848ab2-0', usage_metadata={'input_tokens': 20, 'output_tokens': 3, 'total_tokens': 23, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})

If we've enabled LangSmith, we can see that this run is logged to LangSmith, and can see the LangSmith trace. The LangSmith trace reports token usage information, latency, standard model parameters (such as temperature), and other information.

Note that ChatModels receive message objects as input and generate message objects as output. In addition to text content, message objects convey conversational roles and hold important data, such as tool calls and token usage counts.

Prompt Templates​

Right now we are passing a list of messages directly into the language model. Where does this list of messages come from? Usually, it is constructed from a combination of user input and application logic. This application logic usually takes the raw user input and transforms it into a list of messages ready to pass to the language model. Common transformations include adding a system message or formatting a template with the user input.

Prompt templates are a concept in LangChain designed to assist with this transformation. They take in raw user input and return data (a prompt) that is ready to pass into a language model.

Let's create a prompt template here. It will take in two user variables:

  • language: The language to translate text into
  • text: The text to translate
from langchain_core.prompts import ChatPromptTemplate

system_template = "Translate the following from English into {language}"

prompt_template = ChatPromptTemplate.from_messages(
    [("system", system_template), ("user", "{text}")]
)
API Reference:ChatPromptTemplate

Note that ChatPromptTemplate supports multiple message roles in a single template. We format the language parameter into the system message, and the user text into a user message.

The input to this prompt template is a dictionary. We can play around with this prompt template by itself to see what it does by itself

result = prompt_template.invoke({"language": "Italian", "text": "hi!"})

result
ChatPromptValue(messages=[SystemMessage(content='Translate the following from English into Italian', additional_kwargs={}, response_metadata={}), HumanMessage(content='hi!', additional_kwargs={}, response_metadata={})])

We can see that it returns a ChatPromptValue that consists of two messages. If we want to access the messages directly we do:

result.to_messages()
[SystemMessage(content='Translate the following from English into Italian', additional_kwargs={}, response_metadata={}),
 HumanMessage(content='hi!', additional_kwargs={}, response_metadata={})]

Chaining together components with LCEL​

We can now combine this with the model from above using the pipe (|) operator:

chain = prompt_template | model
response = chain.invoke({"language": "Italian", "text": "hi!"})
print(response.content)
Ciao!
tip

Message content can contain both text and content blocks with additional structure. See this guide for more information.

This is a simple example of using LangChain Expression Language (LCEL) to chain together LangChain modules. There are several benefits to this approach, including optimized streaming and tracing support.

If we take a look at the LangSmith trace, we can see both components show up.

Conclusion​

That's it! In this tutorial you've learned how to create your first simple LLM application. You've learned how to work with language models, how to create a prompt template, and how to get great observability into chains you create with LangSmith.

This just scratches the surface of what you will want to learn to become a proficient AI Engineer. Luckily - we've got a lot of other resources!

For further reading on the core concepts of LangChain, we've got detailed Conceptual Guides.

If you have more specific questions on these concepts, check out the following sections of the how-to guides:

And the LangSmith docs:

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