guide.txt

Quickstart
Installation
To install LangChain run:

Pip
Conda
pip install langchain

For more details, see our Installation guide.

Environment setup
Using LangChain will usually require integrations with one or more model providers, data stores, APIs, etc. For this example, we'll use OpenAI's model APIs.

First we'll need to install their Python package:

pip install openai

Accessing the API requires an API key, which you can get by creating an account and heading here. Once we have a key we'll want to set it as an environment variable by running:

export OPENAI_API_KEY="..."

If you'd prefer not to set an environment variable you can pass the key in directly via the openai_api_key named parameter when initiating the OpenAI LLM class:

from langchain.llms import OpenAI

llm = OpenAI(openai_api_key="...")

Building an application
Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.

The core building block of LangChain applications is the LLMChain. This combines three things:

LLM: The language model is the core reasoning engine here. In order to work with LangChain, you need to understand the different types of language models and how to work with them.
Prompt Templates: This provides instructions to the language model. This controls what the language model outputs, so understanding how to construct prompts and different prompting strategies is crucial.
Output Parsers: These translate the raw response from the LLM to a more workable format, making it easy to use the output downstream.
In this getting started guide we will cover those three components by themselves, and then cover the LLMChain which combines all of them. Understanding these concepts will set you up well for being able to use and customize LangChain applications. Most LangChain applications allow you to configure the LLM and/or the prompt used, so knowing how to take advantage of this will be a big enabler.

LLMs
There are two types of language models, which in LangChain are called:

LLMs: this is a language model which takes a string as input and returns a string
ChatModels: this is a language model which takes a list of messages as input and returns a message
The input/output for LLMs is simple and easy to understand - a string. But what about ChatModels? The input there is a list of ChatMessages, and the output is a single ChatMessage. A ChatMessage has two required components:

content: This is the content of the message.
role: This is the role of the entity from which the ChatMessage is coming from.
LangChain provides several objects to easily distinguish between different roles:

HumanMessage: A ChatMessage coming from a human/user.
AIMessage: A ChatMessage coming from an AI/assistant.
SystemMessage: A ChatMessage coming from the system.
FunctionMessage: A ChatMessage coming from a function call.
If none of those roles sound right, there is also a ChatMessage class where you can specify the role manually. For more information on how to use these different messages most effectively, see our prompting guide.

LangChain exposes a standard interface for both, but it's useful to understand this difference in order to be construct prompts for a given language model. The standard interface that LangChain exposes has two methods:

predict: Takes in a string, returns a string
predict_messages: Takes in a list of messages, returns a message.
Let's see how to work with these different types of models and these different types of inputs. First, let's import an LLM and a ChatModel.

from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI

llm = OpenAI()
model = ChatOpenAI()

llm.predict("hi!")
>>> "Hi"

model.predict("hi!")
>>> "Hi"

The OpenAI and ChatOpenAI objects are basically just configuration objects. You can initialize them with parameters like temperature and others, and pass them around.

Next, let's use the predict method to run over a string input.

text = "What would be a good company name for a company that makes colorful socks?"

llm.predict(text)
# >> Feetful of Fun

model.predict(text)
# >> Socks O'Color


Finally, let's use the predict_messages method to run over a list of messages.

from langchain.schema import HumanMessage

text = "What would be a good company name for a company that makes colorful socks?"
messages = [HumanMessage(content=text)]

llm.predict_messages(messages)
# >> Feetful of Fun

model.predict_messages(messages)
# >> Socks O'Color


For both these methods, you can also pass in parameters as key word arguments. For example, you could pass in temperature=0 to adjust the temperature that is used from what the object was configured with. Whatever values are passed in during run time will always override what the object was configured with.

Prompt templates
Most LLM applications do not pass user input directly into an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.

In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.

PromptTemplates help with exactly this! They bundle up all the logic for going from user input into a fully formatted prompt. This can start off very simple - for example, a prompt to produce the above string would just be:

from langchain.prompts import PromptTemplate

prompt = PromptTemplate.from_template("What is a good name for a company that makes {product}?")
prompt.format(product="colorful socks")


What is a good name for a company that makes colorful socks?

However, the advantages of using these over raw string formatting are several. You can "partial" out variables - eg you can format only some of the variables at a time. You can compose them together, easily combining different templates into a single prompt. For explanations of these functionalities, see the section on prompts for more detail.

PromptTemplates can also be used to produce a list of messages. In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc) Here, what happens most often is a ChatPromptTemplate is a list of ChatMessageTemplates. Each ChatMessageTemplate contains instructions for how to format that ChatMessage - its role, and then also its content. Let's take a look at this below:

from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)

template = "You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])

chat_prompt.format_messages(input_language="English", output_language="French", text="I love programming.")


[
    SystemMessage(content="You are a helpful assistant that translates English to French.", additional_kwargs={}),
    HumanMessage(content="I love programming.")
]


ChatPromptTemplates can also include other things besides ChatMessageTemplates - see the section on prompts for more detail.

Output Parsers
OutputParsers convert the raw output of an LLM into a format that can be used downstream. There are few main type of OutputParsers, including:

Convert text from LLM -> structured information (eg JSON)
Convert a ChatMessage into just a string
Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string.
For full information on this, see the section on output parsers

In this getting started guide, we will write our own output parser - one that converts a comma separated list into a list.

from langchain.schema import BaseOutputParser
class CommaSeparatedListOutputParser(BaseOutputParser):
    """Parse the output of an LLM call to a comma-separated list."""


    def parse(self, text: str):
        """Parse the output of an LLM call."""
        return text.strip().split(", ")

CommaSeparatedListOutputParser().parse("hi, bye")
# >> ['hi', 'bye']

LLMChain
We can now combine all these into one chain. This chain will take input variables, pass those to a prompt template to create a prompt, pass the prompt to an LLM, and then pass the output through an (optional) output parser. This is a convenient way to bundle up a modular piece of logic. Let's see it in action!

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
)
from langchain.chains import LLMChain
from langchain.schema import BaseOutputParser

class CommaSeparatedListOutputParser(BaseOutputParser):
    """Parse the output of an LLM call to a comma-separated list."""


    def parse(self, text: str):
        """Parse the output of an LLM call."""
        return text.strip().split(", ")

template = """You are a helpful assistant who generates comma separated lists.
A user will pass in a category, and you should generated 5 objects in that category in a comma separated list.
ONLY return a comma separated list, and nothing more."""
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(
    llm=ChatOpenAI(),
    prompt=chat_prompt,
    output_parser=CommaSeparatedListOutputParser()
)
chain.run("colors")
# >> ['red', 'blue', 'green', 'yellow', 'orange']


Next Steps
This is it! We've now gone over how to create the core building block of LangChain applications - the LLMChains. There is a lot more nuance in all these components (LLMs, prompts, output parsers) and a lot more different components to learn about as well. To continue on your journey:

Dive deeper into LLMs, prompts, and output parsers
Learn the other key components
Check out our helpful guides for detailed walkthroughs on particular topics
Explore end-to-end use cases