LangChain: A One-Stop Framework Building Applications with LLMs

Introduction

Large Language Models (LLMs) have been gaining popularity for the past few years. With the entry of OpenAI’s ChatGPT, there was a massive popularity gain in the Industry towards these LLMs. These LLMs are being worked upon to create different applications from question-answering chatbots to text generators to conversational bots and much more. Many new APIs are being created and each has its own way of training and testing our own data to it. This is where LangChain fits in. LangChain is a Python framework/library for developing applications powered with these LLMs. In this article, we will explore LangChain in detail, including chains, prompts, and agents, and also learn to build applications using it.

Learning Objectives:

• To understand the LangChain Framework.
• To create applications with LangChain.
• To understand the components involved in LLM building.
• To build chains through LangChain for language models.
• To understand agents and prompts in LangChain.

This article was published as a part of the Data Science Blogathon.

What is LangChain? Why is it Necessary?

LangChain is a Python Library for building applications powered by LLMs. It not only takes care of connecting to different LLMs through APIs but even makes these LLMs connect to a data source and even makes them aware of their environment and interact with it. LangChain is also used to make RAG (retrieval augmented generation) applications. So how does it fit in? The thing is these large language models on their own in isolation may not be that powerful. Thus with LangChain, we can connect them with external data sources and computation, which will greatly help them to come up with good answers.

LangChain thus makes it possible for us to make the language models connect to our very own database and create applications around them, allowing them to reference the data. With LangChain, not only you can create language models to create question-answer bots based on your data provided, but even make the language models take certain actions based on questions thus making them data-aware, agentic, and language-understanding. We will be looking into these actions further down in the article.

LangChain framework contains components that constitute LLM wrappers, which are wrappers for popular language model APIs, from OpenAI, Hugging Face, etc. It even includes the prompt templates for creating our own prompts. LangChain, the name itself has the word chain, thus making it possible to chain multiple components together. And finally, agents, which we have talked about before, that allow the model to interact with external data and computations.

Installing LangChain

Like all other Python libraries, LangChain can be installed through Python’s pip command. The command for this is:

pip install -qU langchain

This will download and install the latest stable version of the LangChain framework in Python. The LangChain framework comes with many LLM wrappers, ChatBot Wrappers, Chat Schemas, and Prompt-Templates

Apart from LangChain Packages, we need to even install the following packages, which we will be working on in this article

pip install ai21
pip install -qU huggingface_hub
pip install -qU openai

This will install the hugging face hub, where we will be able to work with hugging face APIs. OpenAI is installed, thus we can work with GPT models, and one more LLM model we have installed is the ai21, which is the Studio AI21 Language Model. We will be working with these 3 models, writing examples, and understanding how LangChain fits in.

Building Applications with LLMs Wrapper

LangChain provides wrappers to different models from LLMs to chat models to text embedding models. Large language models are the ones that take the text for the input and the output returned is a text string. In this section, we will take a look at the LangChain wrappers i.e. the standard interface it provides for different LLMs like Hugging Face, OpenAI, Studio AI21, etc.

Let’s start by using the OpenAI model in LangChain. The respective code will be:

# importing OpenAI Wrapper from LangChain
from langchain.llms import OpenAI

# provide your API KEY here
os.environ["OPENAI_API_KEY"] = 'Your OpenAI API KEY'

# initializing OpenAI LLM
llm = OpenAI(model_name="text-ada-001")

# query
query = 'Tell me a joke'

# model output
print(llm(query))

Here we import the OpenAI Wrapper from LangChain. Then we create an Instance of it named LLM and provide input, the model name we want to use. Now to this llm variable, we directly pass the query to get an output. Running this code has given the output:

We see that the model gives out a joke based on the query provided. Now let’s try this with the Hugging Face Model.

# importing Hugging Face Wrapper from LangChain
from langchain import HuggingFaceHub

# provide your API KEY here
os.environ['HUGGINGFACEHUB_API_TOKEN'] = 'Your Hugging Face API Token'

# initialize Hugging Face LLM
flan_t5_model = HuggingFaceHub(
    repo_id="google/flan-t5-xxl",
    model_kwargs={"temperature":1e-1}
)

query1 = "Who was the first person to go to Space?"
query2 = "What is 2 + 2 equals to?"

generate = flan_t5_model.generate([query1, query2])
print(generate.generations)

We have followed the same process that we have followed with the OpenAI model. Here we are working with the flan-t5 model and have set the temperature to 1e-1. The only difference is that here we use the generate() method of the LLM model to pass the queries. This generate() method is used when you want to pass multiple queries at the same time. These are passed in the form of a list, which we have done so in the above. To get the model output, we then call the generations function. The resulting output we get after we run the code is:

We see 2 answers from the model. The first is Yuri Gagarin, who was indeed the first person to enter the space and the second answer is 4, which is right. Finally, we will look at another LLM wrapper, this time for the AI21 Studio which provides us with the API access to Jurrasic – 2 LLM. Let’s look at the code:

# importing AI21 Studio Wrapper from LangChain
from langchain.llms import AI21

# provide your API KEY here
os.environ['AI21_API_KEY'] = 'Your API KEY'

# initializing OpenAI LLM
llm = AI21()

# query
query = 'Tell me a joke on Cars'

# model output
print(llm(query))

Again, we see that the overall code implementation is almost similar to the OpenAI example that we have seen earlier. The only difference is the API Key and the LLM wrapper that is being imported which is the AI21. Here we have given a query to tell a joke on cars, let’s see how it performs

The AI21 really did a fab job with the answer it provided. So in this section, we have learned, how to use different LLM wrappers from LangChain for working with different language models. Apart from these 3 wrappers that we have seen, LangChain provides many more wrappers to many other language models out there.

Prompt Templates

Prompts are a key part when developing applications with LLMs. Initially, one would have to retrain the entire model or have to work with completely a different model to do different tasks, like one model for translation, and one model for summarization. But the entry of prompt templates has changed it all. With prompt templates, we can make the language model do anything from translation to question answering, to text generation and summarization of different data.

We will now take a look into prompt templates in LangChain with the OpenAI models. Let’s begin by creating a template and then giving it to LangChain’s PromptTemplate class.

from langchain import PromptTemplate

# creating a Prompt Template
template = """The following is a conversation between a time traveler and a 
historian. The time traveler is from the future and tends to make humorous 
comparisons between past and future events:

Historian: {query}

Time Traveler: """

# assigning the template to the PromptTemplate Class
prompt = PromptTemplate(
    input_variables=["query"],
    template=template
)

The first step is to write a template. Here we have written a template stating that we want the AI to act like a funny time traveler, that is we have set a context for the AI, and how it should be acting. Then we provide the query considering we are a historian. The query is present in {} because we want that part to be replaced by the question we want to ask. Then this template is given to the PromptTemplate class from LangChain. Here we pass the template to the template variable, and we then tell the input variable as “query” (the input variable is the place where our questions sit).

Let’s try to create a question and observe the prompt that will be generated.

# printing an example Prompt
print(prompt.format(query='Are there flying cars?'))

Here in the output, we see that {query} is replaced by the question that we have given to the “query” variable that was given to the format() function of the Prompt Instance. So the PromptTemplate does the job of formatting our query before we can send it to the model. Now, we have successfully created a prompt template. We will now test this template with an OpenAI model. The following will be the code:

# creating the llm wrapper
llm = OpenAI(model_name="text-davinci-003",temperature=1)

# model's output to the query
print(
    llm(prompt.format(
        query='Are there flying cars?')
      )
    )

Here again, we are working with the LLM wrapper from langchain for OpenAI language model. We are then giving the prompt directly to the LLM, just like how we have passed in the beginning code example. This will then print the output generated by the model, which is:

Output

Ha! No way, although I did see a farmer the other day using a robotic scarecrow to shoo away birds from his cornfield. Does that count?

Seeing the output, we can say that the Language model did indeed act like a time traveler. Prompt templates can be provided with multiple queries. Let’s try to create a template to provide multiple inputs to the Language Model

multi_query_template = """Answer the following questions one at a time.

Questions:
{questions}

Answers:
"""
long_prompt = PromptTemplate(
    template=multi_query_template,
    input_variables=["questions"]
)

qs = [
    "Which IPL team won the IPL in the 2016 season?",
    "How many Kilometers is 20 Miles?",
    "How many legs does a lemon leaf have?"
]

print(
    llm(prompt.format(
        query=qs)
      )
    )

The above depicts the code for multiple query templates. Here in the prompt template, we have written at the start, that the questions must be answered one at a time. Then we provide this template to the PromptTemplate class. Store all the questions in a list called qs. Just like before, we pass this qs list to the query variable in the prompt.format() function and give it to the model. Now let’s check the output produced by the language model

We see that the OpenAI language model has given all the right output answers one by one. So far, what we have done with Prompt Template is just a glimpse. We can do much more using the prompt components from the LangChain framework. One example is we can perform Few Shot Learning by providing some examples in the context of the Prompt Template itself.

LangChain – Chaining Components Together

The LangChain, the name itself has the word Chain in it. In this section, we will look at the Chain Module of LangChain. When developing applications, using Language Models in isolation is fine, i.e. for small applications, but when creating complex ones, it’s better to chain LLMs, i.e. either chaining two similar LLMs or two different ones. LangChain provides a Standard Interface for chaining different Language Models together. Chains can be used to connect different Components like LLMs and even Prompts together to perform a particular action

Example

Let’s start by taking a simple example of chaining an OpenAI Language Model with a Prompt.

from langchain.llms import OpenAI
from langchain import PromptTemplate, LLMChain

# creating a Prompt Template
template = """The following is a conversation between a human and an AI 
Assistant. Whatever the human asks to explain, the AI assistant
explains it with humour and by taking banana as an example

Human: {query}

AI: """

# assigning the template to the PromptTemplate Class
prompt = PromptTemplate(
    input_variables=["query"],
    template=template
)

# query
query = "Explain Machine Learning?"

# creating an llm chain
llm = OpenAI(model_name="text-davinci-003",temperature=1)
llm_chain = LLMChain(prompt=prompt, llm=llm)

# model output
print(llm_chain.run(query))

• Create a template, saying that every question asked by the user is answered by the AI in a funny way, and take the example of a banana.
• Then this template passed to the PromptTemplate() function thus creating a Prompt Template out of it.
• Then with the OpenAI LLM wrapper, we initialize the OpenAI model.
• We have imported the LLMChain from LangChain. LLMChain is one of the Simplest Chains provided by LangChain. To this, we pass the Prompt Template and the Language Model.
• Finally, we pass the query directly to the llm_chain’s run() function.

What LLMChain does is, first it sends the input query to the first element in the chain, i.e. to the Prompt, i.e. the PromptTemplate. Here the input gets formatted to a particular Prompt. This formatted Prompt is then passed to the next element in the chain, i.e. the language model. So a chain can be considered like a Pipeline. Let’s see the output generated.

Output:

Machine Learning is like a banana. You keep giving it data and it slowly develops the skills it needs to make more intelligent decisions. It’s like a banana growing from a little green fruit to a fully ripe and delicious one. With Machine Learning, the more data you give it, the smarter it gets!

Here we do get an answer to the question we have asked. The answer generated is both funny and even contains the banana example in it. So we might get a question now, can two chains be chained together? Well, the answer is absolutely YES. In the next example, we will be doing exactly the same. We will create two chains with two models, then we will chain them both together. The code for this is

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chains import SimpleSequentialChain

# creating the first template
first_template = """Given the IPL Team Name, tell the Year in which they first
won the trophy.

% IPL TEAM NAME
{team_name}

YOUR RESPONSE:
"""
team_template = PromptTemplate(input_variables=["team_name"], template=first_template)

# creating the team_chain that holds the year informatino
team_chain = LLMChain(llm=llm, prompt=team_template)

# creating the second Template
second_template = """Given the Year, name the Highest Scoring Batsman in the IPL for that Year.
% YEAR
{year}

YOUR RESPONSE:
"""
batsman_template = PromptTemplate(input_variables=["year"], template=second_template)

# creating the batsman_chain that holds the bastman information
batsman_chain = LLMChain(llm=llm, prompt=batsman_template)

# combining two LLMChains
final_chain = SimpleSequentialChain(chains=[team_chain, batsman_chain], verbose=True)

# checking the chain output
final_output = final_chain.run("Sunrisers Hyderabad")

• Firstly, we have created two templates, the template one asks the model for the year in which a particular Cricket Team won, and the second template, given the year, tells the highest-scoring batsman.
• After creating the templates, then we create the PromptTemplates for both of them.
• Then we create our first chain, the team_chain, which contains our OpenAI Language Model, which we have defined in the first code and the first template. The input to our first template is the TEAM NAME.
• Then we create the second chain. This chain takes the same model but the template given is the second template. This chain takes in the YEAR and gives the highest-scoring IPL batsman in that year.
• Finally, we then combine these two chains with the SimpleSequentialChain() function and store it in the final_chain variable. Here we pass the chains in the form of a list. It’s important to make sure that the chains are stored in the list in the same order that they need to be. We even set the verbose to True, so we can better understand the output.

Now we have run the code by giving Sunrisers Hyderabad for the input to our final chain. The output returned was:

The output produced is indeed True. So how did it do it? Firstly, the input Sunrisers Hyderabad is fed to the first chain i.e. the team_chain. The output of the team_chain is in line 1. So it returned the output. i.e. the year 2016. The team_chain generates an output, which serves as the input for the second chain, the batsman_chain. The batsman_chain takes the input of the Year, specifically 2016, and produces the name of the highest-scoring IPL batsman in that year. The second line displays the output of this chain. So this is how chaining/combination of two or more chains work in particular.

Again, we have just looked at only some of the Chaining concepts in LangChain. Developers can work with the Chains to create various applications. They can also categorize the chains themselves, although this topic is beyond the scope of this article.

Agents in LangChain

Despite their immense power, Large Language Models often lack basic functionalities such as logic and calculation. They can struggle with simple calculations that even small calculator programs can handle more effectively.

Agents, on the other hand, have access to tools and toolkits that enable them to perform specific actions. For instance, the Python Agent utilizes the PythonREPLTool to execute Python commands. The Large Language Model provides instructions to the agent on what code to run.

Example

from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import PythonREPLTool
from langchain.python import PythonREPL
from langchain.llms.openai import OpenAI

# creating Python agent
agent_executor = create_python_agent(
    llm=OpenAI(temperature=0, max_tokens=1000),
    tool=PythonREPLTool(),
    verbose=True
)

agent_executor.run("What is 1.12 raised to the power 1.19?")

Here to create the Python agent, we use the create_python_agent() object from the langchain. To this, we pass our OpenAI Language Model. The tool we will work with is the PythonREPLTool() which is capable of running Python code. To get a detailed output, we set the verbose to True. Finally, we run the model with the input to find the power of 1.12 raised to 1.19. The output generated is:

In this process, the language model generates Python code, which is then executed by the agent using the PythonREPLTool(). The resulting answer is returned by the language model. Agents go beyond code execution and can also search Google for answers when the language model fails. These powerful components in the LangChain enable the creation of complex models with high accuracy.

Conclusion

LangChain is a recently developed Python framework designed for constructing applications with robust language models. LangChain offers a standardized interface to interact with multiple language models. Its components, such as prompts, chains, and agents, enable the creation of powerful applications.

We’ve explored modifying prompts to elicit various outputs from language models and chaining different models together. Additionally, we’ve worked with agents capable of executing Python code. LangChain provides coherent output, filling a gap that LLMs alone cannot. This integration facilitates automation, streamlining processes and tasks within applications.

Key Takeaways:

• The support for different models in LangChain makes it easy to work with different Language Models.
• Multiple queries can be fed into the language models by changing the prompt template.
• You can combine two or more language models together using chain components to get accurate answers.
• With LangChain, the time to change from one language model to another is greatly reduced.

Frequently Asked Questions

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Ajay

I work as a Developer in the field of Data Science. I constantly spend time learning new things be it related to AI, DataSceine, and CyberSecurity. Deep learning and machine learning are two topics that I find particularly fascinating, and Python is my preferred language for programming. Cyber Security is another field that I'm touching upon recently. I have experience with large-scale data analysis, and I have a solid grasp of a variety of deep learning and machine learning approaches, including neural networks, regression models, and natural language processing. I'm eager to take on new challenges and make a meaningful contribution to the industry, so I'm constantly seeking for ways to enlarge and deepen my knowledge and skills in the subject.