Langchain: Explained and Getting Started

Introduction

Large Language Models (LLMs) have been a game-changer in the field of natural language processing. With the release of OpenAI's GPT-3 in 2020, these models gained widespread attention and popularity [1]. However, it was not until late 2022 that LLMs truly revolutionized the industry. Major advancements, such as Google's "sentient" LaMDA chatbot and OpenAI's next-generation text embedding model, propelled LLMs into the spotlight [1].

Amidst this wave of progress, Langchain emerged as a powerful framework built around LLMs. Created by Harrison Chase, Langchain aims to empower data engineers with a comprehensive set of tools for leveraging LLMs in various applications, including chatbots, generative question-answering, summarization, and more. In this article, we will delve into the core components of Langchain and explore how it can revolutionize language models for data engineers.

The Core Components of Langchain

Langchain offers a range of components that can be "chained" together to create sophisticated applications around LLMs. These components include:

Prompt Templates

Prompt templates serve as the foundation for structuring input prompts to LLMs. They enable data engineers to format prompts in different ways to obtain diverse results. For instance, in question-answering applications, prompts can be tailored to conventional Q&A formats, bullet lists of answers, or even problem summaries related to the given question.

Creating prompt templates in Langchain is straightforward. The library provides the PromptTemplate class, which allows you to define templates with placeholders for input variables.

Let's take a look at an example:

from langchain import PromptTemplate

template = """
Question: {question}

Answer: 
"""

prompt = PromptTemplate(
    template=template,
    input_variables=['question']
)

In this example, we create a prompt template for a question-answering scenario. The template includes a placeholder {question} that will be replaced with the actual question when generating prompts.

LLMs

Large Language Models, such as GPT-3 and BLOOM, are the core engines behind Langchain's capabilities. These models possess exceptional language processing capabilities and can generate high-quality textual outputs. Langchain allows data engineers to seamlessly integrate various LLMs into their applications. Two popular options are models from the Hugging Face Hub and OpenAI.

Agents

Agents in Langchain leverage LLMs to make intelligent decisions and perform specific actions. These actions can range from simple tasks like web searches to more complex operations involving calculations or data manipulation. By combining LLMs with agents, data engineers can build powerful applications that automate processes and provide valuable insights.

Memory

Langchain also supports short-term and long-term memory, enabling LLMs to retain information across interactions. This feature is particularly useful in chatbot applications, where the model can remember past conversations and provide more contextually relevant responses.

Getting Started with Langchain

Now that we have a basic understanding of the core components of Langchain let's explore how data engineers can get started with this powerful framework.

Installing Langchain

To begin using Langchain, you need to install the langchain library. You can do this by running the following command:

!pip install langchain

Creating Prompt Templates

Prompt templates are the building blocks of Langchain applications. They allow you to structure prompts in different formats to achieve desired outcomes. Let's create a simple prompt template for question-answering:

from langchain import PromptTemplate

template = """
Question: {question}

Answer: 
"""

prompt = PromptTemplate(
    template=template,
    input_variables=['question']
)

In this example, we define a template with a placeholder {question}. This template will be used to generate prompts by replacing the placeholder with the actual user question.

Using Hugging Face Hub LLM

The Hugging Face Hub is a popular platform for accessing pre-trained language models. Langchain seamlessly integrates with the Hugging Face Hub, allowing data engineers to leverage a wide range of models for their applications.

To use a Hugging Face Hub LLM in Langchain, you need to install the huggingface_hub library:

!pip install huggingface_hub

Next, you can initialize the Hugging Face Hub LLM and create an LLM chain using the prompt template:

from langchain import HuggingFaceHub, LLMChain

hub_llm = HuggingFaceHub(repo_id='google/flan-t5-xl', model_kwargs={'temperature': 1e-10})

llm_chain = LLMChain(
    prompt=prompt,
    llm=hub_llm
)

In this example, we initialize a Hugging Face Hub LLM using the google/flan-t5-xl model. We then create an LLM chain by combining the prompt template and the LLM.

To generate text using the Hugging Face Hub LLM, you can simply call the run method on the LLM chain:

question = "Which NFL team won the Super Bowl in the 2010 season?"

print(llm_chain.run(question))

The LLM chain will generate the answer to the question using the Hugging Face Hub LLM.

Using OpenAI LLMs

Langchain also supports OpenAI LLMs, allowing data engineers to harness the power of OpenAI's state-of-the-art language models. To use OpenAI LLMs in Langchain, you need to have an OpenAI account and API key.

To install the openai library, run the following command:

!pip install openai

Next, you can initialize the OpenAI LLM and create an LLM chain similar to the Hugging Face Hub example:

from langchain.llms import OpenAI

davinci = OpenAI(model_name='text-davinci-003')

llm_chain = LLMChain(
    prompt=prompt,
    llm=davinci
)

In this example, we initialize an OpenAI LLM using the text-davinci-003 model. We then create an LLM chain with the prompt template and the OpenAI LLM.

Generating text using the OpenAI LLM is as simple as calling the run method on the LLM chain:

question = "Which NFL team won the Super Bowl in the 2010 season?"

print(llm_chain.run(question))

The LLM chain will generate the answer using the OpenAI LLM.

Advanced Features of Langchain

Langchain offers a range of advanced features that empower data engineers to build sophisticated applications. Some notable features include:

Asking Multiple Questions

Langchain allows you to ask multiple questions and obtain answers in a streamlined manner. You can either iterate through each question using the generate method or combine all questions into a single prompt for more advanced LLMs.

Let's explore both approaches:

Iterating through Questions

questions = [
    {'question': "Which NFL team won the Super Bowl in the 2010 season?"},
    {'question': "If I am 6 ft 4 inches, how tall am I in centimeters?"},
    {'question': "Who was the 12th person on the moon?"},
    {'question': "How many eyes does a blade of grass have?"}
]

results = llm_chain.generate(questions)

In this example, we iterate through each question using the generate method and obtain the corresponding answers. The results variable will contain the generated answers.

Single Prompt for Multiple Questions

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

Questions:
{questions}

Answers:
"""

long_prompt = PromptTemplate(template=multi_template, input_variables=["questions"])

llm_chain = LLMChain(
    prompt=long_prompt,
    llm=flan_t5
)

questions_str = (
    "Which NFL team won the Super Bowl in the 2010 season?\n" +
    "If I am 6 ft 4 inches, how tall am I in centimeters?\n" +
    "Who was the 12th person on the moon?\n" +
    "How many eyes does a blade of grass have?"
)

print(llm_chain.run(questions_str))

In this example, we combine all questions into a single prompt using a multi-question template. The LLM chain will generate answers for each question within the prompt.

Memory for Contextual Responses

Langchain supports short-term and long-term memory, enabling LLMs to retain information across interactions. This feature is particularly useful in chatbot applications, where the model can remember past conversations and provide contextually relevant responses.

By incorporating memory into your Langchain applications, you can create more engaging and interactive experiences for users.

Conclusion

Langchain is a groundbreaking framework that revolutionizes language models for data engineers. By leveraging its core components, including prompt templates, LLMs, agents, and memory, data engineers can build powerful applications that automate processes, provide valuable insights, and enhance productivity.

Whether using LLMs from the Hugging Face Hub or OpenAI, Langchain empowers data engineers to tap into the full potential of these language models. Advanced features like asking multiple questions and incorporating memory further enhance the capabilities of Langchain.

With Langchain, data engineers can unlock the power of language models and transform the way they process and generate text. It is an invaluable tool for any data engineer looking to leverage the latest advancements in natural language processing.

Try Langchain today and experience the transformative impact it can have on your language modeling workflows.

References

[1] OpenAI. "GPT-3 Archived Repo." GitHub, 2020. Link