Exploring LangChain: The Framework for Building AI Applications

Interview Question

In this article we will be exploring the framework that we can use to integrate AI into
our applications and build AI-powered applications.

AI is being widely used, and it is the future, regardless of the type of application. I’ll
also explain the kinds of applications you can build with AI. Integrating AI has
become a must to streamline tasks that were previously done manually, saving
businesses time and money.

Whether you’re working with any language, framework, or field, you must learn to
integrate AI to secure your career.

LangChain is a framework that lets you develop applications powered by artificial
intelligence and machine learning subsets. It combines large language models with
external components to create LLM-powered applications.

LangChain provides developers with the power and features to integrate AI into their
applications, use large language models, and add external sources. This means you
can integrate AI into your web apps, create AI apps, and more. LangChain provides a
simple interface to interact with pre-trained LLMs from providers like OpenAI,
Cohere, Hugging Face, and others. With LangChain, you can build chatbots, question-
answering systems, and more by integrating LLMs like Hugging Face, GPT, or OpenAI
with data sources and datastores such as vectors. It simplifies the process of creating
generative AI applications and enables context-aware applications by connecting a
language model to sources of context, prompts, instructions, and examples to
ground its responses.

LangChain was launched as an open-source project by co-founders Harrison Chase
and Ankush Gola in 2022. Despite being relatively new, it has grown rapidly due to
the increasing trend of open AI.

You can build applications like chatbots, personal assistants, document
summarization, data analysis, question-answering systems, and more using
LangChain. It has significant potential in sectors like healthcare, e-commerce,
marketing, and more.

LangChain allows you to easily integrate your AI applications into web apps,
regardless of the stack (React, Node, Django, Laravel, Vue, Java, etc.) you’re using, as
long as you’re working with JavaScript or Python.

The Importance of Langchain

When it comes to integrating language models with different data sources
like Google Analytics, Stripe, SQL databases, and various file formats such as
PDFs and CSVs, things can get pretty complex. While it might be easy to
send a request to an API or model for basic tasks, it becomes challenging
when you need these models to do more advanced actions like sending
emails, browsing the web, or running code. This is where Langchain steps in
to offer a solution.

Key Components of Langchain Langchain brings together several key
components to streamline the process. It uses document loaders to pull
data from different sources, text splitters to efficiently break down the data
into manageable chunks, and vector databases to store this data in real-
time. By feeding data into predefined templates and utilizing various tools
to perform actions, Langchain simplifies the interaction between language
models and external data sources.

Modules Offered by Langchain

For those new to Langchain, understanding its seven key modules is

  • Models
  • Prompts
  • Indexes
  • Memory
  • Chains
  • Agents

Models: Language Models and Text Embedding Models

Interview Question (2)

In Langchain, there are two main types of models: language models and text
embedding models. Language models process text, while text embedding
models convert text into numerical arrays for semantic search. Langchain
supports various embedding providers, both free and paid.

Prompts and Output Parsers

Prompts help create inputs for language models, giving control over their
responses. Output parsers structure model outputs for better
interpretation and action.

Indexes and Retrievers

Langchain’s indexes module imports data from different sources and
integrates with vector databases for storage and retrieval. Retrievers
enhance search capabilities within the vector space.

Chains and Agents

Chains simplify interactions with language models, automating tasks and
solving complex problems. Agents provide a deeper level of abstraction.


Langchain makes it easy to create AI applications by combining language
models with other tools. It’s a framework that connects these models with
real-world data, making it simpler to build smart applications. Keep an eye
out for more about Langchain’s features and how it’s used. Thank you for
exploring Langchain with us.

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