This first lecture will be an introduction to LangChain.
You'll learn what it is, its use cases, and its components.
After this theoretical lecture, we'll move on to coding.
What is LangChain?
LangChain is an open -source framework
that allows developers working with AI to combine large language models or LLMs
like GPT -4 with external sources of computation and data.
The framework is currently offered as a Python or a JavaScript package and is
under heavy development.
It is used to build complete apps using
LLMs very quickly.
Using LLMs is often insufficient for
creating truly powerful apps.
This is because LLMs are often limited in
their ability to understand the context, interact with the real world, or learn
and adapt.
To understand what needs LangChain fills,
let's look at a practical example.
We all know that LLMs, such as ChatGPT
and GPT -4, have an impressive general knowledge.
We can ask them about almost anything and
get a pretty good answer.
But what if you want to know something
specific from your own data, your own documents?
It could be a book, a PDF file, or a
database with proprietary information.
LangChain allows you to connect a large
language model like GPT -4 to your own sources of data.
This is what data -aware means.
And LangChain is data -aware.
Of course, you can give the information to the AI model as input into the prompt.
But I'm talking about something else.
I'm talking about referencing an entire
database field with your own data.
Using LangChain, you can make GPT -4 work
with your own data, and it can also take the action you'd like.
For instance, search the web, fill out a form, or send an email with some specific information.
This is what agentic -aware means.
So, LangChain is data -aware and agentic -aware.
The example I've described to you works like this.
You take the document you want your language model to reference and slice it
up into smaller chunks.
Store those chunks in a vector database
as embeddings, which are numeric representations of the text.
In fact, these capabilities open up an infinite number of practical use cases.
Just imagine the possibilities.
You can have a large language model that
references the Constitution or the Civil Code of your own country.
You can ask the application anything about the Constitution or the laws you
are interested in.
And the application will give you the
correct answer.
It's like having a specialized lawyer in
front of you that can be asked anything for free.
This will open up a world of new opportunities.
I believe we are going to see exponential
progress in data analytics and data science.
Our ability to connect large language
models to advanced APIs is really going to take things to the next level.
LangChain implements abstractions like chains and agents so that a developer
doesn't have to know what happens under the hood.
Let's go over some specific examples of applications that can be created using LangChain.
Chatbots – question -answering systems or summarization tools.
LangChain can be used to create chatbots that can interact with users in a natural way.
For example, you could use LangChain to create a chatbot that can answer customer
questions or provide support based on the internal documentation of the company.
LangChain can be used to create question -answering systems that can answer
questions in a comprehensive and informative way.
For example, you could use LangChain to create a question -answering system that
can answer questions about a particular product, service, or topic such as
history, science, or current events.
LangChain can be used to create
summarization tools that can summarize long pieces of text in a concise and
informative way.
For example, you could use LangChain to
create a summarization tool that can summarize documents, news articles,
research papers, or even books.
These are only a few of the applications
that can be built using LangChain.
When learning LangChain, you will
actually learn about three main concepts.
LangChain components, chains, and agents.
Let's see the LangChain components.
First off, we have the LLM wrappers that
allow us to connect to and use large language models like GPT -4 or those from
the Hugging Face Hub.
Next, there are the prompt templates that
allow us to create dynamic prompts which are the input to the LLMs.
Indexes allow us to extract relevant information for the LLMs.
And memory is the concept of storing and retrieving data in the process of a conversation.
There are two main types of memory, short -term and long -term.
Short -term memory generally refers to how to pass data in the context of a
single conversation and long -term memory deals with how to fetch and update
information between conversations.
Chains allow us to combine multiple
components together to solve a specific task and build an entire LLM application.
And agents facilitate interaction between the LLM and external APIs.
They play a crucial role in decision -making determining which actions the LLM
should undertake.
Agents are enabling tools for LLMs.
This process involves taking an action, observing the result, and then repeating
the cycle until completion.
Ok, we'll take a break and in the next
video, we'll move on to coding and go deeper into each LLM concept.