LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners
Summary
TLDRLangChain is an open-source framework that integrates AI language models like GPT-4 with external data sources and computations. It allows developers to reference entire databases and take actions like sending emails. The framework uses vector databases to store text chunks as embeddings, enabling language models to provide accurate answers or perform tasks. LangChain's value lies in its ability to create data-aware and authentic applications with a wide range of practical uses, from personal assistance to advanced data analytics. The video demonstrates core concepts like LLM wrappers, prompt templates, chains, embeddings, and agents, showcasing how to use LangChain for building AI applications.
Takeaways
- 🌐 LangChain is an open-source framework that integrates AI and large language models with external data and computation sources.
- 🔍 It allows developers to connect models like GPT-4 to proprietary data sources, enhancing the model's ability to provide specific answers from user data.
- 📚 LangChain can reference entire databases, not just snippets of text, enabling more comprehensive and relevant responses.
- 🛠️ The framework is offered as a Python package, specifically TypeScript, and is gaining popularity due to the introduction of GPT-4.
- 🔑 It uses embeddings, which are vector representations of text, stored in a vector database to perform similarity searches and retrieve relevant information.
- 🤖 LangChain facilitates the creation of data-aware and authentic applications that can take actions and provide answers to user queries.
- 🚀 The framework supports practical use cases like personal assistance, studying, learning, coding, data analysis, and data science.
- 🔑 Main value propositions of LangChain include LLM wrappers for connecting to large language models, prompt templates for dynamic input, indexes for information extraction, and chains for combining components.
- 🛠️ LangChain also includes agents that enable language models to interact with external APIs, expanding the capabilities of AI applications.
- 📝 The script demonstrates setting up the environment with necessary API keys and using LangChain to create an application that explains machine learning concepts.
- 🔗 The video script provides a high-level overview of LangChain's components, including models, problems, chains, embeddings, vector stores, and agents.
Q & A
What is LangChain?
-LangChain is an open-source framework designed to enable developers working with AI to integrate large language models such as GPT-4 with external sources of computation and data.
Why is LangChain's popularity increasing?
-LangChain's popularity is growing due to its ability to connect large language models with external data sources, which became especially significant after the introduction of GPT-4 in March 2023.
How does LangChain allow developers to use their own data with AI models?
-LangChain enables developers to connect large language models like GPT-4 to their own data sources, such as databases or documents, by referencing entire databases filled with proprietary information.
What is the significance of using embeddings in LangChain?
-Embeddings in LangChain are vector representations of text that allow developers to build applications with a pipeline that can perform similarity searches in a vector database, fetching relevant information chunks to feed into the language model.
What kind of actions can LangChain help automate with the retrieved information?
-LangChain can assist in automating actions such as sending an email with specific information, based on the data retrieved from the vector database and the initial user query.
How does LangChain facilitate the development of data-aware and authentic applications?
-LangChain helps build applications that are data-aware by referencing data in a vector store and authentic by enabling actions and not just providing answers to questions.
What are the three main concepts that make up the value proposition of LangChain?
-The three main concepts of LangChain's value proposition are LLM wrappers for connecting to large language models, prompt templates to avoid hardcoding text inputs, and indexes for extracting relevant information for the language models.
Can you explain the role of chains in LangChain?
-Chains in LangChain combine multiple components together to solve a specific task and build an entire language model application. They allow for the creation of sequential processes where one chain's output can be the input for another chain.
How does LangChain handle the storage and retrieval of text chunks in a vector store?
-LangChain uses a text splitter tool to break down text into chunks, which are then converted into embeddings using a language model's embedding capability. These embeddings are stored in a vector store like Pinecone for later retrieval.
What is the purpose of agents in LangChain?
-Agents in LangChain allow the language model to interact with external APIs, enabling the model to perform tasks such as running Python code or accessing other services, thus expanding the capabilities of the applications built with LangChain.
Outlines
🤖 Introduction to Lang Chain Framework
Lang Chain is an open-source framework designed to facilitate the integration of large language models (LLMs) like GPT-4 with external computation and data sources. The framework, available as a Python package with TypeScript specifics, has gained popularity following the release of GPT-4 in March 2023. It enables developers to connect an LLM to proprietary data sources like databases or documents, allowing for more specific and personalized information retrieval and actions, such as sending emails with specific data. The framework operates by breaking down documents into vector representations stored in a vector database, which can then be referenced by the LLM to provide answers or perform actions. Lang Chain is poised to revolutionize various fields, including personal assistance, learning, coding, data analysis, and data science, by connecting LLMs to company data and advanced APIs.
🛠 Setting Up Lang Chain and Exploring Core Concepts
This section provides a step-by-step guide on setting up the Lang Chain environment, including installing necessary libraries and obtaining API keys for OpenAI and Pinecone, a vector store used in the video. The tutorial begins by demonstrating how to work with LLMs through Lang Chain, showcasing the use of OpenAI's text completion model and chat models like GPT 3.5. It then introduces prompt templates, which allow dynamic user input integration into prompts sent to the language model. The concept of chains is explained as a way to combine language models and prompt templates to create interfaces that process user input and generate model outputs. Sequential chains are also discussed, which can build on the output of one chain to perform further tasks. The tutorial also covers the process of splitting text into chunks, converting them into embeddings using OpenAI's embedding model, and storing these embeddings in Pinecone for later retrieval.
🔍 Embeddings, Vector Stores, and Agents in Lang Chain
In this part of the script, the focus shifts to embeddings and vector stores, which are crucial for storing and retrieving text data in a format that can be efficiently handled by LLMs. The process of checking text, splitting it into chunks, and converting these chunks into embeddings using OpenAI's embedding model is detailed. The embeddings are then stored in Pinecone, a vector store, where they can be used for similarity searches to retrieve relevant information. The script also introduces the concept of agents in Lang Chain, which allows the LLM to interact with external APIs and execute tasks such as running Python code. An example is given where the LLM uses an agent to find the roots of a quadratic function, demonstrating the potential for integrating LLMs with executable code and external services.
Mindmap
Keywords
💡Lang Chain
💡Large Language Models (LLMs)
💡Embeddings
💡Vector Database
💡Prompt Templates
💡Chains
💡Agents
💡Open AI API
💡Pinecone
💡GPT-4
💡Data Analytics
Highlights
Lang chain is an open-source framework that enables developers to integrate large language models with external computation and data sources.
The framework is gaining popularity, especially after the introduction of GPT-4 in March 2023.
Lang chain allows developers to connect large language models like GPT-4 to their own data sources for personalized assistance.
Data can be referenced from entire databases, not just snippets of text.
Lang chain facilitates taking actions based on retrieved information, such as sending emails with specific data.
Data is stored in a vector database as embeddings, which are vector representations of the text.
Applications built with Lang chain follow a pipeline where a user's question is used to fetch relevant information from the vector database.
Lang chain supports building data-aware and authentic applications that can take actions and provide answers.
The framework opens up an infinite number of practical use cases, including personal assistance and data analytics.
Lang chain's value proposition includes LLM wrappers, prompt templates, indexes, chains, and agents.
LLM wrappers connect to large language models like GPT-4 from platforms like OpenAI or Hugging Face.
Prompt templates in Lang chain help avoid hardcoding text inputs for the language models.
Indexes in Lang chain extract relevant information for the language models.
Chains in Lang chain allow combining multiple components to solve specific tasks and build entire LLM applications.
Agents in Lang chain enable the LLM to interact with external APIs.
Lang chain is continuously being updated with new features and capabilities.
The video provides a high-level overview of Lang chain's framework, including models, problems, chains, embeddings, and vector stores.
Lang chain uses Pinecone as a vector store to manage embeddings and perform similarity searches.
The video demonstrates how to use Lang chain to instantiate a Python agent executor that can run Python code using an OpenAI language model.
Transcripts
blank chain what is it why should you
use it and how does it work let's have a
look
Lang chain is an open source framework
that allows developers working with AI
to combine large language models like
gbt4 with external sources of
computation and data the framework is
currently offered as a python or a
JavaScript package typescript to be
specific in this video we're going to
start unpacking the python framework and
we're going to see why the popularity of
the framework is exploding right now
especially after the introduction of
gpt4 in March 2023 to understand what
need Lang chain fills let's have a look
at a practical example so by now we all
know that chat typically or tpt4 has an
impressive general knowledge we can ask
it about almost anything and we'll get a
pretty good answer
suppose you want to know something
specifically from your own data your own
document it could be a book a PDF file a
database with proprietary information
link chain allows you to connect a large
language model like dbt4 to your own
sources of data and we're not talking
about pasting a snippet of a text
document into the chativity prompt we're
talking about referencing an entire
database filled with your own data
and not only that once you get the
information you need you can have Lang
chain help you take the action you want
to take for instance send an email with
some specific information
and the way you do that is by taking the
document you want your language model to
reference and then you slice it up into
smaller chunks and you store those
chunks in a Victor database the chunks
are stored as embeddings meaning they
are vector representations of the text
this allows you to build language model
applications that follow a general
pipeline a user asks an initial question
this question is then sent to the
language model and a vector
representation of that question is used
to do a similarity search in the vector
database this allows us to fetch the
relevant chunks of information from the
vector database and feed that to the
language model as well
now the language model has both the
initial question and the relevant
information from the vector database and
is therefore capable of providing an
answer or take an action
a link chain helps build applications
that follow a pipeline like this and
these applications are both data aware
we can reference our own data in a
vector store and they are authentic they
can take actions and not only provide
answers to questions
and these two capabilities open up for
an infinite number of practical use
cases anything involving personal
assistance will be huge you can have a
large language model book flights
transfer money pay taxes now imagine the
implications for studying and learning
new things you can have a large language
model reference an entire syllabus and
help you learn the material as fast as
possible coding data analysis data
science is all going to be affected by
this
one of the applications that I'm most
excited about is the ability to connect
large language models to existing
company data such as customer data
marketing data and so on
I think we're going to see an
exponential progress in data analytics
and data science our ability to connect
the large language models to Advanced
apis such as metas API or Google's API
is really gonna gonna make things take
off
so the main value proposition of Lang
chain can be divided into three main
Concepts
we have the llm wrappers that allows us
to connect to large language models like
gbt4 or the ones from hugging face
prompt templates allows us to avoid
having to hard code text which is the
input to the llms
then we have indexes that allows us to
extract relevant information for the
llms the chains allows us to combine
multiple components together to solve a
specific task and build an entire llm
application
and finally we have the agents that
allow the llm to interact with external
apis
there's a lot to unpack in Lang chain
and new stuff is being added every day
but on a high level this is what the
framework looks like we have models or
wrappers around models we have problems
we have chains we have the embeddings
and Vector stores which are the indexes
and then we have the agents so what I'm
going to do now is I'm going to start
unpacking each of these elements by
writing code and in this video I'm going
to keep it high level just to get an
overview of the framework and a feel for
the different elements first thing we're
going to do is we're going to pip
install three libraries we're going to
need python.in to manage the environment
file with the passwords we're going to
install link chain and we're going to
install the Pinecone client Pinecone is
going to be the vector store we're going
to be using in this video in the
environment file we need the open AI API
key we need the pine cone environment
and we need the pine cone API key
foreign once you have signed up for a
Pinecone account it's free the API keys
and the environment name is easy to find
same thing is true for openai just go to
platform.orgmaili.com account slash API
keys
let's get started so when you have the
keys in an environment file all you have
to do is use node.n and find that in to
get the keys and now we're ready to go
so we're going to start off with the
llms or the wrappers around the llms
then I'm going to import the open AI
Rubber and I'm going to instantiate the
text DaVinci 003 completion model and
ask it to explain what a large language
model is and this is very similar to
when you call the open AI API directly
next we're going to move over to the
chat model so gbt 3.5 and gbt4 are chat
models
and in order to interact with the chat
model through link chain we're going to
import a schema consisting of three
parts an AI message a human message and
a system message
and then we're going to import chat open
AI the system message is what you use to
configure the system when you use a
model and the human message is the user
message
thank you
to use the chat model you combine the
system message and the human message in
a list and then you use that as an input
to the chat model
here I'm using GPT 3.5 turbo you could
have used gpt4 I'm not using that
because the open AI service is a little
bit Limited at the moment
so this works no problem let's move to
the next concept which is prompt
templates so prompts are what we are
going to send to our language model but
most of the time these problems are not
going to be static they're going to be
dynamic they're going to be used in an
application and to do that link chain
has something called prompt templates
and what that allows us to do is to take
a piece of text and inject a user input
into that text and we can then format
The Prompt with the user input and feed
that to the language model
so this is the most basic example but it
allows us to dynamically change the
prompt with the user input
the third concept we want to Overlook at
is the concept of a chain
a chain takes a language model and a
prompt template and combines them into
an interface that takes an input from
the user and outputs an answer from the
language model sort of like a composite
function where the inner function is the
prompt template and the outer function
is the language model
we can also build sequential chains
where we have one chain returning an
output and then a second chain taking
the output from the first chain as an
input
so here we have the first chain that
takes a machine learning concept and
gives us a brief explanation of that
concept the second chain then takes the
description of the first concept and
explains it to me like I'm five years
old
then we simply combine the two chains
the first chain called chain and then
the second chain called chain two into
an overall chain
and run that chain
and we see that the overall chain
returns both the first description of
the concept and the explain it to me
like I'm 5 explanation of the concept
all right let's move on to embeddings
and Vector stores but before we do that
let me just change the explainer to me
like I'm five prompt so that we get a
few more words
I'm gonna go with 500 Words
all right so this is a slightly longer
explanation for a five-year-old
now what I'm going to do is I'm going to
check this text and I'm going to split
it into chunks because we want to store
it in a vector store in Pinecone
and Lang chain has a text bitter tool
for that so I'm going to import
recursive character text splitter and
then I'm going to spit the text into
chunks
like we talked about in the beginning of
the video
we can extract the plain text of the
individual elements of the list with
page content
and what we want to do now is we want to
turn this into an embedding which is
just a vector representation of this
text and we can use open ai's embedding
model Ada
with all my eyes model we can call embed
query on the raw text that we just
extracted from the chunks of the
document and then we get the vector
representation of that text or the
embedding
now we're going to check the chunks of
the explanation document and we're going
to store the vector representations in
pine cone
so we'll import the pine cone python
client and we'll import pine cone from
Lang chain Vector stores and we initiate
the pine cone client with the key and
the environment that we have in the
environment file
then we take the variable texts which
consists of all the chunks of data we
want to store we take the embeddings
model and we take an index name and we
load those chunks on the embeddings to
Pine Cone and once we have the vector
stored in Pinecone we can ask questions
about the data stored what is magical
about an auto encoder and then we can do
a similarity search in Pinecone to get
the answer or to extract all the
relevant chunks
if we head over to Pine Cone we can see
that the index is here we can click on
it and inspect it
check the index info we have a total of
13 vectors in the vector store
all right so the last thing we're going
to do is we're going to have a brief
look at the concept of an agent
now if you head over to open AI chat GPT
plugins page you can see that they're
showcasing a python code interpreter
now we can actually do something similar
in langtune
so here I'm importing the create python
agent as well as the python Rebel tool
and the python webble from nankchain
then we instantiate a python agent
executor
using an open AI language model
and this allows us to having the
language model run python code
so here I want to find the roots of a
quadratic function and we see that the
agent executor is using numpy roots to
find the roots of this quadratic
function
alright so this video was meant to give
you a brief introduction to the Core
Concepts of langchain if you want to
follow along for a deep dive into the
concepts hit subscribe thanks for
watching
関連する他のビデオを見る
![](https://i.ytimg.com/vi/P3MAbZ2eMUI/hq720.jpg)
What is LangChain? 101 Beginner's Guide Explained with Animations
![](https://i.ytimg.com/vi/1bUy-1hGZpI/hq720.jpg)
What is LangChain?
![](https://i.ytimg.com/vi/2xxziIWmaSA/hq720.jpg)
The LangChain Cookbook - Beginner Guide To 7 Essential Concepts
![](https://i.ytimg.com/vi/u5Vcrwpzoz8/hq720.jpg)
"I want Llama3 to perform 10x with my private knowledge" - Local Agentic RAG w/ llama3
![](https://i.ytimg.com/vi/69bH4IHZivs/hq720.jpg)
"Next Level Prompts?" - 10 mins into advanced prompting
![](https://i.ytimg.com/vi/pfpIndq7Fi8/hq720.jpg)
RAG from scratch: Part 10 (Routing)
5.0 / 5 (0 votes)