LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners

Summary

LangChain 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

00:00

🤖 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.

05:00

🛠 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.

10:02

🔍 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.

Keywords

💡Lang Chain

Lang Chain is an open-source framework designed to facilitate the integration of large language models with external data sources and computational tools. It is pivotal in the video's narrative as it enables developers to harness AI capabilities for various applications. The script mentions Lang Chain's role in connecting models like GPT-4 to databases, illustrating its function in creating data-aware and action-capable applications.

💡Large Language Models (LLMs)

Large Language Models, such as GPT-4, are AI systems with extensive knowledge bases capable of generating human-like text. In the video, they are highlighted as a core component in Lang Chain's framework, used for tasks that require understanding and generating language, emphasizing their importance in creating advanced AI applications.

💡Embeddings

Embeddings are vector representations of text that capture semantic meaning, allowing for efficient storage and retrieval in a vector database. The script explains how embeddings are used to store chunks of text derived from documents, enabling the language model to access relevant information quickly, as demonstrated by the process of storing and querying chunks in Pinecone.

💡Vector Database

A vector database is a type of database optimized for storing and retrieving vectorized data, such as text embeddings. The video script describes using Pinecone as a vector store, showing how it can be used in conjunction with Lang Chain to manage and query embeddings for AI applications.

💡Prompt Templates

Prompt templates in the context of Lang Chain are dynamic text structures that can be populated with user inputs to interact with language models. The script illustrates their use in creating flexible prompts for AI models, allowing for personalized and context-aware interactions.

💡Chains

Chains in Lang Chain refer to a sequence of operations or a pipeline that combines multiple components to perform a specific task. The video explains how chains can be used to create a workflow where a language model and a prompt template are integrated to generate responses or perform actions, exemplified by the sequential chains that explain concepts in simple terms.

💡Agents

Agents in Lang Chain are components that enable language models to interact with external systems, such as APIs. The script discusses agents' potential to allow language models to perform actions beyond generating text, such as executing Python code or interacting with web services, showcasing their role in extending AI capabilities.

💡Open AI API

The Open AI API is a service that provides access to advanced AI models, including GPT-4. The video script mentions the use of the Open AI API key for authentication and to demonstrate how Lang Chain can leverage the capabilities of Open AI's models for various applications.

💡Pinecone

Pinecone is a vector database service used in the video to store and manage embeddings. It is highlighted as an essential tool in Lang Chain's framework for building applications that require efficient text retrieval, as shown by the process of storing document chunks and performing similarity searches.

💡GPT-4

GPT-4 is a large language model introduced by Open AI with significant improvements over its predecessors. The video script discusses GPT-4's role in Lang Chain, emphasizing its enhanced capabilities and the potential impact on AI applications, especially after its introduction in March 2023.

💡Data Analytics

Data analytics involves the examination of data to extract valuable insights. The script suggests that Lang Chain's ability to connect large language models to company data can lead to exponential progress in data analytics, indicating a significant application area for the framework.

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