What is LangChain? 101 Beginner's Guide Explained with Animations

Summary

The video script delves into LangChain, a framework that simplifies the development of applications using large language models. It discusses the evolution from a Python package to TypeScript and Ruby ports, emphasizing LangChain's ability to handle complex tasks involving data integration and model actions. The script explores components like document loaders, text splitters, vector databases, and prompt templates, illustrating how they streamline interactions with language models. It also touches on the importance of semantic search, embedding models, and the use of chains and agents to create dynamic, stateful applications, hinting at the vast potential of this emerging technology.

Takeaways

• 🔥 LangChain is a popular framework for developing applications on top of large language models, addressing the complexities beyond simple API requests.
• 🔧 It started as a Python package but has TypeScript and Ruby ports, aiming for feature parity and expanding accessibility.
• 📈 LangChain simplifies tasks by chaining components to handle data from various sources and execute actions, making it easier to swap models or databases.
• 🔍 The framework supports models like language models and text embedding models, which are crucial for semantic search and understanding text in a vector space.
• 📝 Prompts are essential for guiding the input to language models, with LangChain offering prompt templates for flexibility and structured input.
• 📚 Few-shot learning is facilitated by providing examples to prompts, helping to shape the model's output with a given set of data.
• 🔑 Output parsers are used to automatically structure the language model's output into objects, making it easier to handle and interpret.
• 🗂 The indexes module allows for the use of personal data with document loaders and text splitters, enabling the integration of diverse data formats.
• 🔎 Vector databases are integrated with LangChain for storing and retrieving embedded documents, supporting semantic search capabilities.
• 🔗 Chains in LangChain abstract model calls to language models, handling prompt formatting, model interaction, and result retrieval in a stateless manner.
• 🤖 Agents in LangChain, though not detailed in the script, are mentioned as a higher-level concept that will be explored further, indicating a potential for more complex interactions.

Q & A

What is LangChain?

LangChain is a framework used to develop applications on top of large language models like GPT, Llama, and Hugging Face models. It started as a Python package and now has a TypeScript port and a Ruby version in development.

Why is LangChain needed?

LangChain is needed because it simplifies the development of complex applications that involve connecting language models to various data sources and making them perform actions like sending emails, searching the web, or running code. It provides components that handle these tasks, making the process less messy and repetitive.

What are some of the components LangChain offers?

LangChain offers components such as document loaders, text splitters, vector databases, prompt templates, and tools for actions like sending emails. These components help in loading, processing, and managing data for language models.

What are the two types of models in LangChain?

The two types of models in LangChain are language models and text embedding models. Language models take text as input and return text, while text embedding models convert text into arrays of numbers for use in vector spaces.

What is semantic search and how is it used in LangChain?

Semantic search is a technique that finds pieces of text most similar in meaning to a given input within a vector space. LangChain uses text embedding models to create these vector representations, enabling semantic search over the stored data.

How does LangChain handle prompts?

LangChain provides the prompt template class to construct prompts using multiple values. It supports various use cases like few-shot learning and output parsing, enhancing the flexibility and readability of prompts.

What is few-shot learning in the context of LangChain?

Few-shot learning in LangChain involves providing the model with a few examples to guide its output. This is done by creating a list of examples, formatting them with a template, and using the few-shot prompt template object.

What is the purpose of output parsers in LangChain?

Output parsers in LangChain automatically structure the output from language models into defined objects, making the results more useful and organized. For example, they can parse the output into joke objects with specific fields like setup and punchline.

What are indexes in LangChain, and how are they used?

Indexes in LangChain refer to methods of loading and managing data. The indexes module provides document loaders for various formats, text splitters to chunk large texts, and integrations with vector databases for storing and retrieving embedded text.

What is the difference between stateless and stateful operations in LangChain?

Stateless operations in LangChain do not remember previous interactions, while stateful operations use memory to maintain context across interactions. For example, conversation chains use memory to handle ongoing dialogues, making them stateful.

Outlines

00:00

🤖 Introduction to LangChain: AI Framework for Complex Applications

LangChain is a cutting-edge framework that facilitates the development of applications on top of large language models. Initially a Python package, it now includes a TypeScript version and a budding Ruby version. The framework is essential for handling complex tasks such as integrating language models with various data sources and performing actions like sending emails or executing code. LangChain simplifies these processes through components like document loaders, text splitters, vector databases, and prompt templates. It also supports easy swapping of language models and vector databases, enhancing flexibility and efficiency in AI application development.

05:01

🔍 Deep Dive into LangChain Components: Models, Prompts, and Indexes

This paragraph delves into the intricacies of LangChain's components, focusing on models, prompts, and indexes. It explains the distinction between language models and text embedding models, the latter of which convert text into numerical arrays for semantic search. The paragraph also covers prompt templates, which are flexible inputs for models, and output parsers that structure the model's outputs. Additionally, it introduces the concept of few-shot learning for guiding model responses and example selectors for managing costs. The indexes module is highlighted for its ability to load and process data from various formats and services, emphasizing the importance of data in AI applications.

10:02

🔗 LangChain in Action: Chains, Agents, and Practical Applications

The final paragraph showcases how LangChain can be practically applied in creating AI applications. It discusses the use of chains to abstract model calls and memory to make interactions stateful. The paragraph also touches on custom chains for specific tasks and high-level chains for solving complex problems. An example of chaining language model calls to generate a company name and a catchphrase is provided. The potential of agents in LangChain is mentioned, with a promise of a future in-depth exploration. The paragraph concludes by emphasizing the practicality and versatility of LangChain in the rapidly evolving field of AI.

Keywords

💡LangChain

LangChain is a framework designed to facilitate the development of applications using large language models. It started as a Python package and is expanding to other languages like TypeScript and Ruby. In the video, LangChain is presented as a solution to the complexity that arises when integrating language models with various data sources and actions, such as sending emails or running code.

💡Language Models (LMs)

Language models are AI systems trained to generate human-like text. They are a core component of LangChain, allowing developers to interact with and utilize these models within their applications. The script distinguishes between two subtypes: large language models (LLMs) and chat models, with the former taking text as input and the latter handling chat messages.

💡Vector Databases

Vector databases are used to store and retrieve text data in a format that represents text as vectors in a multi-dimensional space. They enable semantic search, where the system looks for text that is similar in meaning. In the context of the video, vector databases are used in conjunction with text embedding models to index and retrieve data efficiently.

💡Text Embedding Models

Text embedding models convert text into a numerical format, allowing for semantic comparisons and searches within a vector space. The video mentions that these models can process large texts and output vectors of high dimensions, which are crucial for the functionality of vector databases.

💡Prompts

Prompts are the inputs given to language models to guide their output. The video explains that prompts can be constructed using prompt templates, which allow for flexibility and can be formatted with multiple values. They are essential for controlling the responses of language models and are used in techniques like few-shot learning.

💡Few-shot Learning

Few-shot learning is a technique where a model is given a few examples to guide its output for a specific type of query. The video script uses this concept to illustrate how prompts can be formatted with examples to help language models understand the expected responses, thus improving the quality of their output.

💡Output Parsers

Output parsers are used to automatically structure the outputs from language models into a specific format, such as objects. In the script, an example is given where a joke's setup and punchline are parsed into a 'joke' object, demonstrating how output parsers can help in organizing and interpreting the model's responses.

💡Document Loaders

Document loaders are components in LangChain that can load data from various sources and formats, such as PDFs, CSVs, and SQL databases. They are essential for integrating external data into the application, allowing developers to work with their own data within the framework.

💡Text Splitters

Text splitters are used to divide large text documents into smaller, manageable chunks that can fit within the input token size limits of language models. The video mentions using a recursive character text splitter to create documents from large texts, which is important for constructing prompts that can be effectively processed by the models.

💡Chains

Chains in LangChain are a way to abstract model calls to language models, allowing developers to create sequences of operations that can be executed in order. The video describes how chains can be used to format prompts, send them to models, and handle the results, making it easier to manage complex interactions with language models.

💡Agents

Agents in the context of LangChain are higher-level abstractions that use language models to perform tasks. While not fully detailed in the script, agents are mentioned as a way to think about what needs to be done using a language model before actually doing it, potentially involving the use of tools and other components within the framework.

Highlights

Langchain is a framework for developing applications on top of large language models, addressing the complexity of integrating models with various data sources and actions.

Langchain started as a Python package with a TypeScript port catching up in feature parity and a newly started Ruby version.

It simplifies tasks like connecting language models to data from Google Analytics, Stripe, SQL, PDFs, and CSVs, and performing actions like sending emails and running code.

Langchain uses components like document loaders, text splitters, and vector databases to manage data and inject it into prompt templates for model input.

The framework allows for easy swapping of language models, vector databases, and data sources with minimal code changes.

Langchain's modules include models, prompts, indexes, memory, chains, and agents, each serving a specific purpose in the development process.

Models can be language models or text embedding models, with the latter converting text into numerical arrays for vector space representation.

Prompt templates in Langchain offer flexibility in constructing prompts using multiple values and support few-shot learning for guiding model output.

Output parsers automatically structure the outputs from language models into objects, making it easier to handle complex data.

Example selectors help manage the cost and complexity of prompts by selecting relevant examples for few-shot learning.

Chat prompt templates are designed for chat models, which take a list of chat messages as input, each with a role (AI, human, system).

Indexes module in Langchain provides document loaders for various formats and services, allowing easy integration of diverse data sources.

Text splitters are used to break down large texts into manageable chunks for embedding and retrieval in vector databases.

Vector stores in the indexes module offer integrations with various databases like Pinecone, Redis, and ChromaDB for semantic search capabilities.

Chains in Langchain abstract model calls to language models, handling prompt formatting, model interaction, and result retrieval.

Memory can be used with chains to make interactions stateful, allowing for more context-aware responses in applications.

Custom chains or high-level chains can be created for complex tasks, such as solving math problems or letting the language model write and execute code.

Agents in Langchain, to be covered in more depth in future videos, represent a higher level of abstraction for accomplishing tasks using language models.

Transcripts

langjin probably one of the hottest

things in AI right now after Vector

databases it's a framework used to

develop applications on top of large

language models like GPT llama hugging

face models and much more it first

started as a python package but there is

also a typescript port catching up in

feature parity and a ruby one that just

got started but why is it needed in the

first place could we simply send a

request an API or model and call it a

day and you're right that would work for

simple applications but as soon as you

start increasing the complexity like

connecting the language model to your

own data from Google analytics stripe

SQL PDF csvs or making the language

models take actions such as sending

emails searching the web or running code

in your terminal things start to get

messy and repetitive Lane chain offers

to fix this issue with components we can

use document loaders to load data from

sources like PDF stripe and then

optionally use a text splitter to chunk

it down before storing it in a vector

database at runtime the data can be

injected in a prompt template to be then

sent as input to the models we might

also use tools to take actions for

example sending an email with the

output's content in practice these

abstractions mean you can easily swap to

another llm because of cost or features

test another Vector database

capabilities or ingest another data

source in a few lines of code chains are

how the magic happens we chain together

components in a way to accomplish a

specific tasks agents are more abstract

the first think about what they need to

do using a language model before doing

it using things like tools if you're

curious about research papers showing

the power of connecting language models

to your own data and the outside world

consider looking at self ask with search

and react which were published around

the same time Lang chain was launched

now let's take a look at what's really

going on behind the scenes there are

currently seven modules LinkedIn offers

that newcomers to the space should know

about models prompts indexes memory

chains and agents models at a high level

there are two different types of models

language models and text embedding

models the embedding models turn text

into an array of numbers we can then

think of text in a vector space in this

image we can see how in a 2d space man

is the king what woman is the queen they

represent different things but we can

see a pattern this allows for semantic

search where we look for pieces of text

that are most similar in meaning for a

given thesis text within the vector

space for example open ai's text

embedding model can embed large pieces

of text precisely 8 100 and 91 tokens

which given their word to token ratio of

0.75 should roughly work for up to

6143 words it outputs vectors of 1536

Dimensions we can use LINE chain to

interface with multiple embedding

providers some apis like open Ai and

cohere are paid but we can do it for

free and enter privacy on our data by

using open source models run locally

with hugging faces open source embedding

you can now create your own embeddings

on your own machine using only four

lines of code however the number of

Dimensions might be different and the

quality of embeddings might be lower

which could make retrieval less accurate

moving on to language models there are

two different subtypes llms and chat

models llms wrap apis which take text in

and return text chat models wrap models

which take chat messages in and return a

chat message it's a subtle difference

but the interface to use them is the

same we can import both classes

instantiate them and then use the

predict function on both of them and see

the difference however you probably

won't be passing text directly to the

models and instead use prompts a prompt

refers to the input of the model we

often want more flexibility than in

hard-coded string link chain provides

the prompt template class to

constructive prompt using multiple

values the important Concepts to know of

prompts are prompt templates output

parsers example selectors and chat

prompt templates prompt templates here's

an example first you'll want to create a

prompt template object there are two

ways to do this import prompt template

then instant sheet 1 using the

Constructor specify your input variables

in an array and in your template string

by putting them inside of curly braces

if you're feeling lazy you can also use

the helper method from template so you

don't have to specify the input

variables explicitly in both cases you

can then format The Prompt by telling it

what values to replace the placeholders

with under the hood it uses F strings to

format your prompt by default but you

could also use ginger 2 but then why not

just use f strings directly the prompts

help with readability play well with the

rest of the ecosystem and support common

use cases like Q shot learning or output

parsing few shot learning means we give

our prompts a few examples to guide its

output let's see how this is done first

create a list with a few examples next

we specify the template to format the

examples we have provided every example

will be formatted using this prompt

template finally we create the few shot

prompt template object pass in the

examples the example formatter a prefix

given the command and suffix that aims

to guide the output of the llm let's not

forget the input variables and a

separator that will be used to separate

the examples from the prefix and the

suffix we can now generate a prompt and

this is what it looks like this is an

extremely useful Paradigm to control the

output of the llm and guide its response

similarly we might want to use output

parsers we will be automatically parsing

the output of the language model into an

object it's a little more complex but

very useful in structuring the random

outputs from llms let's say we want to

use openai to create joke objects we can

Define our joke class to be more

specific we'll need the jokes setup and

punchline We'll add descriptions to help

the language model understand what those

mean then we can set up a parser and

tell it to parse using our joke class we

use the pedantic output parser which is

the strongest and most recommended now

we create our prompt template let's pass

the template string input variable and

we can use the partial variables field

to inject or parsing instructions into

the prompt template we can then ask the

llm to tell us a joke now that we have

our prompt ready this is what we need to

do to send it to openai we'll first load

our openai API key from our DOT and file

then instantiate the model run its

called under method and parse the

model's output using the parser that

we've instantiated it and there we go we

now have our joke object with its setup

and punchline defined the generated

prompt is pretty complex consider

checking out the GitHub repo to have a

better look at it and to really

understand what's going on we've

previously covered few shot learning

where we pass along examples to show the

model expected answers for a certain

type of query it might be possible that

we have many such examples and we can't

possibly fit all of them also that could

become pretty expensive pretty fast

that's where example selectors come into

play to keep the cost of a prompt

relatively constant we'll use the

length-based example selector just like

before we specify an example prompt this

defines how each example will be

formatted we curator selector pass in

the examples and then the max length by

default the length refers to the number

of words and new lines used by the

formatter examples section of the prompt

and we can take the previous View shot

template we defined and simply add the

selector we just created replacing the

example keyword argument but what about

interacting with chat models this brings

us to chat prompt template as we've

previously mentioned chat models take a

list of chat messages as input the list

is referred to as a prompt what's

different about them is that each

message is prepended by a role either AI

human or system the model is supposed to

follow instructions from the system

message closely there's only one system

message at the beginning and it can at

times sound pretty hypnotic you are a

kind customer service agent responds

allegedly to the customer's question dot

dot dot something like that to tell the

chatbot how to behave AI messages are

messages that come from the model human

message is what we've typed in the roles

give the LM better context on the

ongoing conversation models and prompts

are cool and standardized but how can we

use our own data this is where the

indexes module comes in handy data is

the new oil and you can definitely dig

anywhere and find tons of it Lange

delivers the rigs by providing document

loaders document is their fancy way of

saying text there are a ton of supported

formats and services like CSV email SQL

Discord AWS S3 PDF the list goes on and

on it can take only three lines of code

to import your here's how easy it is

first import the loader then specify the

file path and then call the load method

this will load the PDF as text in memory

as an array where each index represents

a page that's nice but when we want to

construct a prompt and include text from

these Pages they might be too big to fit

within the input token size we've talked

about earlier which is why we want to

use text Splitters to chop them into

chunks after reading the text we can

instantiate a recursive character text

splitter and specify a chunk size and a

chunk overlap we call the create

documents method and place our text as

argument we then get an array of

documents now that we have text chunks

we'll want to embed and store them to

eventually retrieve them using semantic

search which is why we have Vector

stores this part of the indexes module

offers multiple Integrations with Vector

databases like pine cone redis Super

Bass chroma DB and much more once you

have your documents ready you'll want to

choose your embedding provider and store

the documents using a vector database

helper method now we can write a

question and do a similarity search over

the vector space moving on to the

retriever part of the module the

retriever Lane chain has the most

support for is the vector store

retriever which you can get by calling

as retriever on a vector store note that

the retriever interface is generic and

doesn't have to represent a vector store

it could also be querying services like

Wikipedia a chat GPT Plugin or a couple

other Integrations it only has to

implement the get relevant documents

which takes in a string and return

documents as an illustrative example one

thing you could do is self querying with

chroma say you have films stored in

chroma DB the movies have as metadata

score and genre then you could run the

following line of code and get the

movies that you wanted now that we have

models prompts and indexes we can use

chains to abstract a model call to an

llm we can instantiate our model writer

prompt and then pass our model and

prompt to the llm change we can then run

the chain on the input variables of our

prompt the chain takes care of

formatting The Prompt sending it to the

model and returning the results this is

a stateless operation meaning the chain

does not remember previous interactions

we could use memory to make it stateful

let's instantiate a chat model and now

use a conversation chain we'll pass the

chat model along with a memory object

now we'll ask it to respond partially to

a question and then to complete its

answer you could also create your own

custom chains or use high level chains

to do things like chaining chains

together solve math problems or even

things like letting the llm write code

and then execute it let's say we want to

create a company name and write a good

catchphrase for it we can chain both LM

calls and a chain after instantiating

our model we create our first prompt and

our first chain then we write our second

prompt asking for the catchphrase and

create our second chain now we can chain

them using the simple sequential chain

and specifying the chains in order we

run the chain on the input variables of

the first prompt and its output will be

used as input variable for the second

prompt there are also a couple of

pre-made chains that you could use like

the LM math chain moving on to agents as

this video is already getting pretty

long we will cover agents more in depth

with real life examples Concepts and

working code in a following video this

is a hot space and has tons of practical

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