Langchain vs LlamaIndex vs OpenAI GPTs: Which one should you use?

are you using large language models or

llms in your work and seeking the most

effective way to leverage the power for

your application then this video is for

you let's dive into llm application

development comparing the path of

building your own framework from scratch

with utilizing established platforms

like Lang chain L index and open AI

assistants the first obvious choice is

to construct your own framework from the

ground up you need to code everything

this route well demon in terms of

technical expertise time and resources

gives you invaluable freedom and control

plus you can easily fork and edit open

source approaches as we did with our AI

tutor with Buster a useful repository if

you aim to build retrieval augmented

generation or rag systems imagine

implementing the hide technique which

generates synthetic documents based on

the user's prompt and uses the generated

documents embedding for retrieval which

may be Clos closer to a data point in

the aming space than the original query

it's a challenging technique to

implement from scratch but it's possible

to incorporate it in Frameworks like

Lama index and Lang chain in one line of

code if you aim for a very long-term

product that you can fully own its IP

and updates then going from scratch is

the way to go the results will be the

perfect fit for your specific

requirements but you will encounter many

challenges that you didn't expect and it

will take much more time to develop if

you do not have unlimited time and

resources then you may want to take a

look at pre-built platforms like using

open

gpts if quick deployment and

accessibility are your priorities this

path is the go-to openi assistance

including GPT 3.5 turbo and gp4 provide

a streamlined and userfriendly

experience you can build very powerful

apps super quickly but they will be

quite dependent on open a and you will

hardly be able to bring unique value

this is definitely not an an ideal

long-term option but it's a powerful way

to quickly build a proof of concept and

show it to others they are perfect for

those eager to integrate llm

capabilities swiftly and efficiently

into their applications without the

complexities of building and training

models and Frameworks from scratch plus

the code interpreter noledge Retriever

and custom function code they provide

allow you to build a quite powerful app

especially if you can code your own apis

or use external ones the cost while

present is just generally more

manageable than undertaking the entire

development process on your own as well

it's going to cost you a few dollars to

make it and then it will depend on how

much you share it with others obviously

but what if you need something more

tailored than offir Solutions yet not as

time incentive as building from scratch

this is where Lang chain and L index

come into play but you need to

understand the difference between

both Lang chain offers a powerful and

flexible framework for building

applications with llms it stand out for

its ability to integrate seamlessly with

various llm providers like openi cohere

and hugging face or your own as well as

data sources such as Google search and

Wikipedia use l chain to create

application that can process user input

text and retrieve relevant responses

leveraging the latest NLP technology a

key advantage of Lang chain is its

support for prompt engineering a crucial

aspect of working with llms by

constructing effective prompts you can

significantly influence the quality of

the model's output Lang chain simplifies

this process with tools like prompt

templates which allow for the easy

integration of variables and context

into the prompts additionally output

parsers in L chain will transform the

language models text responses into

structured data like Json objects which

you don't have to code yourself L chain

is also quite useful for applications

requiring maintaining a user's context

throughout a conversation similar to

chat GPT like a medical chatbot or a ma

tutor for example they also recently

introduced Lang chain expression

language or LCL for short a coding

syntax where you can create chains by

simply piping them together using the

bar symbol it enables Swift prototyping

and trying different combinations of

components they also introduced The Lang

serve feature designed to facilitate

chains deployment process using fast API

they provide great features like

templates for different use cases and a

simple chat interface in summary Lang

chain is a nice middle ground for a

balance between customization and ease

of use its flexibility and integrating

with different llms and external data

sources coupled with its userfriendly

tools for prompt engineering and data

parsing make it an ideal choice for

building a wide range of llm powered

applications across various domains

another Advantage is their debugging

tools that simplify the development

process reducing the technical burden

significantly if you are curious about L

chain we shared two free courses using

it in the Gen 360 course series Linked

In the description below in contrast

Lama index excels in sophisticated data

handling and retrieval

capabilities it's particularly suited

for projects where you must handle

complex data sets and use Advanced

querying techniques Lama index's

strength lies in its robust data

management and manipulation features

making it a powerful tool for data

intensive applications practical terms l

IND offers key features such as data

connectors for integrating diverse data

sources including apis PDFs and SQL

databases it's data indexing capability

organizes data to make it readily

consumable by llms enhancing the

efficiency of data retrieval this

framework is particularly beneficial for

building rag applications where it acts

as a powerful data framework connecting

data with language models simplifying

programmers lives L index supports

efficient indexing and retrieval methods

better chunking strategies and

multimodality making it suitable for

various applications including Document

qna Systems data augmented chat buts

knowledge agents structured analytics

and Etc these tools also make it well

suited for advanced use cases like

multi-document analysis and querying

complex PDFs with embedded tables and

charts one example query tool is the sub

question query engine which breaks down

a complex query into several sub

questions and uses different data

sources to respond to each it then

complies all the retrieved documents to

construct the final answer as I

mentioned the Lama index framework

offers a wide range of advanced

retrieving techniques but more

specifically there's the recursive

retrieval enabling the application to

navigate through the graph of

interconnected nodes to locate precise

information in multiple chunks they also

introduced the concept of Lama packs a

collection of real world rag based

applications ready for deployment and

easy to build on top of these were just

a few concrete examples but there are

many other techniques that they can

facilitate for us which makes the

library really useful in essence Lama

index is your go-to for a rag based

application also offering fine-tuning

and embeding optimizations and the best

thing is that it's free open source and

continually

developed each of these paths offer its

unique set of advantages and challenges

building your own framework from scratch

gives you complete control but demands

substantial resources and expertise open

ey assistants offer an accessible and

quick to deploy option suitable for

those looking to integrate llms without

deep technical involvement or to create

a quick proof of concept L chain

provides a balance of customization and

ease of use ideal for developers seeking

flexibility in their llm interactions in

most cases Lama index stands out in its

robust data handling and retrieval

capabilities perfect for data Centric

applications like rag in the end the

choice boils down to your project and

the company's specific requirements and

constraints the key is to align your

decision with the projects goals and the

resources at your disposal they each

have a purpose and I personally used all

of them for different projects we also

have detailed lessons on Lang chain and

Lama index with practical examples in

the course we've built in collaboration

with 2di active Loop and the Intel

disruptor initiative I hope this video

was useful to help you choose the best

framework for your use case thank you

for

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