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 [Music] [Music] watching

Channel: What's AI by Louis-François Bouchard

Views: 7,656

Keywords: ai, artificial intelligence, machine learning, deep learning, ml, data science, ainews, ai news, whats ai, whatsai, louis, louis bouchard, bouchard, ai simplified, simple ai, ai explained, ai demystified, demystify ai, explain ai, what's ai

Id: g84uWgVXVYg

Length: 8min 59sec (539 seconds)

Published: Thu Dec 21 2023