Welcome to the LangChain Beginner’s Tutorial for Typescript/Javascript
If you are new to programming and are interested in learning Typescript/Javascript, then you have come to the right place. LangChain provides a beginner-friendly tutorial for Typescript/Javascript that will help you get started with this popular programming language.
What is Typescript/Javascript?
Typescript is a superset of Javascript that adds static typing to the language. It is designed for large-scale applications and provides many advanced features that make it easier to build complex software systems. Javascript, on the other hand, is a scripting language that is widely used for web development. Both Typescript and Javascript are essential languages for anyone interested in web development and programming in general.
What will you learn in this tutorial?
In this tutorial, you will learn the basics of Typescript/Javascript, including:
- Variables and data types
- Functions and control flow
- Object-oriented programming concepts
- Modules and packages
- Handling asynchronous operations
- Working with the DOM
Getting started with the tutorial
To get started with the LangChain Beginner’s Tutorial for Typescript/Javascript, simply head over to our website and follow the step-by-step instructions. The tutorial is designed to be interactive and hands-on, so you can practice what you learn as you go along. Whether you are a complete beginner or have some experience with programming, this tutorial will help you build a solid foundation in Typescript/Javascript.
Conclusion
Learning Typescript/Javascript is a valuable skill that can open up many opportunities in the world of web development and software engineering. With the LangChain Beginner’s Tutorial, you can start your journey towards becoming a proficient Typescript/Javascript developer. So, what are you waiting for? Dive in and start learning today!
Timestamps:
01:00 What problems are LangChain solving?
07:40 LangChain usecases + components
09:30 LangChain demo
11:07 Simple LangChain chain
14:34 LangChain prompt templates
21:11 LangChain agents
25:57 LangChain memory
29:19 LangChain indexes + embeddings (vectorstores)
42:27 Question answering docs
Really really interesting!
My big realization was at the end when you explained that we do the vector comparing offline and then we use the top search as context, that is how we save on openAi tokens! Thank you!
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sixlline_paraggone@mediatama
Can LangChain JS work with a locally running Llama.cpp? if so, do you have any materials on getting that up and running?
Which application do use to make the illustrations? Great video.
Your github code just works and that is excellent !
'PromoSM'
All good you explained very well.
But I have some question I'm Sure I missed something.
During question_answer_docs run every time i'm getting the result "I don't know." —> even i don't know why.
So i tried a diff question same answer.
after checking code i didnt find anything so i tried a question "give me the summary" and response was way different.
this is the content :"This context is about the importance of understanding different perspectives. It emphasizes the need to be open to different points of view in order to form a more complete understanding of a situation. It suggests that having a well-rounded perspective is necessary in order to make informed decisions."
let me know if anyone have idea about this
Great tutorial! Do you notice any downsides to using the JS/TS version over the python version?
Great one, thank you!
Two months late to this video. But still, pretty valuable. I want to say thank you for providing such complete informations.
Langchain began as a Blockchain Project.
Super useful!
Your discord community link is invalid,
Please can you update it?
Thank you for a great introduction!
Fantastic video
This was awesome, thank you for such simple explanations! Joined your newly launched course and looking forward to it!
In openai's defence, chatGPT only has information till 2021 and since langchain was officially launched in 2022, it's expected to not know what it is.
Can you explain how you set up your pinecone index? For example what values you chose for dimensions and metrics?
I keep encountering an error with mine despite the fact that I have a working key and a discoverable index
Thank you. I have a question please. So what I understood at the end, is to answer a question from our data, we convert the query to an embedding, then do vector similarity search, get the block of text, make a prompt + user question + context text –> get the answer.
That's okay, but that only gives us first level reference answers.
For example: "How to do XYZ?" Our document is large and contains: "To do XYZ see ABCED topic".
I want the answer not to be "See ABCED topic", instead I want it to keep internally asking to get the final answer.
Any thoughts?