LangChain Expression Language (LCEL) | Langchain Tutorial | Code

Dive into the world of LangChain Expression Language (LCEL) with our comprehensive tutorial! In this video, we explore the core features of LCEL, focusing on how LangChain elements implement the runnable interface for effective chaining and independent execution.
We demonstrate practical uses of the pipe operator for seamless operation chaining and illustrate the power of the invoke method in executing individual LangChain components. This video is perfect for developers and enthusiasts eager to enhance their skills in AI and language model integration. Join us for an insightful journey into LCEL and unlock new possibilities in text processing and AI!
Code and Explanation : blog.futuresmart.ai/unlocking...
If you're curious about the latest in AI technology, I invite you to visit my project, AI Demos, at www.aidemos.com/. It's a rich resource offering a wide array of video demos showcasing the most advanced AI tools.
For even more in-depth exploration, be sure to visit my KZbin channel at / @aidemos.futuresmart . Here, you'll find a wealth of content that delves into the exciting future of AI and its various applications.
🚀 Top Rated Plus Data Science Freelancer with 8+ years of experience, specializing in NLP and Back-End Development. Founder of FutureSmart AI, helping clients build custom AI NLP applications using cutting-edge models and techniques. Former Lead Data Scientist at Oracle, primarily working on NLP and MLOps.
💡 As a Freelancer on Upwork, I have earned over $100K with a 100% Job Success rate, creating custom NLP solutions using GPT-3, ChatGPT, GPT-4, and Hugging Face Transformers. Expert in building applications involving semantic search, sentence transformers, vector databases, and more.

[@prasad_yt]

Nice simplified explanation ❤

[@MuhammadFaizanMumtaz3]

Sir! Your doing great job

[@andaldana]

Great tutorial - thanks!

[@benepstein3970]

Thanks, subscribed!

[@FutureSmartAI]

Awesome, thank you!

[@mushinart]

First of all , thank you for the amazing way you explain stuff.... Its elegant...now i have only one question when we userd the retriever to assign it's value to the context variable, i assume the retriever will pass all of the documents in the rag list to the llm so that when it validates the question,it will use the whole rag documents that are all being passed as a context in the prompt. Am i right? ... Will it be a good idea if we created a chain to first take the question to query the rag and get just the needed context then pass it with the question again to form a human understandable answer from the llm ? ....im asking to see if im understanding it right or not .... Thanks man

[@thumarzeel]

Awesome buddy

[@FutureSmartAI]

Thanks ✌️

[@muhammedaslama9908]

AzureOpenAi chat model doesn't seem to support LCEL. Am I doing something wrong?

[@Tushii]

Is there a way in which I could batch invoke a list of text files ? I want to extract certain texts from each file using openai? Or would I have to do it one by one and loop

[@FutureSmartAI]

you can extract fill text and pass it in batch

[@Tushii]

@@FutureSmartAI cool, thanks, I shall try it out