Mohamed Samy’s Post

Just finished " LangChain for LLM Application Development! " checkit out : https://lnkd.in/dyAcqfKD #GenAi #LLM #Langchain #RAGs #Agents

I’m thrilled to share my latest blog, “Some of the Useful Functionality Provided by Langchain”! 🎉 In this post, I dive into the functionalities of LangChain that I find most valuable, complete with practical code snippets. From reusing prompts to real-time responses, I’ve explored features that can truly enhance your Langchain experience. I’d love to hear your thoughts and feedback! If you’ve come across any other Langchain features that you find incredibly useful, please share them in the comments. Let’s learn and grow together! 🚀 #Langchain #LangchainAgents #LangchainChain #LLMApplication #LLM #LLMProduct #OpenAI #StructuredOutput #StreamingResponse #Batching

Just finished the LangChain for LLM Application Development course by DeepLearning.AI 🚀 This course has given me insights into building and deploying LLM applications. Excited to apply these newfound skills! #DeepLearningAI #LangChain #LLMApplicationDevelopment #NewSkills

The exploration of LangChain has significantly enhanced my approach to search agent development, facilitating the handling of complex queries and improving the overall user experience. The creation of a search agent using LangChain operates akin to a digital assistant that effectively interprets and responds to user inquiries. Each line of code within LangChain represents a commitment to optimizing information retrieval processes, resulting in more intuitive search functionalities. Engaging with LangChain has reshaped my perspective on search agents, fostering innovation and creativity in coding practices. Ultimately, the process of developing a search agent in LangChain is reminiscent of assembling a puzzle, where each component clarifies user requirements and preferences. #LangChain #SearchAgent #TechInnovation #UserExperience

Completed the Langchain for LLM Application Development. In this sort of course, I have learned different components. - Langchain: Prompts. - Langchain: Memory. - Langchain: Chains. - Langchain: Question and Answer. - Langchain: Agents. - Langchain: Evaluation.

Super important course to understand the basics of LangChain.

I have successfully completed the course on "Functions, Tools and Agents with LangChain." #langchain #deeplearningai

In this article, we discuss one such framework known as retrieval augmented generation (RAG) along with some tools and a framework called LangChain. Check it out! #SingleStore

In this article, we discuss one such framework known as retrieval augmented generation (RAG) along with some tools and a framework called LangChain. Check it out! #SingleStore

Nice initial course on LangChain for LLM Application development and how to see into working of LLMs step by step. Major key takeaways from the course - the documents can be split into chunks, different type of buffer memory exists, Chains can be run in parallel or sequential, Evaluation of model results is important (to be accepted or not). Agents are evolving. 1 thing which stood out was to store entire websites like Wikipedia and get any answers from it :) #llm #langchain #learneveryday