🚀 Welcome to Data Discovery! In this video, we dive into the world of Large Language Models (LLM) and how to integrate various proprietary models with LangChain. This tutorial, in Bengali, will guide you step by step through using some of the most powerful LLMs in the market, including OpenAI’s GPT, Google’s Gemini, Anthropic’s Claude, Meta’s LLaMA (using Groq inference), and Mistral’s large model.

We’ll explore:
• How to connect proprietary LLMs with LangChain for seamless integration
• Basic concepts of Retrieval-Augmented Generation (RAG) applications
• How RAG works and its key components like vector databases, document splitter and chunking strategies
• A hands-on demo of building a simple RAG project

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🔗 Code of this video:
https://github.com/Shaon2221/Generati...

🕒 Timestamps:
00:00 Connecting Proprietary LLMs (GPT, Gemini, Claude, LLaMA, Mistral)
10:30 LangChain Overview
15:45 Introduction to RAG Applications
22:14 Vector Databases, Splitter, and Chunking Strategies
30:20 RAG Project Demo

Context and information are drawn from various foundational NLP resources and research papers. The video references various LLM resources, including proprietary models and tools from OpenAI, Google, Meta, and Anthropic. Credit has been provided accordingly.

💬 Have questions or want to share your thoughts? Drop a comment below!
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