GenAI QNA Chatbot with Blog URL and PDF Using LangChain RAG and Google Gemini Pro LLM | Streamlit UI

Do you want to know how to get answers from any URL with just one click? Or talk to PDF documents to extract insights effortlessly? Welcome to Part 6 of the Learn RAG From Scratch series! In this exciting video, we’ll create two amazing Generative AI-enabled apps: 1. Chat with URLs: Extract data from a URL, store it in vector embeddings using FAISS, and get intelligent responses using RAG and LLMs. 2. Chat with PDFs: Interact with PDF documents, extract key information, and generate insightful answers using Google Gemini Pro 1.5 and FAISS vector databases. Complete Course on RAG Explained with its Architecture: https://bit.ly/3Zl47KD What’s Covered in This Video? 1. A quick introduction to Retrieval-Augmented Generation (RAG). 2. Step-by-step coding tutorial for creating these applications. 3. Tools Used: a. LLM: Google Gemini Pro 1.5 b. Embedding Model: Google GenerativeAI Embeddings c. Vector Database: FAISS Whether you're an AI enthusiast, a data scientist, or a developer curious about how to integrate AI into practical applications, this video is for you! Upcoming Project Teaser: Next up, we’ll build a GenAI-enabled Anime/Manga Chatbot that lets fans discuss theories, ask about characters, and get recommendations. Anime lovers, stay tuned! 🎉 Join this channel to get access to perks: https://www.youtube.com/channel/UC4RZP6hNT5gMlWCm0NDzUWg/join Don’t forget to: Like this video, subscribe to the channel and Comment your thoughts or questions To get the Source Code, Follow me on GitHub: https://bit.ly/3gg07Uc Book your call with me at topmate.io and learn how to harness the latest technologies power and speed up your learning process. Book your call at https://bit.ly/43TLDCD Follow me on Medium for the latest blogs and projects: https://bit.ly/3JGXqwc Playlists that make you skilled up 1. GenAI Full Course with LLM Fine Tuning and Evaluation: https://bit.ly/4bJwZla 2. Learn RAG from scratch with GenAI projects: https://bit.ly/3Zl47KD 3. Latest AI/GenAI Research Papers Explained: https://bit.ly/4huqEMT 4. RAG and LLM Use Cases in Finance Domain Projects: https://bit.ly/3AGSRQm 4. Prompt Engineering: https://bit.ly/42v376M 5. Financial Data Analysis and Financial Modelling: https://bit.ly/3OCWI5O 6. Artificial Intelligence Projects: https://bit.ly/3L8lhEi 7. Predict IPL 2023 Winner (End to End Data Science Project): https://bit.ly/3BfC3N9 8. Explainable AI (XAI) Machine Learning: https://bit.ly/3gsuIxb 9. Face Recognition: https://bit.ly/2YphpHm Youtube Tags: Krish Naik genai, Krish Naik llm, Krish naik rag, krish naik vector databases, Krish Naik Python tutorial, Krish naik explainable ai, Krish Naik data science, Krish Naik statistics, machine learning full course, machine learning tutorial, machine learning interview questions, machine learning projects in Python, data science for beginners, data science project, data science full course, data science interview questions, data science interview, machine learning interview questions, statistics interview questions, Python interview questions, interview questions, leetcode questions, interview preparation for FAANG, FAANG Interview questions, Google data science Interview questions, Amazon data scientist interview, Meta data scientist interview questions, learn rag from scratch, rag tutorials, rag llm tutorials, rag llm project, genai projects, chat with websites using llm and rag, chat with pdf llm rag,