Build a RAG Application that enables seamless interaction with any website, powered by LangChain, FAISS, Google Palm, Gemini Pro, and Streamlit.

In this video, we build an application designed to load data from URLs and generate answers based on the provided context using LLMs from Google.

Key Highlights:

- Langchain: Utilized to streamline the RAG application workflow.
- Data Retrieval from URLs: Explore methods for seamlessly extracting data from diverse websites.
- Generate text embeddings: Transform texts to embeddings using Google embeddings.
- Leverage LLMs from Google: Google Palm and Gemini-Pro are used for generating context-based responses.
- Efficient Similarity Searches with FAISS: Use FAISS to perform vector similarity searches.
- Frontend: Streamlit to create an intuitive and interactive interface for users.

By the end of this tutorial, you'll have a solid understanding of building a dynamic RAG application that can effectively interact with various websites, thanks to the powerful combination of FAISS, Google Palm, Gemini Pro, and Streamlit.

🔥 Don't forget to 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲, 𝐬𝐦𝐚𝐬𝐡 the 𝗹𝗶𝗸𝗲 𝐛𝐮𝐭𝐭𝐨𝐧, and 𝐭𝐮𝐫𝐧 𝐨𝐧 the 𝐧𝐨𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐥𝐥🔔 for more 𝗲𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 and 𝘁𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀. Let's embark on this coding journey together!

Links:
💻 GitHub repo for code: https://github.com/Eduardovasquezn/ra...
☕️ Buy me a coffee... or an iced tea: https://www.buymeacoffee.com/eduardov
👔 LinkedIn:   / eduardo-vasquez-n  

🚀 Timestamps:
0:00 Introduction
3:58 RAG URL Reader Workflow
6:53 Installation and Usage
08:54 Data Retriever
12:09 Split data into chunks
15:00 Text embeddings
18:17 FAISS
20:31 Load LLM
21:50 Create Chain
27:25 Create functions for the app
35:52 Streamlit app

#LLM #AI #GenerativeAI #LangChain #Streamlit #Gemini #geminipro #GooglePalm #webscraping #FAISS #tutorial #python