In this video, I will show you how you can create your own advanced scalable RAG system without writing a single line of code. We will be using something I have developed, called NyRAG. The code is open source and is available here: https://github.com/vespaai-playground/NyRAG Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: X: https://x/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

In this video, we will learn about what reciprocal rank fusion is, how it works and why it matters in hybrid search. What you will learn: ✔️ What reciprocal rank fusion means ✔️ How to calculate reciprocal rank ✔️ How to combine different ranking functions Code is available here: https://github.com/abhishekkrthakur/search Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

In this video, we upgrade our simple BM25 search engine into a Hybrid Search system using BM25 + vector embeddings with reciprocal rank fusion. We start from the existing bm25.py file and step through every change to build hybrid.py, explaining each line in simple language. What you will learn: ✔️ What hybrid search means ✔️ How to add embedding fields ✔️ How to use HNSW for vector similarity search ✔️ How to build semantic ranking ✔️ How to combine BM25 and vector ranking using Reciprocal Rank Fusion (RRF) ✔️ A clean step-by-step walkthrough ✔️ UI to search Code is available here: https://github.com/abhishekkrthakur/search Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Full video tomorrow! Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

In this video, we break down BM25, one of the most important ranking algorithms in information retrieval. BM25 powers almost all search engines determine which documents are most relevant to a user’s query. - What BM25 is and why it matters - How term frequency and inverse document frequency influence scoring - How document length affects ranking - What the key parameters k₁ and b actually do - How BM25 improves real-world search performance - BM25 in action! Code: https://github.com/abhishekkrthakur/search Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

In the previous video, we built a simple BM25-based search using Vespa, but we didn’t have a proper user interface. Today, we’re fixing that! 🎉 In this video, we vibe-code a modern and clean UI for our local search engine using: • FastAPI • HTML • CSS • JavaScript • PyVespa • Uvicorn By the end of the video, you’ll see a fully working search interface capable of querying 250K+ documents super fast: all running locally! 😎 What we did in this episode ✔️ Created a clean UI with responsive search input ✔️ Added result cards & styling for a modern look ✔️ Added a control to change maximum number of search results ✔️ Moved static assets into templates / static folders ✔️ Integrated search from the UI using PyVespa Example queries 🔍 “pizza with pineapple” 🔍 “learning python” 🔍 “New York hotel” The performance is amazing, thousands of results in milliseconds! Code: https://github.com/abhishekkrthakur/search Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek

Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :) Follow me on: Twitter: https://twitter.com/abhi1thakur LinkedIn: https://www.linkedin.com/in/abhi1thakur/ Kaggle: https://kaggle.com/abhishek