🔥 Complete Semantic Caching Tutorial for Beginners | Explained in Tamil | GenAI | RAG | AI Agents

AI with Akash · Beginner ·🔍 RAG & Vector Search ·6d ago

Skills: RAG Basics90%

Key Takeaways

Building a semantic caching system using GenAI, RAG, and AI agents

Original Description

Level: Beginner to Advanced All Complete Tutorials for Beginners: RAG: https://www.youtube.com/watch?v=4Qp5D5hcE4A CrewAI Agents: https://www.youtube.com/watch?v=PgPo9WHQczw LangGraph Agents: https://www.youtube.com/watch?v=vVtzWXTv3vM MCP: https://www.youtube.com/watch?v=2wyaDf04n_I FastAPI: https://www.youtube.com/watch?v=DRPpaFNpS-8 Fine-Tuning: https://www.youtube.com/watch?v=gOOS3k-7t6U Embedding Models: https://www.youtube.com/watch?v=5nHH_6PXgEo Deep Research Agent: https://www.youtube.com/watch?v=jlPbVm2bigA Socials: 1:1 Mentorship : https://topmate.io/akash_balakrishnan/706031 LinkedIn: https://linkedin.com/in/akashb22 Instagram: https://instagram.com/ai.with.akash Github: https://github.com/akash-balakrishnan-22/semantic-caching Links: VS Code: https://code.visualstudio.com/download UV: https://docs.astral.sh/uv/getting-started/installation/ Docker: https://www.docker.com/products/docker-desktop/ LLM Pricing Link: https://artificialanalysis.ai/ Redis Data Structrues: https://redis.io/technology/data-structures/ Redis VL: https://redis.io/docs/latest/integrate/redisvl/ 📖 About This Video: Welcome to this hands-on Semantic Caching series for AI and LLM applications. In this playlist, you'll learn how semantic caching can dramatically reduce LLM costs, improve response times, and enhance user experience in real-world AI systems. We start by understanding LLM pricing, token-based costs, and the trade-off between model intelligence and price. You'll also see why traditional caching requires exact string matches and where it falls short for modern AI applications. As the series progresses, we'll explore Retrieval-Augmented Generation (RAG) architecture with semantic caching, build a customer support AI agent, and set up a complete development environment using VS Code, UV, and Docker. You'll gain a solid understanding of Redis, vector databases, cosine similarity, semantic search, fuzzy matching, cache expiration (TTL), and multiple approaches to imple

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