WhatsApp AI Agent Tutorial 3: Unlocking Ava's Memories | Intro to RAG & Vector DBs скачать в хорошем качестве

WhatsApp AI Agent Tutorial 3: Unlocking Ava's Memories | Intro to RAG & Vector DBs

In this third tutorial, we unlock Ava’s memories by exploring text embeddings and the Retrieval Augmented Generation (RAG) framework. First, we show how short‑term memory (using SQLite) preserves immediate context in Ava’s graph. Then, we dive into long‑term memory—demonstrating how RAG leverages embeddings to retrieve Ava’s historical interactions for richer, more personalized responses. Finally, we walk through setting up a free Qdrant Cloud cluster and obtaining an API key, so you can store and retrieve long‑term memory at scale. By the end, you’ll understand the difference between short‑term and long‑term memory in AI agents, how text embeddings power RAG, and how to seamlessly integrate these concepts into a real‑world WhatsApp agent. Let’s get started! 📌 Links: • Miguel’s Newsletter: https://theneuralmaze.substack.com • Project GitHub repo: https://github.com/neural-maze/ai-com... • Qdrant Cloud: https://cloud.qdrant.io Chapters: 00:00 Intro 01:14 Short-term Memory 03:09 Long-term Memory 01:45 Agentic Frameworks 05:24 RAG Explanation 06:16 Retrieve 06:40 Semantic Search and other terms 08:22 Vector DB – Zooming In 10:58 Augment & Generate 12:36 Chunking 13:09 Choosing Embedding Model 14:19 Vector Database Providers 15:00 Why Qdrant? 16:12 Code Overview 22:02 Qdrant Cloud Overview 23:43 Outro #aiagents #aiagent #qdrant #whatsappchatbot #whatsappbot