Let’s Talk Vectors: Databases, Stores & Indexing Explained

About the speaker https://www.linkedin.com/in/manish-sahani-001/ # 📢 **Meetup Announcement: Deep Dive into Vector Databases, Vector Stores & Indexing Techniques for RAG** As part of our ongoing **Hands-On RAG Series**, we now move to the next crucial building block in building real-world RAG applications: **Vectors, VectorDBs, Vector Stores, and Indexing Techniques**. Over the past sessions, we have taken a systematic, layered approach to demystify and master Retrieval-Augmented Generation: ### **🔹 Session 1** — *Foundational RAG Building Blocks* We explored the complete lifecycle of RAG: **chunking**, **embedding generation**, **vectorization**, **indexing**, and **retrieval workflows**, building strong conceptual clarity. ### **🔹 Session 2** — *Advanced Chunking Techniques* We went deeper into chunking strategies for **text, images, audio, video, and multimodal data**, understanding how chunk quality directly impacts RAG accuracy and retrieval performance. ### **🔹 Session 3** — *All About Embeddings* We dedicated an entire session to **embeddings**, covering types, models, quality, dimensionality, similarity metrics, optimization techniques, and best practices. *** # ⭐ **Now: Session 4 — Vectors, Vector Databases & Indexing for High-Performance RAG** This upcoming meetup focuses on one of the most critical components of any serious RAG system: **How do we store, search, and retrieve vectors efficiently at scale?** ### **What we will cover** #### **1️⃣ Why Vector Databases?** * Why traditional RDBMS systems fail for RAG workloads * What makes vector search unique * Real-world performance challenges in RAG projects #### **2️⃣ What Exactly Is a Vector?** * Understanding vector representation * How embeddings turn unstructured data into searchable geometry * Similarity search & distance metrics #### **3️⃣ Vector Databases vs. Vector Stores** * Key differences * Architectures, storage formats, indexing strategies * When to choose which #### **4️⃣ Popular Vector Databases & Tools** A comparative, practical guide across the ecosystem: **FAISS, Milvus, Pinecone, Chroma, Weaviate, Qdrant, Redis Stack, Vespa**, etc. For each, we'll cover: * Strengths & limitations * Licensing considerations * Scaling behaviours * Performance benchmarks * Ecosystem & integrations (LangChain, LlamaIndex, Python APIs, etc.) #### **5️⃣ Indexing Techniques (Core of This Session)** We will deep-dive into indexing algorithms used for vector search: * **IVF / IVF-PQ / IVF-OPQ** * **Flat indexing** * **HNSW (Hierarchical Navigable Small Worlds)** * **Annoy, ScaNN, LSH, PQ, OPQ** * Tradeoffs: accuracy vs. speed vs. memory * How indexing impacts RAG output quality * Which indexing to choose for which use-case #### **6️⃣ Real-World Experiences & What Actually Works** This session is **hands-on and practitioner-driven**. The experts presenting have **real-world implementation experience**, have “dirtied their hands,” and will share: * What worked for them * What failed * Practical, project-tested tips * Pitfalls they discovered the hard way * Optimization techniques for enterprise-grade RAG systems This is not theory or a high-level overview. These are **practical, actionable learnings** from people who build real systems every day. *** # 🎯 **Who Should Attend** * Engineers building RAG systems * Data scientists & ML practitioners * Architects & solution designers * Students exploring AI systems engineering * Anyone who wants to understand the backbone of modern intelligent search *** # 🎉 **Outcome** This session will equip you with **practical, working clarity** on: * How vectors are stored * How similarity search works under the hood * Why indexing matters for RAG * How to pick the right vector database for your use-case * How to avoid common mistakes while building vector-powered AI apps