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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.

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# ⭐ 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.

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# 🎯 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

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# 🎉 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

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