📌 Detailed Explanation (RAG-focused)

🔍 What is an Index in a Vector Database?

A vector index is a data structure that enables fast similarity search over high-dimensional vectors (embeddings).

In RAG systems, indexes decide:

• ⚡ How fast retrieval is

• 🎯 How accurate the context is

• 📈 How well the system scales

Embeddings give meaning — indexes give speed.

🧱 1. Flat Index (Brute-Force / Exact Search)

📌 How it works

• Stores all vectors in raw form

• Compares query vector with every vector

• Uses cosine / L2 / dot product

✅ Pros

• 100% accurate

• No approximation

• Simple

❌ Cons

• Extremely slow at scale

• O(N) complexity

🧠 Used when

• Small datasets (<50k vectors)

• Benchmarking

• Validation

🛠 Used in

• FAISS IndexFlatL2

• Testing RAG pipelines

🧱 2. IVF (Inverted File Index)

📌 How it works

1. Clusters vectors into centroids

2. Each centroid → inverted list

3. Query searches only nearest clusters

🧠 Analogy

📚 Library shelves — you search only the relevant shelf, not the entire library.

✅ Pros

• Much faster than flat

• Scales to millions

❌ Cons

• Approximate

• Needs tuning (nlist, nprobe)

🛠 Used in

• FAISS (IVF)

• Milvus

• Large-scale RAG

🧱 3. HNSW (Hierarchical Navigable Small World) ⭐ GOLD STANDARD

📌 How it works

• Builds a multi-layer graph

• Top layers = fast jumps

• Bottom layer = accurate neighbors

🧠 Analogy

🛣️ Google Maps: highways → city roads → local streets

✅ Pros

• Extremely fast

• High accuracy

• Best for real-time RAG

❌ Cons

• Higher memory usage

• Slower index build

🛠 Used in

• FAISS

• Qdrant

• Weaviate

• Pinecone (conceptually)

👉 Default choice for production RAG

🧱 4. Product Quantization (PQ)

📌 How it works

• Splits vectors into sub-vectors

• Each sub-vector quantized

• Stores compressed representation

🧠 Goal

Reduce memory footprint drastically

✅ Pros

• Very memory efficient

• Enables billion-scale search

❌ Cons

• Loses accuracy

• Approximate distances

🛠 Used in

• FAISS PQ

• Mobile / edge AI

• Huge corpora

🧱 5. IVF + PQ (Hybrid Compression Index)

📌 How it works

• IVF narrows search space

• PQ compresses vectors inside clusters

✅ Pros

• Massive scale

• Low memory

• Fast

❌ Cons

• Lower accuracy than HNSW

🛠 Used in

• FAISS (IVFPQ)

• Very large enterprise datasets

🧱 6. Annoy (Tree-Based Index)

📌 How it works

• Builds multiple random projection trees

• Search across trees

✅ Pros

• Simple

• Disk-friendly

❌ Cons

• Less accurate than HNSW

• Static (no frequent updates)

🛠 Used in

• Spotify recommendations

• Lightweight systems

🧱 7. ScaNN (Google)

📌 How it works

• Combines partitioning + quantization

• Optimized for TPUs / GPUs

✅ Pros

• Extremely fast at scale

❌ Cons

• Complex

• Less flexible

🛠 Used in

• Google internal systems

• Large ML pipelines

📊 Comparison Table (Interview-Ready)

🧠 Which Index Should You Use in RAG?

✅ Recommended Stack

Embeddings → HNSW → Hybrid Search → MMR → LLM

Simple Rule:

• 🔹 <100k vectors → Flat / HNSW

• 🔹 100k–10M → HNSW

• 🔹 10M+ → IVF + PQ

🎯 One-Line Summary

Vector indexes are the performance engine of RAG — and HNSW is the industry’s most trusted index for fast, accurate semantic retrieval.

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