What is an Index in a Vector Database?

4 min readMay 21, 2025

In the age of Artificial Intelligence, where machines strive to understand language, images, and our world, a powerful transformation is taking place. Instead of merely storing text or numbers, we now store meaning. And how do we do that? With vectors.

Yet, as we wade into the waters of semantic search, recommendation engines, retrieval-augmented generation (RAG), and intelligent agents, one humble hero ensures that we don’t drown in oceans of data: the index.

In this lyrical exploration, let’s uncover the essence of an index in vector databases, and how various systems like FAISS, LlamaIndex, and others craft their own versions of this unsung wizard.

🎯 Step 1: What Are Vectors and Vector Databases?

Before we admire the index, we must first understand the world it lives in.

🧩 What is a Vector?

A vector is a list of numbers — a numerical representation of something meaningful. Words, images, products, code snippets — all can be converted into vectors by deep learning models (like OpenAI’s embeddings, CLIP, SentenceTransformers, etc).

For example:

The word “dog” becomes: [0.3, -0.1, 0.8, 0.7, ...]
A sentence like “I love pizza” becomes: [0.21, 0.13, 0.44, 0.29, ...]

These vectors live in a high-dimensional space, where closeness = similarity.

📦 What is a Vector Database?

A vector database stores these vectors and allows for efficient similarity search. It doesn’t just care what you ask — but what you meant.

Popular vector databases include:

FAISS (Facebook AI Similarity Search)
Weaviate
Pinecone
Qdrant
Milvus

You feed it vectors + metadata, and it helps you retrieve what’s most similar.

🧭 Step 2: Why Do We Need Indexes?

Let’s say you have 10 million vectors. Now, someone sends in a new query vector and says, “Find me the most similar ones.”

Do you:

Check all 10 million one-by-one?
Use a clever trick to narrow down the search?

🥁 Enter the index — a data structure that makes similarity search fast and efficient.

🎒 Analogy Time

A vector database is a giant library.

Each vector = a book’s “soul”
The index = the Dewey Decimal System that lets you find related books in seconds.

Without an index: You wander aisle after aisle, scanning every book.
With an index: You go straight to the shelf.

🧠 How Indexes Work (in Concept)

Indexes break down the vector space to avoid brute-force comparisons. Broadly, they do this using one or more techniques:

Clustering vectors into groups (so you search fewer)
Graph-based navigation (like how you’d ask for directions)
Quantization (compressing vectors to save space)
Trees (partitioning space hierarchically)

Each method makes trade-offs between accuracy, speed, and memory usage.

⚙️ Types of Indexing Techniques (With FAISS)

Let’s understand the most famous library in this realm: FAISS by Meta AI.

🔍 1. Flat (Brute Force Index)

Checks every single vector.
100% accurate, but slow.
Use only for small datasets.

import faiss
index = faiss.IndexFlatL2(dim)  # L2 = Euclidean distance

🌐 2. IVF (Inverted File Index)

Clusters vectors into “centroids”
First finds nearest cluster, then searches within
Much faster for large datasets

quantizer = faiss.IndexFlatL2(dim)
index = faiss.IndexIVFFlat(quantizer, dim, nlist)

🌉 3. HNSW (Hierarchical Navigable Small World Graph)

Builds a navigable graph of vectors
Great for high recall and low latency

index = faiss.IndexHNSWFlat(dim, M=32)

🧮 4. PQ (Product Quantization)

Compresses vectors into compact codes
Ideal for large datasets where memory is tight

quantizer = faiss.IndexFlatL2(dim)
index = faiss.IndexIVFPQ(quantizer, dim, nlist, m, 8)
index.train(xb)  # xb is your training data
index.add(xb)    # add vectors after training

🏗️ What About Other Index Frameworks?

🧠 LlamaIndex (formerly GPT Index)

LlamaIndex isn’t a vector index per se — it’s a framework for building data interfaces for LLMs.

It integrates with vector stores like FAISS, Pinecone.
Helps structure documents, create chunks, and route queries.
Think of it as the librarian who decides which index to use and how.

So, while FAISS builds and manages the index, LlamaIndex acts like a middleware that gives LLMs better access to these indexes.

🕸️ Weaviate / Qdrant / Pinecone

Each of these vector DBs has its own built-in indexing system:

🧪 Practical Use Case: Retrieval-Augmented Generation (RAG)

Imagine you’re building a chatbot with memory.

You store past knowledge (docs, chat logs) as vectors.
When the user asks something, you embed the query.
Use an index to retrieve similar content.
Pass this content to the LLM to generate an answer.

Here, the index is the bridge between “raw memory” and “relevant recall”.

🧵 Final Thoughts: The Quiet Power of Indexes

In the sprawling land of vectors — where meaning is no longer in words but in numbers — the index is the humble cartographer. It draws the maps, builds the shortcuts, guides the traveler.

Whether you’re exploring FAISS’s flexible options, riding Weaviate’s managed highways, or orchestrating LLMs with LlamaIndex — understanding indexing is your first step toward mastery.

So next time your AI system responds in seconds, remember: it wasn’t just the model, or the data — it was the index, silently working behind the curtain, like a wizard with a compass.