Indexing vector db

📌 Does the way embeddings are arranged in vector space depend on the indexing mechanism?

Short answer: ➡️ No — the representation of embeddings in vector space is independent of the indexing mechanism. ➡️ But how they’re organized for search (their access paths, partitions, or shortcuts) depends on the indexing mechanism.

📖 Detailed breakdown:

1️⃣ Embedding representation (vector space)

When you generate an embedding — say a 1536-dimensional vector using OpenAI or a 768-dim vector using BERT — it’s a point in a multi-dimensional space.

This position in vector space is solely determined by the embedding model and the input data. For example:

  • “airplane” → [0.234, 0.849, … 0.125]
  • “cockpit” → [0.627, 0.231, … 0.922]

This numerical representation is fixed before indexing and remains the same regardless of which index type you choose.

The indexing mechanism doesn’t change the embedding itself. It affects how those points are organized internally for faster or more memory-efficient retrieval.

Different FAISS indexes arrange these points in different data structures:

Index Type Organization Strategy
IndexFlatL2 No structure — brute-force search
IndexIVFFlat Clusters points into Voronoi cells (inverted lists)
IndexHNSWFlat Organizes points in a navigable graph
IndexPQ Compresses points into low-bit quantized buckets

But in all cases, the actual position of an embedding in the vector space (the coordinate values) is the same. The index only defines how to quickly navigate to nearby points when searching.

📊 📌 Visual intuition:

Imagine a 3D cloud of points (representing vectors):

  • 📌 The cloud shape and point positions are defined by the embedding model.
  • 🗺️ The index decides how to lay down roads, pathways, or shortcuts between those points to make neighbor-finding efficient.

Changing the index is like changing the map/grid you lay on top of that cloud — but not moving the points themselves.

✅ Conclusion:

Aspect Determined by
Embedding position in vector space Embedding model & data
Organization of embeddings for search Indexing mechanism
Similarity measurement (L2, cosine) Search algorithm / index config

Bonus:

If you transform embeddings before indexing (e.g. dimensionality reduction like PCA, quantization, etc.), that would move their positions in vector space — but that’s a preprocessing step outside the index type itself.

📌 Is FAISS just a library to do indexing?

➡️ Yes — FAISS is primarily a library for building and querying efficient vector indexes for similarity search. It helps you:

  • Store vector embeddings in an index
  • Search for nearest neighbors efficiently

But by itself, FAISS is not a full-fledged vector database. It’s a library you embed into your own system for vector search capability.

📌 Can FAISS be used in any vector DB?

➡️ Not directly — but many modern vector databases either:

  • Use FAISS internally
  • Or allow FAISS as one of their indexing backends

📖 Example:

Vector Database Uses FAISS? Other Index Options
Pinecone No (built its own optimized engine) Native
Weaviate No (uses HNSW by default) HNSW
Milvus ✅ Supports FAISS and Annoy FAISS, HNSW, IVF, etc.
Chroma ✅ Uses FAISS as backend FAISS
Qdrant No (has native HNSW implementation) HNSW

So — you can use FAISS directly inside your Python app, or with databases like Milvus or Chroma. But vector DBs like Pinecone, Weaviate, and Qdrant use different indexing mechanisms (HNSW is super popular because it’s fast and scalable for production).

📌 So what’s the difference then?

Feature FAISS Vector DB (like Pinecone, Milvus)
Indexing ✅ Supports many types ✅ Typically support HNSW/FAISS/others
Vector storage In-memory (or on disk with extra work) Persistent, distributed storage
Scalability Local, single machine or manually distributed Cloud-native, horizontally scalable
APIs Python, C++ REST, gRPC, and Python/Java SDKs
Metadata storage ❌ No native support ✅ Can store and query metadata
Multi-user, multi-tenant ❌ ✅

📌 How people use FAISS today:

  • In small-scale, local vector search systems embedded inside apps.
  • As the indexing backend for RAG applications in LangChain or custom Python projects.
  • Inside larger systems like Milvus where it runs distributed.

✅ Summary:

  • FAISS is a vector indexing library — not a full vector DB.
  • It can be plugged into certain vector DBs like Milvus and Chroma.
  • For large, distributed, persistent, metadata-supported, multi-user use cases, you’d typically use a vector DB, which may or may not use FAISS under the hood.

Excellent question, Kashyap — FAISS is popular, but it’s just one player in the world of vector indexing libraries. Let’s chart out the landscape.

📌 Popular Vector Indexing Libraries (FAISS alternatives)

Library Language Highlights Use Cases
Annoy C++ / Python Simple, lightweight, builds on-disk indexes Good for static datasets, small to medium size
HNSWlib C++ / Python Super fast Approximate Nearest Neighbors using HNSW graphs Real-time, high-performance search
ScaNN C++ / Python Google’s optimized library for large-scale nearest neighbor search Cloud-scale search systems
NMSLIB C++ / Python Highly flexible library supporting many indexing algorithms including HNSW, SW-graph, etc. Research experiments, custom ANNS systems
Vespa Java / C++ Open-source vector search engine with built-in ANN support Enterprise search, production web services
Elasticsearch KNN plugin Java Adds vector search with HNSW to Elasticsearch Existing Elasticsearch deployments needing vector search
Milvus C++ / Go / Python Full vector database — supports FAISS, HNSWlib, and custom indexes Large-scale, distributed, enterprise search
Qdrant Rust Native HNSW-based vector database engine with a RESTful API Fast, distributed, production-grade search

📖 Quick Breakdown of Popular Indexing Algorithms

Algorithm Used in Description Trade-offs
IVF (Inverted File) FAISS Clusters data, searches only nearby clusters Fast, approximate
HNSW (Hierarchical Navigable Small World) HNSWlib, NMSLIB, Milvus, Vespa, Qdrant Graph-based navigation Extremely fast, very accurate
PQ (Product Quantization) FAISS Compresses vectors to lower bits for efficient storage Small size, lower accuracy
Brute-force (Flat) FAISS, Annoy No approximation, full scan Slow for large data, 100% accurate
LSH (Locality Sensitive Hashing) NMSLIB, older systems Uses hash functions for similarity Fast for high-dim, approximate

📌 When would you choose what?

Use Case Library Recommendation
Small dataset, fast prototyping Annoy
Large, distributed, scalable search Milvus, Qdrant
Extremely fast, real-time, in-memory search HNSWlib
Google-scale vector search ScaNN
Adding vector search to Elasticsearch Elasticsearch KNN plugin

✅ Summary:

  • FAISS is one of several popular vector indexing libraries.
  • Libraries like HNSWlib and ScaNN often outperform FAISS in certain use cases.
  • Full vector DBs like Milvus and Qdrant integrate these libraries and add persistence, APIs, scaling, and metadata management.
  • The choice depends on your dataset size, latency needs, deployment environment, and whether you need cloud scaling.
graph LR A([Start]) --> B{Is dataset small under 1M vectors and static?} B -- Yes --> C[Use Annoy disk or HNSWlib in-memory] B -- No --> D{Need scalable, production-ready vector DB?} D -- Yes --> E{Which one?} E -- Milvus or Qdrant --> F[Use Milvus FAISS/HNSW or Qdrant native HNSW] E -- Elasticsearch --> G[Use Elasticsearch KNN plugin] D -- No --> H{Real-time, in-memory search?} H -- Yes --> I[Use HNSWlib] H -- No --> J{Cloud-scale Google-scale?} J -- Yes --> K[Use ScaNN] J -- No --> L[Use FAISS default] 
Start
  ↓
Is dataset small?
  ├─ Yes → Annoy/HNSWlib
  └─ No → Need scalable DB?
          ├─ Yes → Which one?
          │       ├─ Milvus/Qdrant → Use them
          │       └─ Elasticsearch → Use its KNN plugin
          └─ No → Real-time in-memory?
                  ├─ Yes → HNSWlib
                  └─ No → Cloud-scale?
                          ├─ Yes → ScaNN
                          └─ No → FAISS

Start
  │
  ├──> Is your dataset small (< 1M vectors) and static?
  │       │
  │       ├── Yes → Use **Annoy** (if disk-based index needed)
  │       │           or **HNSWlib** (if in-memory and very fast)
  │       │
  │       └── No
  │
  ├──> Do you need a scalable, production-ready vector DB?
  │       │
  │       ├── Yes
  │       │     ├── Want open-source + FAISS/HNSW support? → **Milvus**
  │       │     ├── Want native HNSW, lightweight, and fast? → **Qdrant**
  │       │     └── Already using Elasticsearch? → **Elasticsearch KNN plugin**
  │       │
  │       └── No
  │
  ├──> Are you doing real-time, in-memory vector search?
  │       │
  │       ├── Yes → **HNSWlib**
  │       └── No
  │
  ├──> Is your dataset cloud-scale and Google-level huge?
  │       │
  │       ├── Yes → **ScaNN**
  │       └── No
  │
  └──> Default to **FAISS**
Use Case Recommended Library / DB
Small, static dataset (disk) Annoy
Small, static dataset (RAM) HNSWlib
Large, scalable, cloud-native Milvus, Qdrant
Real-time, in-memory search HNSWlib
Elasticsearch users Elasticsearch KNN plugin
Huge, cloud-scale applications ScaNN
Local RAG prototypes, general-purpose FAISS

✅ Recap:

  • FAISS is the default workhorse.
  • HNSWlib beats FAISS on real-time search for small-medium data.
  • Milvus and Qdrant are cloud-ready DBs for scalable, distributed workloads.
  • ScaNN shines for Google-scale workloads.
  • Annoy is great for on-disk, simple, and static indexes.

✅ Indexing Libraries

Name Index type(s) Approximate / Exact Cloud native? Notes
FAISS IVF, HNSW, PQ, Flat Both (configurable) No In-memory
Annoy Random projection trees Approximate No Disk-based
HNSWlib HNSW Approximate No In-memory
ScaNN Partition+Asymmetric Hash+Reordering Approximate No Google open-source

✅ Vector Databases

Name Indexing Mechanism Cloud native? Notes
Pinecone HNSW (managed) Yes Distributed, managed
Milvus FAISS / HNSW Yes/No (hybrid) Open source
Qdrant HNSW (native) Yes/No Metadata-rich
Weaviate HNSW Yes/No Modular plugins
Vespa HNSW Yes/No Integrates search & inference
flowchart TD A[Raw Documents] --> B[Embedding Function OpenAI, HuggingFace, etc.] B --> C[Vector Embeddings] C --> D[Vector Store e.g., LangChain FAISS store] D --> E[FAISS Index in-memory] C --> F[Vector Store e.g., LangChain Pinecone store] F --> G[Pinecone Vector DB Cloud, persistent] E -->|Similarity Search| H[Query Result] G -->|Similarity Search| I[Query Result] style A fill:#fef3c7,stroke:#facc15,stroke-width:2px style B fill:#bfdbfe,stroke:#3b82f6,stroke-width:2px style C fill:#ddd6fe,stroke:#8b5cf6,stroke-width:2px style D fill:#bbf7d0,stroke:#22c55e,stroke-width:2px style F fill:#bbf7d0,stroke:#22c55e,stroke-width:2px style E fill:#fcd34d,stroke:#f59e0b,stroke-width:2px style G fill:#fcd34d,stroke:#f59e0b,stroke-width:2px style H fill:#fecaca,stroke:#f87171,stroke-width:2px style I fill:#fecaca,stroke:#f87171,stroke-width:2px

✅ Summary:

Feature Flat IVF (Inverted File Index) HNSW (Graph-based Index)
Type of Search Exact Approximate (cluster-based) Approximate (graph-based traversal)
Speed Slow (linear scan) Fast (search only in top clusters) Very Fast (graph walk)
Dataset Size Recommended Index
UPTO 1L IndexFlatL2 or IndexFlatIP
UPTO 1M IndexIVFFlat or IndexHNSWFlat
> 1M IndexIVFPQ or IndexHNSWFlat

Things to keep in mind when choosing a vector indexing library or database:

  1. Index - flat, Inverted file index (IVF) (cluster based), HNSW (Graph based), PQ, etc.
  2. Similarity Search - L2 distance, cosine similarity, etc.

Workflow

graph LR A(PDF) --> B(Pages) B --> C[Chunking] C --> D(Document Object) --> D(meta data) D --> E(Page content) D --> C[Vector Embeddings] C --> D[Vector Store e.g., FAISS, Pinecone] D --> E[Similarity Search] E --> F[Query Result] style A fill:#fef3c7,stroke:#facc15,stroke-width:2px style B fill:#bfdbfe,stroke:#3b82f6,stroke-width:2px style C fill:#ddd6fe,stroke:#8b5cf6,stroke-width:2px style D fill:#bbf7d0,stroke:#22c55e,stroke-width:2px style E fill:#fcd34d,stroke:#f59e0b,stroke-width:2px style F fill:#fecaca,stroke:#f87171,stroke-width:2px