Youtube Link-What’s our vector, Victor?

What’s our vector, Victor? -Reddit

“What’s our vector, Victor?”: What does this mean?

What Even Is AISS?

Approximate Incremental Similarity Search (AISS) = efficiently finding items in a database that are similar to a given query, especially as new data is (continuously) added.

Imagine you have a massive collection of high-dimensional data—think of it like a giant library where instead of books, you have complex mathematical vectors representing images, audio, and text.

AISS (Approximate Incremental Similarity Search) is a database that specializes in storing, searching, and retrieving these vectors at lightning speed.

In simple terms:

• Regular databases deal with rows and columns. 🥱
• Vector databases like AISS deal with embeddings and similarity search. 🚀

Why Should You Care?

Because if you’ve ever wondered how AI systems recognize faces, recommend movies, or understand your garbled voice commands, AISS (or similar vector databases) is the magic behind the scenes.

Let’s say you have a billion images.

Instead of searching for an exact match, AISS lets you find the most similar images in milliseconds. It’s like Shazam, but for any kind of data.

Key Features That Make AISS Cool

• Superfast Similarity Search – Finds stuff that looks or sounds similar, not just exact matches.
• Scalable – Works whether you’re dealing with a few thousand vectors or a few billion.
• Optimized for AI & ML – Perfect for neural network-powered applications.
• Efficient Storage – Stores high-dimensional data without making your hard drive cry.

How Does AISS Work?

AISS uses Approximate Nearest Neighbor (ANN) search to quickly find similar vectors. Instead of brute-force scanning everything (which would be painfully slow), it uses optimized indexing techniques like:

• Hierarchical Navigable Small Worlds (HNSW) 🌎
• Product Quantization (PQ) 🧮
• Locality-Sensitive Hashing (LSH) 🏷️

Each of these methods helps chop down the search time while maintaining accuracy.

So instead of searching for a needle in a haystack, AISS organizes the haystack so you can find that needle in no time.

Where Is AISS Used?

• AI-powered search engines – Like Google Images or reverse image search.
• Recommendation systems – “You liked Inception? Here are 10 more movies that will make your brain hurt.”
• Fraud detection – Finding similar patterns in transaction data.
• Autonomous systems – Helping self-driving cars recognize objects.
• Chatbots & NLP – Powering AI that actually understands context (well, sometimes).

Should You Use AISS?

If your app involves anything AI, ML, or similarity search, AISS is a useful tool.

It’s like having a librarian that instantly finds the closest match to what you’re looking for—except this librarian runs on caffeine and algorithms.

🔑 Key Ideas Behind AISS

Ok, so What DB Engine can I use?

Approximate Incremental Similarity Search (AISS) = efficiently finding items in a database that are similar to a given query, especially as new data is (continuously) added.

While no database is explicitly branded as an “AISS database,” several vector databases and libraries provide excellent support for approximate similarity search with incremental updates.

1. Milvus

Milvus is an open-source vector database designed for scalable similarity search.

It supports dynamic data insertion, deletion, and updates, making it good for apps requiring real-time data modifications.

🔗 Milvus

2. Weaviate

Weaviate is an open-source, cloud-native vector database for efficient similarity searches across different data types.

Weaviate supports real-time data ingestion and has plugin\modules for specific cases.

🔗 Weaviate

3. Qdrant

Qdrant is a vector database built on the HNSW algorithm, providing fast cosine similarity search with high-dimensional data.
(COOL!)

Qdrant supports real-time data insertion and deletion, to cater to apps requiring continuous data updates.

🔗 Qdrant

4. Pinecone

Pinecone is a managed vector database service that offers real-time indexing and querying of high-dimensional vectors.

Pinecone handles dynamic data updates allowing for (efficient) similarity searches as new data is added.

🔗 Pinecone

5. FAISS

FAISS, developed by Facebook AI, is a library for efficient similarity search and clustering of dense vectors.

Really a library rather than a full-fledged database, FAISS supports various indexing methods and can be integrated into systems that require approximate similarity search with incremental data handling.

🔗 FAISS

Dbs Compared

Key Ideas of Each DB