Demystifying Vector database : The Future of Search and Recommendations

Hey everyone, welcome! Today, we're diving into the fascinating world of vector databases. If you're curious about how semantic search, recommendation systems, and even image recognition are evolving, stick around

What is a Vector base

So, what exactly are vector databases? Imagine traditional databases, which are like massive spreadsheets with rows and columns. Now, vector databases are a bit different. They're designed to handle high-dimensional vectors—think of these as complex mathematical representations of data.

Vectors are essentially lists of numbers that represent data. For example, in image recognition, a vector might include pixel values or specific features of the image. In a recommendation system, vectors could capture elements like tempo, genre, or even the lyrics of a song.In semantic search, vectors are the embeddings that has captured the contextual meaning of the text

Vector databases store these vectors and make it super efficient to search through them. This is especially useful for applications that require semantic search—searching based on the meaning and context rather than just keywords.

How Do Vector Databases Work

Let’s break down how these databases work. Traditional databases use row-based or column-based storage. But with vector databases, data is organized into vectors, and each vector is associated with an ID and metadata.

To search over the database, we create a vector embedding of the query and perform the search to find the similar vector embeddings in the database

To find similarities between vectors, we use techniques like Euclidean Distance, Manhattan Distance, and Cosine Similarity. For instance:

Cosine Similarity focuses on the direction of the vectors, ignoring their magnitude.

Euclidean Distance measures the straight-line distance between two points in high-dimensional space.

Manhattan distance measures distance by summing absolute coordinate differences.

Building a Vector Database

Now, let’s dive into how you can build your own vector database from scratch. Here’s a high-level overview of the steps involved:

1. Define Your Use Case :Determine what type of data you’ll be storing (text, images, audio, etc.) and what kind of queries you need to support (semantic search, recommendation, etc.).

2. Chunking Your Data :Divide your data into manageable chunks if necessary. This is especially important for text processing as breaking down large texts into smaller helps to capture better semantic meaning in the sentences

3. Generating Embeddings:Select a model to generate vector embeddings for your data. For text, consider models like Word2Vec, GloVe, or BERT. For images, CNNs (Convolutional Neural Networks) are useful. Convert your data into vector embeddings using the chosen model.

4. Set Up Your Database : Choose an appropriate indexing technique for efficient similarity search. Options include Hierarchical Navigable Small World (HNSW) graphs, Inverted File with Product Quantization (IVF-PQ), or ANN (Approximate Nearest Neighbors).Decide on how you want to store your vectors. You can use open-source databases like Milvus or FAISS, or a managed service like Pinecone, Qdrant

5. Implement the Search Algorithm :Implement search algorithms based on your needs. K-Nearest Neighbors (KNN) can be used for exact matches, while ANN can provide faster approximate results. Implement methods like Cosine Similarity or Euclidean Distance to measure how similar vectors are.

6. Test, Deploy and Monitor :Test the performance of your vector database. Ensure that it handles large datasets efficiently and returns relevant results quickly.Deploy the database in appropriate cloud based on bussiness requirements.

Vector Databases in Action

Recommendation Systems: Vector databases help in finding items similar to what you like by comparing vectors representing user preferences and item features.

Content-Based Image Retrieval: They enable searching for images with similar content by comparing their vector embeddings.

Semantic Search: Instead of just keyword matching, search engines can use vectors to understand the context and provide more relevant results.

Examples of Vector Databases :

There are a few standout vector databases worth mentioning(Refer to the uploaded notedbooks for implementation):

Pine cone :Pine cone is a cloud-based service that handles vector search and similarity search with high throughput and low latency.

Milvus :An open-source vector database designed for similarity search, capable of handling large-scale data efficiently.

Qdrant : Qdrant Cloud is a scalable, efficient vector database for search.

FAISS : Developed by Facebook, this library is optimized for high-speed similarity searches in large datasets.

Which Vector database to choose?

Feature	Pine cone	Milvus	Qdrant	FAISS
Deployment	Fully managed cloud service	Open-source, cloud and on-premise	Open-source, Fully managed cloud service	Open-source, on-premise, in memory
Ease of Use	Easy to use, minimal setup required	Moderate setup complexity	Easy to use, minimal setup required	Requires more configuration and tuning
Cost	Pay as you go	Open source with optional paid support	Pay as you go	Open source(costs for custom solutions)
Platform	Cloud	Cloud and on premise	Cloud and on premise	Cloud and on premise
Query types	Vector similarity, exact match	Vector similarity, approximate search	Vector similarity, hybrid search	Vector similarity, approximate search

And that's a wrap on our deep dive into vector databases! These technologies are shaping the future of search, recommendations, and data retrieval.

See you next time, and stay curious!