In one line, a vector database is a specialized database designed to efficiently store and query vector embeddings.
Now if you don't know what is vector embeddings, then....⬇️
Now, we have pre-trained word embeddings to make AI applications. Models like Word2Vec, GloVe, and FastText have been trained on a large text corpus. These pre-trained embeddings save time and resources compared to training embeddings from scratch on a specific dataset 😁.
Problems with Traditional Databases 🤕
Traditional scalar databases are not optimized for handling vector data and struggle to scale to the massive size and dimensionality of vector embeddings. Hence, it is difficult to query the data.
Here comes the Vector Databases....😎
Vector databases address those issues by:
Storing vectors in an optimized format using techniques like product quantization
Performing approximate nearest neighbor searches using algorithms like locality-sensitive hashing (LSH) and hierarchical navigable small worlds (HNSW)
Providing data management features like filtering, aggregation, and complex queries
🌐 You can learn more about these techniques from this following website: https://www.pinecone.io/learn/vector-database/
Some benefits of vector databases are:
Operationalizing embedding models for production use
Enable fast and accurate similarity search
Ideal for Real-time AI applications
Integrating with data pipelines and analytics tools
Providing security features
Some examples of vector databases are: 👇
I hope this helps explain what a vector database is!😉 Let me know if you have any other questions.